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BUGSPharmaSUG 2017 Papers - Index
205 papers. Source: PharmaSUG (Pharmaceutical Software Users Group) conference proceedings.
| # | Title× | Authors× | Abstract× | Keywords× | Extras | Pages |
|---|---|---|---|---|---|---|
| 1 | A Business Solution for Improved Drug Supply Chain Visibility | David Butler, Joy King, Ronald Chomiuk, Richard Neafus |
Global increases in supply chain regulations are creating additional burden on manufacturers, distributers and pharmacies. Drug Supply Chain Security Act (DSCSA) is the U.S.A version. It is vital for regulatory purposes to be able to see every product in the supply chain at a unit-of-sale level, but the data can be used for business gains as well. Predictive analytics provide this capability. Examples of opportunities include: what each customer is going to order next; which point in the supply chain is going to not meet SLA; where stock deficiencies or surpluses are going to occur and what can be done in advance to avoid these variations; Identify when a customer's needs are not going to be met and implement steps to attenuate concerns and losses before they happen; Know how to move any and all available products and groups of products to specific anticipated needs and groups of anticipated needs at lowest price | sample, pharmaceutical industry | source | 9 |
| 2 | A Case of assessing adverse events of interest based on their grade changes | Sriramu Kundoor | Adverse Events play an important role in assessing the safety of a drug. While shift tables are commonly used to describe laboratory data and determine shifts in toxicity grades, a similar approach can also be taken to determine the AE grade shifts in describing AE data. These shift tables provide an insight on whether a subject is recovering/resolving or recovered/resolved and the KM analysis provides further insight of the median duration for the improvement or resolution which in turn helps the safety assessment of the product. In order to do this, we have to filter the data for the adverse events of special interest and find the shift from chosen (Maximum) grade to the subsequent lowest grade. With suitable examples, this paper describes an approach to create AE shift tables and the KM estimate for the median duration of improvement or resolution | adverse events, ctcae grade, shift table, trial elements, km analysis | source | 7 |
| 3 | A Combined AE + CM Graph using SAS | Sanjay N. Matange | Patient profile graphs generally include visual display of the clinical data for one subject on a timeline. The graphs include display of the Adverse Events by severity, Concomitant Medications and other graphs such as Vital Statistics, Lab Results and other plots over time. This paper will demonstrate how to combine some of these graphs into one graph, where the Adverse Events and the Concomitant Medications can be displayed in the same graph, interspersed by the start date for each event or medication on a common time line. Such a graph places related information in close proximity thus improving the understanding of the data. This technique can also be extended to include more data in one graph | adverse events, macro variable, concomitant medications | source | 7 |
| 4 | A Critique of Implementing the Submission Data Tabulation Model (SDTM) for Drugs and Medical Devices | Carey G. Smoak | The Clinical Data Interchange Standards Consortium (CDISC) encompasses a variety of standards for medical research. Amongst the several standards developed by the CDISC organization are standards for data collection (Clinical Data Acquisition Standard Harmonization - CDASH), data submission (Study Data Tabulation Model - SDTM) and data analysis (Analysis Data Model - ADaM). Drug development was the original impetus for developing these standards. Therapeutic Area User Guides (TAUGs) have been a recent focus to provide advice, examples and explanations for collecting and submitting data for a specific disease. Non-subjects even have a way to collect data using the Associated Persons Implementation Guide (SDTMIG-AP). SDTM domains for medical device were published 2012. Interestingly, the use of Device domains in the TAUGs occurs in seventeen out of twenty-four of TAUGs providing examples of the use of the various Device domains | clinical trial, clinical research, vital signs, adverse events, cdisc | source | 16 |
| 5 | A custom ADaM domain for time to event analysis in adverse events | John Saida Shaik | One of the primary objectives of first-in-human oncology clinical trials is to evaluate the safety of a drug and assess its maximum tolerated dose (MTD). To evaluate safety and MTD, it is important to study all the adverse events that are occurring during the trial. In particular, adverse events with severity grade 3 and above and adverse events that are recurring need to be analyzed in detail. In addition, when an AE first occurred in a patient and how long it took to improve to a better grade and/or complete resolution are endpoints of i nterest. In this paper, I am proposing a custom ADaM domain to capture onset, improvement and resolution of adverse events information following the basic data structure (BDS) as described in CDISC ADaM Implementation guide (v1.0). This approach facilitate s in preparing Kaplan- Meier and time to event analysis TLFs in one PROC away method and reduces time and effort on validation |
adverse events, adamig, survival analysis, cdisc | source | 4 |
| 6 | A Guide To Programming Patient Narratives | Renuka Tammisetti, Karthika Bhavadas |
The ICH-E3 guidelines require patient narratives, which are targeted patient profiles of clinical importance. Patient narratives describe death, other serious adverse events, and certain other significant adverse events judged to be of special interest collected for a subject over the course of a clinical trial. The SAS programmer is expected to provide key data information to the medical writer. The Medical writer will review patient profiles to coincide with an event of interest and address the safety concerns of interest at the patient level. This paper will provide helpful insight on the traditional process of narrative generation; identify the requirements and gather information to program narratives | adverse events, medical history, clinical trial, adam dataset, sample, concomitant medications | source | 6 |
| 7 | A Macro to carry values through observations forwards or backwards over missing or 'null' values within a BY group or a SAS data set. | Timothy J. Harrington | This paper explains the structure of a macro and its usefulness for imputing missing or defined 'null' values with a prior or following quantity of a numeric or character column in a SAS data set or BY group. The macro uses DICTIONARY.COLUMNS to determine the data type, carries values over multiple successive observations with missing or 'null' amounts. When the specified data set column is numeric, parameters can be passed to return the differences between each quantity and the first or last values of the by group or the successive differences between non-missing values. The 'null' value defaults to missing but can be user specified. Included in this paper are examples of practical applications, such as imputing missing dates and times of events since the first or most recent dose in PK data | sample, missing value, by group processing, macro call | source | 9 |
| 8 | A novel method to track mapping of all CRF variables into SDTM datasets. | Aishhwaryapriya Elamathivadivambigai | A common approach to ensure mapping of all CRF variables into SDTM datasets is by manually comparing annotated CRF pages with mapping documents. However, this approach is highly time consuming, error prone and more tedious, especially in situations when new variables are added to CRFs as the study progresses. Hence, a user -friendly automated approach or a technique that could facilitate evaluating whether all CRF variables are successfully mapped to SDTM domains is highly desirable. So, in an attempt to prov ide a solution, this paper introduces a method that can automatically evaluate whether or not all CRF variables are properly mapped to SDTM datasets and also report a list of CRF variables that are yet to be mapped to the user. This approach accurately captures all the CRF variables from the CRF document and compares each variable with the SDTM program log files. The programming techniques used for this approach and a working example will be described in this paper | sdtm domain, macro variable, clinical trial, sample, proc step, do loop, cdisc | source | 7 |
| 9 | A Practical Guide to Healthcare Data: Tips, Traps, and Techniques | Gregory S. Barnes Nelson | Healthcare is weird. Healthcare data is even more so. The digitization of healthcare data that describes the patient experience is a modern phenomenon with most healthcare organizations still in their infancy. While the business of healthcare is already a century old, most organizations have focused their efforts on the financial aspects of healthcare and not on stakeholder experience or clinical outcomes. Think of the workflow that you may have experienced such as scheduling an appointment through doctor visits, obtaining lab tests, or prescriptions for interventions such as surgery or physical therapy. As you traverse the modern healthcare system, we are left with a digital footprint of administrative, process, quality, epidemiological, financial, clinical, and outcome measures that range in size, cleanliness and usefulness | clinical endpoint, unstructured data | source | 20 |
| 10 | A Precision-strike Approach to Submission Readiness: How to Prepare Your Filing Teams for Consistent Excellence | Michael Hagendoorn, Tony Chang |
Getting an electronic submission through the door at FDA used to be a Fantastic Journey of Discovery for individual programming and biostatistics teams within our company. We faced the following challenges: Lack of a consistent process for handling electronic filing deliverables across products Varying levels of filing experience between teams, causing painful re-learning for each filing Beneficial best practices and lessons are kept within each filing team , not shared with others Variable understanding and engagement by other functions of programming-related filing deliverables To address these issues, we established a small Submission Consultancy Group (SCG) consisting of 2- 3 experienced filing lead programmers | data standard, clinical programming | source | 7 |
| 11 | A Review of "Free" Massive Open Online Content (MOOC) for SAS Learners | Kirk Paul Lafler | Leading online providers are now offering SAS users with "free" access to content for learning how to use and program in SAS. This content is available to anyone in the form of massive open online content (or courses) (MOOC). Not only is all the content offered for "free", but it is designed with the distance learner in mind, empowering users to learn using a flexible and self- directed approach. As noted on Wikipedia.org, "A MOOC is an online course, content, or other resource aimed at unlimited participation and made available in an open access forum using the web." This presentation illustrates how anyone can access a wealth of learning technologies including comprehensive student notes, instructor lesson plans, hands-on exercises, PowerPoints, audio, webinars, and videos | sas base, sas studio, sample | source | 16 |
| 12 | A SASsy Study of eDiary Data | Amie Bissonett | Many sponsors are using electronic diaries (eDiaries) to allow subjects to enter study data themselves, such as daily events, concomitant medications taken, and symptoms that occur. Depending on the study, subjects may enter data at varying time increments, from weekly or monthly history up to a daily account of activities and events. The timeliness of the data entry as well as the cleanliness of the data make a big impact on deriving SDTM and ADaM data sets and how the analysis will be performed. This paper goes through different scenarios and gives some tips to help from data cleaning, setting up the variable derivations, and programming the analysis data sets | raw data, proc sql, sdtm domain, proc step, concomitant medications, cdisc | source | 5 |
| 13 | A Vivid and Efficient Way to Highlight Changes in SAS Dataset Comparison | Jeff Xia, Lugang Larry Xie, Shunbing Zhao |
How to find the updates/changes across periodic data extractions in an ongoing clinical trial is a very interesting topic. Many authors have papers discussing limitations and gray areas in dataset comparison using conventional methods. This paper presents a method to identify and highlight changes/updates in both the metadata and contents level of two corresponding datasets. Newly added variables or records, as well as the removed/updated ones are identified by a SAS macro utility, and highlighted and color coded in an EXCEL spreadsheet. It provides a vivid and efficient way for SAS programmers, data managers and statisticians to master every detail of the updates in datasets of different versions |
excel, proc step, proc compare, macro variable, character variable, sample | source | 12 |
| 14 | ADaM Compliance - Validating your Specifications | Trevor Mankus, Kent Letourneau |
As of December 17th, 2016, the FDA and PMDA require that all new studies included in submissions have their analysis data sets created in compliance with the CDISC ADaM standards. The possibility of having your submission not accepted heightens the importance of having an effective process for ensuring that you are following this standard. At PRA Health Sciences, this process starts with a review of the metadata in our ADaM specifications. This paper gives background on this topic and describes the process by which we create data sets that are fully compliant with the ADaM standards. We discuss that determining ADaM compliance cannot only be done with available validation software tools and that a human component is needed. We will also discuss the benefits of beginning this review on the ADaM specifications rather than on the data sets | data cleaning, macro parameter, data validation rules, adam dataset, adverse events, excel, cdisc | source | 9 |
| 15 | ADaM Grouping: Groups, Categories, and Criteria. Which Way Should I Go? | Jack Shostak | ADaM has variables that allow you to cluster, group, or categorize information for analysis purposes. Sometimes it may not be entirely clear to you which variable you should be using and when. The goal of this paper is to help to provide some guidance around what ADaM grouping variables are available, what is appropriate and when, and then to discuss when more than one technique will work for a given analysis situation. We will also look at problems where a single solution isn't entirely obvious. The paper focus will be primarily on Basic Data Structure (BDS) grouping variables, although other non-BDS variables will be mentioned. The following ADaM BDS variables will be examined: GRy(Gy), *CATy, CRITy, MCRITy, AVALCATy, and PARAM. These will be compared and contrasted. The paper will conclude with suggested ADaM categorization strategies as well as ideas for where ADaM can be improved in this regard | bds, adamig, stage 2, flag variable, class variable, sample, adam dataset, filtering, shift table, data science, cdisc | source | 12 |
| 16 | ADQRS: Basic Principles for Building Questionnaire, Rating and Scale Analysis Datasets | Nancy Brucken, Karin LaPann |
Questionnaires, ratings and scales (QRS) are frequently used as primary and secondary analysis endpoints in clinical trials. The Submission Data Standards (SDS) QRS sub-team has compiled a considerable library of SDTM supplements defining standards for the collection and storage of QRS data. The ADaM ADQRS sub-team has been formed to develop addenda to these supplements, which will define standards for corresponding analysis datasets. This paper represents the current thinking of the ADQRS sub-team regarding basic principles for building QRS analysis datasets | adam dataset, missing value, imputation, sample, cdisc | source | 6 |
| 17 | Adverse Event Data over Time | Kriss Harris | Commonly in clinical trials Adverse Events (AEs) are captured over time and the incidence and time to first occurrence of an event are presented descriptively. The duration of the event is also often calculated in the subset of patients who have experienced the event. However, better methods are needed to combine and present AE incidence and duration information over time. In oncology, methods for displaying tumor response and duration of response were developed by Temkin (Temkin, 1978)). This paper will investigate the applicability, with modification as necessary, of these methods, in particular the Probability of Being in Response method, to the display and meaningful interpretation of AE data over time in clinical trials. Recommendations for the presentation of AE data in future trials will be ma de | adverse events, clinical trial, verbatim term, trial elements, negative binomial regression, adam dataset, binomial distribution, poisson distribution, cdisc | source | 11 |
| 18 | Ahead of the Curve: Leading with Industry Data Requirements | Maria Dalton, Nancy Haeusser |
Most programmers in the pharmaceutical industry are aware of the requirement to submit data in CDISC data format. However, the CDISC mandate is not always clear-cut as there are some nuances and ambiguities to this requirement. Also, there are many other data requirements for the pharmaceutical industry, such as data disclosure regulations, data sharing policies, and dictionary requirements. In addition, clinical trials may now include real world data or electronic health care data which follow new data standards. It can be very challenging for companies to stay "ahead of the curve" in this area. This paper discusses some of the recent developments in industry data requirements, and it also discusses the importance of an organized, cross-functional approach to managing and embedding industry data requirements within a pharmaceutical or CRO company. This paper covers industry data standards and regulatory data requirements, so it is not specific to any programming language | clinical trial, data standard, pharmaceutical industry, clinical data, adverse events, controlled terminology, data cleaning, clinical research, cdisc | source | 17 |
| 19 | AIR Binder: An Automatic Reporting and Data Analysis SAS Application for Cytochrome P450 Inhibition Assay to Investigate DDI | Hao Sun, Kristen Cardinal, Carole Kirby, Richard Voorman |
We developed a SAS-based application, called AIR Binder (Automatic Inhibition Reporting Binder), which integrates a series of SAS macros to perform data analysis and visualization, and generate report- ready tables and figures for the cytochrome P450 (CYP) inhibition assay, a widely-used in vitro ADME assay that provides key supporting data for drug discovery and development. The dataset from this assay was designed to have only 3 columns (assay/sample identifier, concentration, and activity) to simplify data management/storage and QC process. AIR Binder has been successfully used to analyze this type of dataset, generate required statistics, make report-ready comprehensive tables, and plot high- quality grouped figures. The program calculates IC50 values using non-linear fitting algorithms based on several variations of a 4-parameter logistic (4PL) model | proc step, macro variable, logistic distribution, standard deviation, data science, logistic regression, call symput, txt, sample, proc report, proc nlin | source | 8 |
| 20 | An Efficient Solution to Efficacy ADaM Design and Implementation | Chengxin Li, Zhongwei Zhou |
In clinical trial data processing, the design and implementation of efficacy datasets are often challenging. The efficacy datasets here refer to the analysis data subject level (ADSL) and efficacy endpoints datasets at specific study level (e.g., ADEFF). Those two types of datasets are also recommended for FDA submission. To achieve optimal programming with efficient and reusable codes, this paper investigates some standardization methods for the design and implementation of ADSL and ADEFF datasets. For ADSL, based on rigid SDTM common domains, and ADSL components and functions, ADSL variables are further designated into categories of global, project, and study (GPS). The global variables (approximately 80% of all ADSL variables) in ADSL are specified, derived, and validated only once within a company; the project variables can be further managed within a therapeutic area or an indication; and study variables are handle d at specific study level | adam dataset, imputation, sdtm domain, data cleaning, cdisc | source | 7 |
| 21 | Analyzing data from multiple clinical trials using SAS Real World Evidence | Jay Paulson, David Olaleye |
This paper explains how SAS Real World Evidence solution makes use of data sets obtained from multiple clinical trial data sources to derive intelligence beyond the scope of a typical clinical trial. By using Prostate Cancer clinical trial data from Project Data Sphere as example, this paper explains the concept of Workspaces, Index Cohorts, Population Cohorts and Expressions. Then the paper explains how users can create Workspaces, Index Cohorts, Population Cohorts and Expressions within SAS Real World Evidence framework to perform analysis. The paper also explains how Inde x and Population cohorts will be used to perform analyses, derive insights and answer critical research questions | clinical trial, clinical data, proc freq, insurance claims | source | 13 |
| 22 | Analyzing Singly Imputed Utility Based Weighted Scores Using EQ-5D for Determining Patients' Quality of Life in Oncology Studies | Vamsi Krishna Medarametla, Liz Thomas, Gokul Vasist |
Quality of Life (QoL) analysis provides complementary information to standard safety and efficacy analysis in clinical trials. This analysis allows regulators to take patient -reported outcomes (PROs) on quality of life into account when making decisions about drug approvals and labeling. A standard technique in QoL analysis is to combine patient-reported outcome data collected on a questionnaire into a single utility score. Missingness in PRO data is common, and must be addressed and accounted for in analysis using an imputation method. The EQ-5D is a standardized instrument for use as a measure of health outcome. Analysis of the EQ-5D requires computation of a weighted utility score. Country -specific time trade-off (TTO) weights are commonly used. Calculating the index weighted scores of the EQ -5D profiles under several different single imputation schemes will help in understanding the health state of the patient | imputation, proc sort | source | 9 |
| 23 | Application of Deming Regression in Molecular Diagnostics using a SAS Macro | Merlin Njoya, Pari Hemyari |
In Molecular Diagnostics, method comparison studies are conducted to estimate possible systematic difference between measures from a new investigational assay and an approved assay. The U.S. Food and Drug Administration (FDA) recommends in such case the use of Deming regression analysis to establish that the new assay measures the target value as accurately as the gold standard method 1. While extensive literature is available on the estimation of the Deming Regression parameters usi ng SAS, there is limited information on how to construct confidence interval of these parameters when there is correlation among data due to repeated measurements. This paper will present a SAS macro that efficiently calculates the slope and intercept of the Deming regression and also performs the bootstrap2 method to estimate the confidence interval around these parameters | regression analysis, confidence interval, macro definition, sample, bootstrap, correlation, proc reg | source | 6 |
| 24 | Automated DSMB Presentation in SAS: Yeah, I'd Submit That! How to auto populate PowerPoint presentations so you don't have to | Brett Jepson, Kaitie Lawson |
Data & Safety Monitoring Board (DSMB) members are increasingly requesting high-level overview presentations to assess the current status of a study in lieu of lengthy binders. When multiple studies report to the same DSMB, a certain level of standardization across presentations is desirab le to facilitate ease in understanding and familiarity with the presentation format for the DSMB members. Manually entering text and data into a presentation is tedious and is subject to transcription errors. By utilizing ODS POWERPOINT, the ODSTEXT and ODSLIST procedures, and a series of other familiar SAS procedures, we have created a suite of SAS macros that automatically populate presentations , therefore eliminating the need to manually enter data. These SAS macros allow for presentations conforming to a general template to be created in a standardized manner which saves time, ensures data accuracy and integrity, and provides continuity in data presentation to DSMB members |
macro call, macro variable, protocol deviations, primary endpoint, macro definition, macro quoting function, proc step, proc sgplot, proc report, %if-%then-%else | source | 17 |
| 25 | Automated Generation of Clinical Study Reports using SAS and RTF (Literate programming) | Rajaram Venkatesan, Julien Sauser, Carlos Antonio De Castro |
Clinical study report (CSR) is the final milestone in any clinical trial. A CSR typically consists in Table of Contents (TOC), background of study, interpretation of results, tables, listings and figures (TLF). The process of populating TLFs, updating TLF numbers, cross-references and incorporating results in a CSR are laborious and potentially error prone. An 'automated' technique is presented in this paper which uses SAS and literate programming concept to generate a CSR document populated with specified TLFs, text and cross-references. CSR written using literate programming reduces significant amount of time post database lock (DBL). Literate programming efficiently combines the text and the statistical analysis in one single SAS source file which can be run immediately after unblinding the data to automatically produce the CSR in Microsoft (MS) Word and PDF format | clinical trial, sample | source | 13 |
| 26 | Automating Title and Footnote Extraction Using Visual Basic for Applications (VBA) and SAS | Tony Cardozo | When developing tables, figures and listings (TFLs) for clinical trial data we must ensure we are providing outputs that are consistent with the Statistical Analysis Plan (SAP) shells. Titles and footnotes, among other key elements, need a high level of accuracy to minimize review cycles with the statistician and overall development time. Depending on your current process, this may involve copying and pasting every title and footnote into a centralized macro or individual programs. This may be done by multiple programmers and can be a time-consuming process that leaves opportunities for human error. When the TFL shells are created in Microsoft Word, title and footnote extraction can be done programmatically utilizing Visual Basic for Applications (VBA) and SAS. In this paper, we'll discuss what VBA is, how to set it up and program title and footnote extraction | txt, output delivery system | source | 12 |
| 27 | Awareness from Electronic Data Submission to PMDA and FDA -- Lesson & Learnt from hands-on experiences -- | Yuichi Nakajima, Takashi Kitahara, Ryan Hara |
Pharmaceuticals and Medical Devices Agency (PMDA) has started accepting electronic study data (e- study data) submissions, beginning October 2016 with a 3.5 year transitional period. The requirement of PMDA is similar to that of U.S. Food and Drug Administration (FDA) e-study data submission requirements. However, there are some differences in the requirement between both health authorities. Because recent drug development should be done in a more globally integrated way and similar clinical data package should be used for submission to both PMDA and FDA, sponsors need to understand the differences precisely and consider efficient processes for a new drug application (NDA) to meet requirements of each health authority (HA). In November 2016, Novartis submitted e-study data of global study including Japanese subjects in Clinical Data Interchange Standards Consortium (CDISC) formats to PMDA and FDA simultaneously | data cleaning, adam dataset, clinical data, data wrangling, phase i, phase ii, clinical trial, cdisc | source | 12 |
| 28 | Basic SDTM House-Keeping | Emmy V Pahmer | When creating SDTM datasets, there are a few simple checks that can be performed to help spot potential problems. These checks will be presented here. They are generally for common programming errors, not for checking the validity of source values or cross-checking certain values against others. In other words, basic house-keeping | proc step, character variable, cdisc | source | 3 |
| 29 | Becoming a Successful Manager of SAS Programmers from an Ex-Programmer-s Perspective | Christine Young | Have you ever wondered how to take that next step from programmer to manager of programmers? Or even if you want to take that step? What are the challenges? What are the rewards? What are some techniques you can carry with you from your programming days to make you a more effective manager? Which do you need to scrap? Here is a look at all of these questions and answers from someone who has been there and successfully done that | confidence interval, do loop | source | 3 |
| 30 | Beyond - Just fix it!- Application of Root Cause Analysis Methodology in SAS Programming. | Nagadip Rao | "Just fix it, we have too much to do"- a common phrase heard in organizations and especially in SAS programming departments. It is relatively easy to fix programming issues every time it occurs rather than look into why it occurred in the first place. The problem with this approach is that, there is a good chance that a similar issue may occur and not be caught next time resulting in compromising validity of deliverables. This is analogous to treating the symptom, every time it occurs and not looking deeper into figuring out the actual cause. In order to prevent systemic programming issues occurring, it is essential to identify what, how and why an undesirable event occurred before taking steps to prevent it in the future. A systematic process of investigating and identifying root cause that can be controlled by programming/management team with an intention of preventing it in future, can be done through Root Cause Analysis (RCA) | root cause analysis, clinical trial, adverse events, trial elements, lookup, data cleaning, data cutoff, proc freq | source | 8 |
| 31 | Beyond IF THEN ELSE: Techniques for Conditional Execution of SAS Code | Joshua M. Horstman | Nearly every SAS program includes logic that causes certain code to be executed only when specific conditions are met. This is commonly done using the IF…THEN…ELSE syntax. In this paper, we will explore various ways to construct conditional SAS logic, including some that may provide advantages over the IF statement. Topics will include the SELECT statement, the IFC and IFN functions, the CHOOSE and WHICH families of functions, as well as some more esoteric methods. We'll also make sure we understand the difference between a regular IF and the %IF macro statement |
if-then-else, subsetting if, where statement, ifn, select, ifc, macro definition, sas programmer, %if-%then-%else, missing value, proc format, whichn, execution phase, macro parameter, when, proc step, macro facility | source | 10 |
| 32 | Bird-s eye view of the data, a graphical exploration! | Hrideep Antony | Have you ever wondered if there is a better way to know who those outlier subjects are on your summary graphs without going through individual listings or without going back to your database filters to recognize them? Have you wished for a more efficacious means of identifying the outliers in your database and evaluating an individual subject's contribution to the overall database without the repetitious task of comparing numerous pages of patient listing outputs vs. summary tables? If you did, continue reading, as this paper may provide you with an optimal solution that you are looking for! The number of listings and tables that are created for the purpose of understanding a subject's contribution or significance to an overall database could be reduced if we can "recognize" a subject or a group of subjects of interest on a broader summary picture | outlier, correlation | source | 9 |
| 33 | Building a Better Dashboard Using SAS Base Software | Kirk Paul Lafler, Joshua M. Horstman, Roger D. Muller |
Organizations around the world develop business intelligence dashboards, sometimes referred to as enterprise dashboards, to display the current status of "point-in-time" metrics and key performance indicators. Effectively designed dashboards extract real-time data from multiple sources for the purpose of highlighting important information, numbers, tables, statistics, metrics, performance scorecards and other essential content on a single screen. This presentation explores the basic rules for "good" dashboard design, the metrics frequently used in dashboards, and the use of best practice programming techniques in the design of highly interactive, filterable, and drill-down dashboards using Base-SAS software | proc step, bar chart, vertical bar chart, proc format, if-then-else, dataset, proc template, proc print, ods html, footnote statement, proc sgpanel, user-defined format, histogram | source | 17 |
| 34 | Building a Fast Track for CDISC: Practical Ways to Support Consistent, Fast and Efficient SDTM Delivery | Steven R. Kirby, Mario Widel, Richard Addy |
Standardized data are so useful that sponsors are now required to provide study data to the FDA (CBER and CDER) using the standards, formats, and terminologies specified in the Data Standards Catalog. In practice that means following CDISC SDTM for tabulations content. Good planning is required to make sure that SDTM data are ready as needed to support regulatory submission; better planning is needed to have SDTM data available as needed to support all internal and external data consumers. We will share some effective strategies that we have used to provide the study data as collected to data consumers in SDTM format from first patient first visit through database lock. Our presentation will focus on three key areas: Planning to collect what we will submit with CDASH and SDTM-friendly protocols; preparing to consistently implement SDTM with metadata standards; and designing robust, reusable mapping code that is validated early and used often | cdisc controlled terminology, sdtm programming, controlled terminology, clinical data, raw data, trial summary, sample, cdisc | source | 14 |
| 35 | Building Intelligent Macros: Using Metadata Functions with the SAS Macro Language | Arthur L. Carpenter | The SAS macro language gives us the power to create tools that to a large extent can think for themselves. How often have you used a macro that required your input and you thought to yourself "Why do I need to provide this information when SAS already knows it?" SAS may well already know what you are being asked to provide, but how do we direct our macro programs to self-discern the information that they need? Fortunately there are a number of functions and other tools within SAS that can intelligently provide our programs with the ability to find and utilize the information that they require. If you provide a variable name, SAS should know its type and length; given a data set name, the list of variables should be known; given a library or libref, the full list of data sets that it contains should be known. In each of these situations there are functions that can be utilized by the macro language to determine and return these types of information | macro variable, macro facility, macro function, macro loop, do loop, dataset, sas log, proc step, proc contents, where statement, macro put, %if-%then-%else | source | 17 |
| 36 | CDISC Compliant NCA PK Parameter Analysis When Using Phoenix WinNonlin | Renfang Hwang | Mapping the Non-Compartmental Analysis (NCA) PK parameter defaulted names when using Phoenix WinNonlin to CDISC compliant PK parameter names needs to involve the pharmacokineticist to avoid confusion and incorrect report on PK parameter analysis. In meeting the compliance with CDISC data standards and obtaining PK parameters submission ready domains, PP and ADPP, Pharmacokinetics group at Celgene has developed a process to ensure that NCA (Non-Compartment Analysis) PK parameter analysis using WinNonlin, the NCA defaulted PK parameter names are accurately mapped and compliant to the PK parameter codelist and controlled terminology defined by CDISC. PK scientists use their Pharmacokinetics expertise along with the knowledge of CDISC data standards, the PK scientists have carefully mapped the entire mapping worksheet between NCA defaulted PK parameter names and CDISC compliant PK parameter codes | pk parameter, controlled terminology, excel, cdisc | source | 11 |
| 37 | Challenge for Dramatic Streamlining of TLFs Creation Process with SAS and Java | Yohei Takanami, Fumihiro Yamasaki |
It requires a large amount of time and effort to create tables, listings and figures (TLFs) and conduct QC/Verification of them in clinical trials. In this paper, we introduce a GUI-based system with SAS and Java that enables users to generate over 90% of TLFs in typical clinical trials. The system has been successfully implemented in the actual TLFs creation process in Takeda in Japan, which has resulted in the reduction of workload, time and cost with keeping high quality. Key factors and considerations for streamlining TLFs creation process are also discussed | macro definition, data science, adam dataset, clinical trial, macro parameter, sas programming, cdisc | source | 8 |
| 38 | Charting Your Path to Using the "New" SAS ODS and SG Graphics Successfully - Interactively Generate the Code | Roger D. Muller | SAS Output Delivery System (ODS) Graphics started appearing in SAS 9.2. Collectively these new tools were referred to as "ODS Graphics," "SG Graphics" and "Statistical Graphics". When first starting to use these tools, the traditional SAS/GRAPH software user might come upon some very significant challenges in learning the new way to do things. This is further complicated by the lack of simple demonstrations of capabilities. Most graphs in training materials and publications are rather complicated graphs that, while useful, are not good teaching examples for starting purposes. This paper contains many examples of very simple ways to get very simple things accomplished. Many different graphs are developed using only a few lines of code each, using data from the SASHELP data sets. The use of the SGPLOT, SGPANEL, and SGSCATTER procedures are shown |
proc step, proc template, sas studio, proc sgplot, sas enterprise guide, output delivery system, proc sgpanel, sample | source | 16 |
| 39 | Check Please: An Automated Approach to Log Checking | Richann J. Watson | In the pharmaceutical industry, we find ourselves having to re-run our programs repeatedly for each deliverable. These programs can be run individually in an interactive SAS session, which enables us to review the logs as we execute the programs. We could run the individual programs in batch and open each individual log to review for unwanted log messages, such as ERROR, WARNING, uninitialized, have been converted to, and so on. Both of these approaches are fine if there are only a handful of programs to execute. But what do you do if you have hundreds of programs that need to be re- run? Do you want to open every single one of the programs and search for unwanted messages? This manual approach could take hours and is prone to accidental oversight. This paper discusses a macro that searches a specified directory and checks either all the logs in the directory, only logs with a specific naming convention, or only the files listed | where statement, sample, macro variable | source | 12 |
| 40 | Check Your Data: Tools for Automating Data Assessment | Paul Stutzman | This paper presents tools that can be employed to check the consistency and validity of data, and it discusses building blocks for automating these tasks. Individually or in combination, these components can be used to check data within data sets, across data sets, and over time. It is important to carefully examine data to ensure consistency and accuracy. This can be a time- consuming process, so automating these tasks can be of great value. These data checking tasks range from simple (e.g. making sure related variable pairs like --TEST/--TESTCD are consistent within a data set) to more complex (e.g. seeing if VISIT/VISITNUM pairs are consistent across all data sets, or looking at how the structure of a set of data sets and the information they contain change over time). Regardless of the complexity, there are some fundamental building blocks that can help automate these processes | proc step, proc freq, proc report, proc sql, dictionary tables, numeric variable, call execute, excel, macro variable, nominal variable, call symput, proc contents, xls, proc means, sample, sdtm domain, output, sas log, data profiling, ods output, cdisc | source | 12 |
| 41 | Clarifications About ADaM Implementation Provided in ADaMIG Version 1.1 | John K. Troxell | Since the publication of version 1.0 of the CDISC (Clinical Data Interchange Standards Consortium) ADaM (Analysis Data Model) Implementation Guide (ADaMIG) in December 2009, experience has shown that there are some misunderstandings among practitioners about ADaM implementation. The ADaM Team realized that the best way to address these misunderstandings was to clarify the ADaMIG. In fact, although additional variables were added in version 1.1, published in 2016, the primary motivation was to clarify version 1.0. This paper describes important clarifications contained in version 1.1, and what they mean for implementers of both ADaMIG versions | adam dataset, clinical data, cdisc | source | 5 |
| 42 | Clinical and Vendor Database Harmony; Can-t we all just get along? | Brian Armstrong, Renee Kerwin |
Clinical trial data collection is often comprised of multiple databases, in a hub and spoke network with the clinical database as the hub and external vendor databases as spokes (e.g. laboratory, electronic diary, electrocardiogram, magnetic resonance imaging, pharmacokinetic, etc.). The submission database is presented as clean package of datasets with associated define documentation. However, behind the scenes, the creation of that neat and tidy submission package is often complicated by data collection inconsistencies and data reconciliation issues that cause inefficiencies to programmers. Spending time at the front end of a clinical trial to ensure consistent data values among various databases and constructing checks to reconcile common data points will promote quality data and more efficient programming | clinical database, sample, data reconciliation, raw data, data governance, clinical trial, clinical programming, database lock | source | 9 |
| 43 | Clinical Data Visualization using TIBCO Spotfire and SAS | Ajay Gupta | In Pharmaceuticals/CRO industries, you may receive requests from stakeholders for real-time access to clinical data to explore the data interactively and to gain a deeper understanding. TIBCO Spotfire 7.6 is an analytics and business intelligence platform, which enables data visualization in an interactive mode. Users can further integrate TIBCO Spotfire with SAS (used for data programming) and create visualizations with powerful functionality e.g. data filters, data flags. These visualizations can help the user to self-review the data in multiple ways and will save a significant amount of time. This paper will demonstrate some basic visualizations created using TIBCO Spotfire and SAS using raw and SDTM datasets. This paper will also discuss the possibility of creating quick visualizations to review third party vendor (TPV) data in formats like EXCEL and Comma Separated File (CSV) |
bar chart, horizontal bar chart, box plot, adverse events, order by, vital signs, dataset, data visualization, laboratory data, excel, cdisc | source | 12 |
| 44 | Clinical Trials Data: It-s a Scary World Out There! or Code that Helps You Sleep at Night | Scott Horton | Clinical trials data can be tough to manipulate and summarize. Sometimes the problems that data can cause are not obvious when SAS code runs clean and perusal of large data sets does not reveal any anomalies. Text truncation, Merging data, Coded data, Unexpected dates, Duplicate data, Data set naming, Macro parameters, Select statement, Array statement will be covered. Most of these tricks are a few lines of code. Very little effort that continues to pay dividends without too much up front cost. All of the tricks are straightforward as well; even beginning programmers can implement them | adverse events, select, clinical data, clinical trial, macro parameter, if-then-else, missing value, put statement, character functions, date variable, macro call, %macro parameters | source | 10 |
| 45 | ClinicalTrials.gov Results: an End of Study Deliverable That Should Be Considered at Study Startup | Maya Barton, Elizabeth Paynter |
Results submission to ClinicalTrials.gov is a deliverable required by law for most clinical trials. The deadline occurs one year from reaching the primary endpoint Last Patient Last Visit (LPLV) or the study LPLV. If the primary endpoint LPLV occurs prior to the study LPLV, this would lead to two submissions to ClinicalTrials.gov. Since this milestone will be different for different studies, the optimum time point to start working on this deliverable needs to be considered. Additionally, the submission typically pulls in data from multiple resources which are populated at different time points during the study lifecycle. We propose using early planning and standardization at study startup so that with the press of a button results will be generated for ClinicalTrials.gov for review and upload to the website. With this in mind we created a template program using SAS Enterprise Guide 7.1 that will enable the study team to map the data from multiple sources into one | clinical trial, adverse events, primary endpoint, ods xml, sample, database lock, treatment arm | source | 9 |
| 46 | Combining Survival Analysis Results after Multiple Imputation of Censored Event Times | Jonathan L Moscovici, Bohdana Ratitch |
Multiple Imputation (MI) is an effective and increasingly popular solution in the handling of missing covariate data as well as missing continuous and categorical outcomes in clinical studies. However , in many therapeutic areas, interest has also risen in multiple imputation of censored time-to-event data, because in many cases the Censored at Random (CAR) assumption is not clinically plausible for all subjects and MI allows for Censored Not at Random (CNAR) assumptions. In SAS, MI is possible through the MI procedure, procedures implementing Bayesian analysis (e.g., MCMC, PHREG) or user - implemented approximate Bayesian bootstrap. In MI, the missing values are filled in and several imputed datasets are created with differing values swapped for the missing ones. Each of those imputed datasets are analyzed separately using the methods that compute the statistics of interest (Kaplan -Meier survival estimates, hazard ratios, etc.) |
imputation, multiple imputation, hazard ratio, proc calis, proc phreg, proc step, survival analysis, regression analysis, confidence interval, proc mianalyze, follow-up period, km analysis, proc freq, clinical trial | source | 11 |
| 47 | Common Mistakes by Programmers & Remedies | Sairam Veeramalla | A SAS programmer is a critical entity of any company. It is of our knowledge that a programmer is responsible for providing support for statisticians and statistical programming expertise for the company. Simultaneously, programmers are also occupied with varied tasks, for instance, writing programs to create SDTM, ADaM and TLFs, weekly and monthly meetings, one to ones, completing time sheets and meeting the tight timelines. In the midst of this busy schedule, it is usual for programmers to overlook few basic concepts and perform mistakes. I would now share similar mistakes which I have observed during my experience working as a programmer, validator and as reviewer and would also like to provide easy solutions to rectify those mistakes. By doing so, we can ultimately deliver good quality deliverables and it also increases First Time Right (FTR) numbers which helps boost the performance of both the company and the individual as well | adverse events, data cleaning | source | 9 |
| 48 | Common Programming Errors in CDISC data | Sergiy Sirichenko | Data in standardized format is now a required part of regulatory submissions. CDISC standards have now achieved widespread adoption and have become a commodity skillset for thousands of clinical programmers. Nevertheless, there are still mapping and programming errors commonly observed in standardized data. These reduce the overall quality of submissions and should be avoided. Especially since majority of programming errors can be fixed even after the study data is locked, which is not the case with data management and data collection design issues. This presentation will share our experience of the most common mapping and programming errors observed across hundreds of regulatory submissions. We will provide examples and recommendations on how to detect these issues, how to evaluate their impact on regulatory review, and how each can be corrected | data cleaning, missing value, trial elements, regulatory submission, controlled terminology, data validation rules, adverse events, sas programmer, imputation, cdisc | source | 9 |
| 49 | Considerations and Conventions in the Submission of the SDTMIG Tumor and Response Domains | Jerry Salyers, Fred Wood |
The SDTMIG tumor domains (TU, TR, and RS) were first published in conjunction with the release of SDTMIG v3.1.3. The original intended use of these domains was largely confined to oncology. The TU domain contains tumor identification data and the TR domain contains subsequent tumor measurements over the course of a clinical trial. The measurements of target lesions and the continued evaluation of non-target lesions are used to support response assessment criteria such as outlined in RECIST (Response Evaluation Criteria in Solid Tumors) and represented in the RS domain. With the development of Therapeutic Area User Guides (TAUGs), most notably the CV TAUG, the use of the TU and TR domains has been broadened to allow for the identification and assessment of other types of "lesions", not just those associated with cancer and its treatment. This paper will examine how these two domains are now used across the spectrum of clinical studies and indications | clinical trial, controlled terminology, cdisc | source | 7 |
| 50 | Considerations in Submitting Standardized Electronic Data Under the Animal Rule: The Use of Domains in the SDTMIG and the SENDIG | Fred Wood | The Animal Rule (AR) in the Code of Federal Regulations (CFR) provides a mechanism for the approval of drugs (1) and biological products (2) without requiring human clinical trials for efficacy when to do so is not ethical or feasible. Instead, the AR allows efficacy studies to be conducted in animals, with certain requirements. Examples of products covered under the AR are vaccines against diseases such as smallpox and anthrax. Product safety testing in humans is still needed, but the requirements for this are out of scope for this paper. In conjunction with the Critical Path Institute (C-Path), CDISC is currently engaged in an effort to develop an implementation guide for AR studies. The FDA's Counter-Terrorism and Emergency Coordination Staff (CTECS; within CDER) has provided their data requirements | clinical trial, trial summary, cdisc | source | 7 |
| 51 | Controlled Terminology Without Excel | Mike Molter | The world of clinical data standards brought with it plenty of rules about metadata. This domain must have these variables, those variable values must be no longer than 8 characters, these other variables are restricted to certain values, etc. What standards came up short on was how/where these rules could be stored and maintained, as well as any processes for implementing them. Nowhere is this mo re evident than controlled terminology. Standard metadata such as NCI controlled terminology has been made available through EXCEL, and the rules for implementing these standards are stored in PDF documents. Much of the industry has defaulted to EXCEL for the collection and maintenance of study metadata |
excel, controlled terminology, relational database, dataset, sas programmer, set statement, sas base, proc sql, macro definition, cdisc | source | 15 |
| 52 | Conversion of CDISC specifications to CDISC data - specifications driven SAS programming for CDISC data mapping | Yurong Dai, Jameson Cai |
We'd like to introduce metadata driven approach utilizing sas programming techniques for SDTM and ADaM data mapping. Metadata extracted from specifications are converted into dataset's attributes, format, variable names and their order and sorting order for specification implementation in our reference code. It increases code's reusability, efficiency and consistency between data specifications and output data, and reduced re-work after data specification's update, during code development for SDTM mapping and ADaM datasets derivation | proc format, macro variable, sas programming, proc step, cdisc | source | 9 |
| 53 | Converting Non-Imputed Dates for SDTM Data Sets With PROC FCMP | Noory Y. Kim | SDTM (Study Data Tabulation Model) data sets are required to store date values with ISO8601 formats, which accommodate both complete dates (e.g. YYYY-MM-DD) and partial dates (e.g. YYYY-MM). On the other hand, raw data sets may come with non-ISO8601 date formats (e.g. DDMMMYYYY). Converting complete date values to an ISO8601 format can be as simple as applying a SAS date format to a numeric version of the date value. Conversion of partial date values is trickier. How may we convert, say, "UNMAY2017" and "UNUNK2017" into "2017-05" and "2017" respectively? This paper provides an example of how to do this using the SAS Function Compiler procedure (PROC FCMP). This paper also discusses methods of how to avoid the output of nonexistent dates such as "2017-01-99" |
date variable, proc step, proc fcmp, proc print, imputation, character variable, sas function, cdisc | source | 12 |
| 54 | Creating a Winning Resume Reflecting Your Skills and Experience With Honesty and Integrity. | Kelly Spak | A great resume is important to securing the next step in your career. This presentation will identify the dos and don'ts of your resume submission which will help set you above the competition. Topics will include personal data, education, certifications, present and previous employment (including ways to list out employer/contractor roles), select accomplishments, social media and references | source | 3 | |
| 55 | Creating Define-XML version 2 including Analysis Results Metadata with the SAS Clinical Standards Toolkit | Lex Jansen | Analysis Results Metadata provide traceability for a given anal ysis result to the specific Analysis Dataset Model (ADaM) data that were used as input to generating the analysis result; they also provide information about the analysis method used and the reason the analysis was performed. Analysis Results Metadata will assist the reviewer by identifying the critical analyses, providing links between results, documentation, and datasets, and documenting the analyses performed. This paper describes the Analysis Results Metadata extension 1.0 for Define-XML version 2, and also, how the SAS Clinical Standards Toolkit 1.7.1 can be used to create Define-XML version 2.0 documents including Analysis Results Metadata. This paper assumes that the reader is familiar with some basic XML concepts, and also with Define-XML version 2 | clinical trial, where statement, dataset, csr, adam dataset, data science, sample, data cleaning, csv, reference data, format statement, cdisc | source | 31 |
| 56 | Data Change Report | Eric Kammer | This paper shows a data change concept for comparing data between data milestones for clinical studies that was developed using SAS. SAS allows the data changes to be colored in GREEN in an output EXCEL file indicating what data has changed which can be new, deleted, or changed records. While this program was developed for a Unix system it is applicable to any environment since it uses a metadata concept. Data can then be compared to see if any significant changes occurred between milestones that could affect safety or any other important decisions applicable with analysis, monitoring, or clinical assessment |
proc compare, key, proc step, excel | source | 5 |
| 57 | Data Integrity: One step before SDTM | Pavan Kathula, Sonal Torawane |
In clinical research, errors occur despite careful study design, conduct, and implementation of error - prevention strategies. Data cleaning intends to identify and correct these errors or at least to minimize their impact on study results. Little guidance is currently available in the peer-reviewed process on how to set up and carry out cleaning efforts in an efficient and ethical way. With the growing importance of Good Clinical Practice guidelines and regulations, data cleaning and other aspects of data ha ndling will emerge from being mainly subjects to being the focus of comparative methodological studies and process evaluations. We would like to present an overall summary of the scattered information, integrated into a conceptual framework aimed at assisting investigators with planning and implementation. Our paper will explain the suggestions on using unique specifications, processes and specific methods along with SAS to maintain data integrity and cleanliness | adverse events, sdtm programming, missing value, clinical trial, concomitant medications, data review, independent qc, data validation rules, excel, data cleaning, cdisc | source | 9 |
| 58 | Data Monitoring Committee Report Programming: Considering a Risk-Based Approach to Quality Control | Amber Randall, Bill Coar |
Quality control (QC) is fundamental to ensuring both correct results and sound interpretation of clinical trial data. Most QC procedures are a function of regulatory requirements, industry standards, and corporate philosophies. However, no one should underestimate the importance of independent, thoughtful consideration of relevance and impact at each step in the process from data collection through analysis. Good QC goes far beyond just reviewing individual results and should also consider monitoring data throughout the course of a study. In particular, QC is essential when supporting a Data Monitoring Committee (DMC). Given the nature of interim and incomplete data, inherent challenges exist when it comes to generation of DMC reports. Many of the usual practices associated wit h quality control need to be adapted to accommodate the repetitive nature of DMC review on accumulating data tha t may have outstanding queries | data cleaning, adam dataset, data review, quality control, adverse events, database lock, clinical trial, proc compare, independent qc, sas programming, likelihood function, proc step, randomization, cdisc | source | 9 |
| 59 | Data Quality Control: Using High Performance Binning to Prevent Information Loss | Deanna Naomi Schreiber-Gregory, Lakshmi Nirmala Bavirisetty, Kaushal Chaudhary |
It is a well-known fact that the structure of real-world data is rarely complete and straight- forward. Keeping this in mind, we must also note that the quality, assumptions, and base state of the data we are working with has a very strong influence on the selection and structure of the statistical model chosen for analysis and/or data maintenance. If the structure and assumptions of the raw data are altered too much, then the integrity of the results as a whole are grossly compromised. The purpose of this paper is to provide programmers with a simple technique which will allow the aggregation of data without losing information. This technique will also check for the quality of binned categories in order to improve the performance of statistical modeling techniques. The SAS high performance analytics procedure, HPBIN, gives us a basic idea of syntax as well as various methods, tips, and details on how to bin variables into comprehensible categories | proc step, interval variable | source | 9 |
| 60 | Data Transparency, de-identification strategies, and platforms available for sharing data | Arun Raj Vidhyadharan, Sunil Mohan Jairath |
The idea of data transparency became a reality year 2013 onwards. Pharmaceutical companies were started to share their own company trial data, and enables that data to be combined with data from other pharmaceutical companies. Many Regulatory auth orities now require clinical trial sponsors to make clinical trial data available. Data sharing from clinical trials can lead to better understand and faster development and approval of medicines for rare diseases. One of such initiatives led to launch of platform to share clinical trial data related to cancer studies and the platform is called as PDS (Project Data Sphere). Other platforms available are Yale Open Data Access (YODA) and ClinicalStudyDataRequest.com ( CDSR) . Data transparency in Pharmaceut ical industry requires data de- identification to protect patient confidentiality and comply with legal governing laws. In order to put data for research and other purpose we need to first de-identify the clinical data | clinical trial, clinical data | source | 10 |
| 61 | Dealing with alignment of statistics: Beyond the decimal point. | Eric Pedraza | When displaying summarizations for continuous variables, we need to take several factors into account, such as dynamically assessing the required formats to obtain the desired alignment, values comprised of two statistics, a text to display in case the statistic cannot be computed, what is going to be aligned and in relation to what (i.e. a comma in a value comprised of two statistics aligned to the decimal point of single- statistic values), etc. These are time consuming tasks that can be simplified by using a macro to take care of the issues mentioned above, and taking very little time from the programmer to parametrize. This allows customizing the look and alignment of each separate statistic independently, thus it can be used for different studies with different display requirements | proc step, sample, macro call, interval variable, macro definition | source | 11 |
| 62 | Dear Dave, Please See the .LST File for Our Validation Differences. Thanks, Bad Validation Programmer | Tracy Sherman, David W. Carr, Brian P. Fairfield-Carter |
Good validation programmers are a rare commodity: too often validation programmers view themselves as filling a subordinate role, one that requires little more than basic understanding of SAS syntax, and that does not require imagination or an ability or willingness to question or critically eval uate or investigate. Validation is often reduced to a rubber-stamp corroboration of specifications, and a reverse-engineering of production output; investigating and resolving discrepancies is too often viewed as the exclusive domain of the production programmer. This paper will give examples of the good, the bad and the ugly among validation practices in statistical programming | data cleaning, proc compare, proc step, raw data, adam dataset, response variable, filtering, proc freq, cdisc | source | 9 |
| 63 | Defensive Programming - Tips and Techniques for Producing that Dataset, Table, Listing or Figure First Time | David W. Franklin | If you have ever bought one of those 'put together furniture kits', it is not usually an easy process. There is the setting up of the work area, taking out of the box, reading the instructions, getting the tools needed that were not included, construction, and finally look it over and check what you have done and that it is going to do as intended. A similar process is used for writing programs - we should first do some planning, construct the program, and check it over to see that it producing what was asked. This paper takes a brief and lighthearted look at each of these stages, providing a few tips for avoiding some of the many pitfalls and gives a few pieces of SAS code that are useful in the development of your program, hopefully avoiding having to say that dreaded phrase, "Oh Clanger" | adverse events, proc step | source | 7 |
| 64 | Deriving Rows in CDISC ADaM BDS Datasets | Sandra Minjoe | The ADaM Basic Data Structure (BDS) can be used for many analysis needs, including complex analyses. The SAS DATA STEP is a very flexible and powerful tool for data processing. Combined, the DATA STEP is very useful in the creation of a non-trivial BDS dataset. This paper walks through a series of examples showing use of the SAS DATA STEP when deriving rows in BDS. These examples include creating new parameters, new timepoints, and changes from multiple baselines |
output, adam dataset, controlled terminology, study day, adlb, cdisc | source | 12 |
| 65 | Developing ADaM Dataset for Cardiovascular Outcome Studies | Joanne C. Zhou, Rakesh Kumar, David L. Wade, David C. Chen |
CDISC released Therapeutic Area Data Standards User Guide for Cardiovascular Studies (TAUG-CV) Version 1.0 (Provisional) at July 2014. The TAUG-CV describes the most common data needed for ACS or reporting cardiovascular endpoints, and how to store these data in the SDTM datasets. This paper presents a method to create the ADaM dataset following the Basic Data Structure (BDS) for the clinical events reporting following the TAUG-CV | adam dataset, bds, cdisc | source | 4 |
| 66 | Developing Your Data Strategy | Gregory S. Barnes Nelson | The ever-growing volume of data challenges us to keep pace in ensuring that we use it to its full advantage. Unfortunately, often our response to new data sources, data types and applications is somewhat reactionary. There exists a misperception that organizations have precious little time to consider a purposeful strategy without disrupting business continuity. Strategy is a phrase that is often misused and ill-defined. However, it is nothing more than a set of integrated choices that help position an initiative for future success. With that in mind, this presentation will cover the key elements defining data strategy. Key topics include: What data should we keep or toss? How should we structure data? (warehouse vs. data lake vs. real-time/event stream) * How do we store data? (cloud, virtualization, federation, cloud, Hadoop), the approach we use to integrate and cleanse (ETL vs | data lake, structured data | source | 15 |
| 67 | Documenting Traceability for the FDA: Clarifying the Legacy Data Conversion Plan & Introducing the Study Data Traceability Guide | David C. Izard, Kristin Kelly, Jane Lozano |
Traceability from data collection through to the presentation of analysis results has always been a concern of the US Food & Drug Administration (FDA). The introduction of electronic data as part of submission added additional steps to confirm provenance of information. Now the requirement to provide clinical and non-clinical data based on a set of FDA endorsed data standards adds exponentially to the challenge, especially if legacy format data structures were utilized when the study was originally executed and reported but data meeting FDA requirements must be present in your submission. The PhUSE organization, made up of volunteers across industry, has worked closely with the FDA to develop tools to support the organization, presentation and interpretation of clinical and non-clinical data to regulatory bodies. Examples include the Study & Analysis Data Reviewer's Guides and the Study Data Standardization Plan | data science, adam dataset, cdisc | source | 9 |
| 68 | Don-t Get Lost! A High-efficiency, Low-tech Solution for Navigating Your Department-s Many-Many-Many SOPs and Guidance Documents | Michael Hagendoorn, Tim Yerington |
In the biotech and pharmaceutical industry, daily activities of biostatisticians and statistical programmers ("we" for short in this paper) are governed by countless processes described in an impressively high stack of documents. From study protocol creation and statistical analysis plan design, through collecting, reviewing, and analyzing patient data, to ultimately producing the clinical study report (CSR), we must continually abide by corporate and functional policies, Standard Operating Procedures (SOPs), how -to manuals, guides, templates, checklists, best practices, and memos in order to remain compliant with regulatory guidance and produce high-quality deliverables in an efficient and timely manner. Keeping tabs on precisely which of these hundreds of documents to reference in order t o perform any task at any time is a daunting proposition by itself | database lock, excel | source | 11 |
| 69 | Effective Ways to Perfect the Visualization of Clinical Trial Results | Amos Shu | A picture is worth a thousand words, which is why graphs are widely used for communicating clinical trial results. Well-designed graphs not only bring clarity to statistical results, they also add elegance to the report. How to make a perfect graph, however, is both technically and aesthetically challenging. This paper illustrates three different ways to present perfect graphs in the oncology area. The first example shows that color adjustment helps bring the audience's attention to subjects of interest. The second example illustrates how to clearly present multiple categories in a graph by using legends and text boxes. The third example demonstrates adding more information into a graphical display without compromising its clarity and beauty | data visualization, clinical trial | source | 6 |
| 70 | Everyone can use a little Currency - when dependent data set updates silently make your analysis data set out of date | Scott Worrell | Nearly every analysis data set has dependencies on multiple other data sets. The dependent data sets may be raw data sets or other analysis data sets. At times a dependent data set is updated, but the person in charge of updating the analysis data set is not notified of the modification to the dependent data set. This makes the analysis data set with the data dependency invalid. In turn, this condition produces data integrity issues in every Table, Listing or Figure (TLFs) that uses the analysi s data set. Before running TLFs, it is a good idea to verify that all dependent data sets are older or the same age as the analysis data sets. The Data Currency Utility detects data set currency issues and reports them so that they can be corrected through an update run of the analysis data set creation program. This presentation is appropriate for persons with all levels of SAS experience, but assumes basic understanding of SAS Dictionary Tables | macro variable, macro call, adam dataset, proc sql, data cleaning | source | 8 |
| 71 | Excel-VBA Tool to Auto-Create Validation log and Review Form using List of TLF-s. | Balaji Ayyappan | When a clinical study gets started we start with the List of Reports (Tables, Listings, Figures and Appendices) to be created for interim analysis (like BDR's, DMC, SRT deliveries) and Final CSR delivery. Creating and maintaining the Validation Log and Re view Form documents are vital and needed for the submission process. We created a tool to auto create the Validation Log and Client Review Form. Validation Log File is created with pre- defined columns for the corresponding TLF's - where programmers/statisticians need to enter their work status, comments, validation comments, initials and dates during work progress. In Review Form, client fills their comments, issues/solutions discussed during review meeting with initials and dates. This tool helps to create these documents in time efficient way and avoid manual error. This tool is developed using EXCEL-VBA technology |
data cleaning, excel | source | 6 |
| 72 | Expansion of Opportunities in Programming: DS2 Features and Examples of Usage Object Oriented Programming in SAS | Serhii Voievutkyi | DS2 is a SAS programming language that allows us process data in a way that we could not do before in the traditional DATA STEP. PROC DS2 is a powerful instrument for advanced problem solving and data manipulation. It also includes additional data types, ANSI SQL types, programming structure elements, threaded processing and user-defined methods. DS2 language is based on object-oriented concepts, which allows programmer to use packages and methods. This presentation will demonstrate how to modify the traditional DATA STEP with using DS2 to resolve different issues and how object-oriented programming can be used in statistical programming |
proc sql, proc step, set statement, sas base, by group processing | source | 11 |
| 73 | Facebook Data Analysis with SAS Visual Analytics | Piyush K. Singh, Ghiyasuddin Mohammed Faraz Khan, Vikrant Bisht, Prasoon Sangwan |
Nowadays, social media data is playing a key role in understanding market trends and user sentiment. Organizations are interested more than ever to know user choices, their needs, feedback and social media like Facebook is very common platform to gather this type of data. This paper demonstrates how SAS Visual Analytics can be used to analyze Facebook Data and create new reports based on this data which helps the management to understand the market and users' preference. It also demonstrates the Word Cloud analysis on Facebook data using SAS Visual Analytics. This paper contains some techniques which can be very helpful for SAS Coders, where they can write their own SAS Code for analytics. Users can bring Facebook data to SAS Enterprise Guide or Base SAS and find all sorts of interesting things | sas visual analytics, sas enterprise guide | source | 10 |
| 74 | Five Ways to Create Macro Variables: A Short Introduction to the Macro Language | Arthur L. Carpenter | The macro language is both powerful and flexible. With this power, however comes complexity, and this complexity often makes the language more difficult to learn and use. Fortunately one of the key elements of the macro language is its use of macro variables, and these are easy to learn and easy to use. Macro variables can be created using a number of different techniques and statements. However the five most commonly methods are not only the most useful, but also among the easiest to master. Since macro variables are used in so many ways within the macro language, learning how they are created can also serve as an excellent introduction to the language itself. These methods include: %LET statement macro parameters (named and positional) iterative %DO statement using the INTO in PROC SQL * using the CALL SYMPUTX routine |
macro variable, macro facility, macro put, macro loop, proc sql, put statement, do loop, macro call, proc step, missing value, macro parameter, macro definition, %macro parameters, automatic macro variable, symbol table, keyword parameter, select, call symput, call symputx | source | 12 |
| 75 | Generating Colors from the Viridis Color Scale with a SAS Macro | Shane Rosanbalm | In this paper we present a SAS macro capable of producing a list of RGB color values from the Viridis color scale. The Viridis color scale was originally designed for MATLAB as an open-source alternative to MATLAB's proprietary Parula color scale, which itself was a replacement for the oft criticized Jet color scale. Viridis is designed to be: Colorful, spanning as wide a palette as possible so as to make differences easy to see, Perceptually uniform, meaning that values close to each other have similar-appearing colors and values far away from each other have more different-appearing colors, consistently across the range of values, Robust to colorblindness, so that the above properties hold true for people with common forms of colorblindness, as well as in grey scale printing, and Pretty, oh so pretty | heatmap, line chart, macro call, sas programmer | source | 8 |
| 76 | Generating Homeomorphically Irreducible Trees (Solving the Blackboard Problem in the Movie - Good Will Hunting-) | John R. Gerlach | In the movie, Good Will Hunting (1997), a mathematics professor challenges his students to draw all Homeomorphically Irreducible Trees of Order Ten, that is, a collection of trees each having ten dots connected by lines. The well-known blackboard problem in the movie poses a formidable challenge, especially for larger trees having twenty or thirty nodes. It would require an extremely large blackboard to draw all the trees, as well as to erase those deemed redundant or incorrect. This paper explains a SAS solution for generating Homeomorphically Irreducible Trees of order N | if-then-else, data mart, when, macro variable | source | 14 |
| 77 | Good artists copy;Great artists steal; (implement pseudo R ,Jquery & code in Base SAS without installing R or other Applications | Hui Liu | There are some syntax sugars in other languages we are dreaming of using in SAS. Why? Because they are more expressive. To borrow these syntax sugars usually we install other languages and use them in the background by passing X command to them or relying on some modern SAS modules such as PROC IML to link them together. The downside of these ways is that the code is no longer the pure SAS code and when we pass SAS code mixed with R code to FDA they have to use a computer with both SAS/IML and R installed to regenerate results. We aren't supposed to carry a cow everywhere when we only want to drink a cup of milk. So, our goal is to STEAL these syntax features from other languages and implement them in pure SAS BASE/macro as pseudo code. The philosophy of pseudo code is "what you see is what you get, almost". Examples of code syntax we want to steal (include but not limited to) a |
sas base, sas programmer | source | 8 |
| 78 | Good Data Validation Practice | Sergiy Sirichenko, Max Kanevsky |
According to FDA and PMDA guidance for regulatory submissions, sponsors are expected to perform study data validation and explain any issues that were not fixed. This is a new requirement introduced only a few years ago, which is why the industry is still struggling to achieve full implementation. In many cases, data validation is not performed correctly with sponsors having the wrong interpretation of validation results, or having invalid, confusing, or non-relevant issue explanations. The major reason for these problems is a lack of explicit and detailed documentation about the data validation process. The purpose of this presentation is to address this gap. We will summarize the regulatory requirements for data validation, discuss basic concepts and methodology, provide instructions on correct installation and usage of Pinnacle 21 Community open source tool, and review best practices for how to explain issues in study data | data cleaning, data validation rules, regulatory submission, missing value, cdisc controlled terminology, adverse events, flag variable, cdisc | source | 10 |
| 79 | Good Programming Practices at Every Level | Maria Dalton | Programming in the pharmaceutical industry focuses on transforming and analyzing clinical data. It is critical for pharmaceutical programs to be accurate since decisions about the safety and efficacy of drugs are made based on their results. In addition, programs must be well-documented, efficient, and reusable - this is necessary to meet tight timelines and resource constraints and to ensure that programs can be understood by other programmers and regulators. Thus, it is critical that pharmaceutical programmers follow good programming practices (GPP). This paper explores the topic of GPP in the pharmaceutical industry at different levels - at the level of individual programs, at the study level, and at the compound or therapeutic area level. At each level, there are different yet complementary practices that a programmer should follow to produce accurate and robust programs | adam dataset, pharmaceutical industry, clinical trial, data science, cdisc | source | 6 |
| 80 | Good versus better SDTM: Including Screen Failure Data in the Study SDTM? | Henry B. Winsor, Mario Widel |
In the FDA Study Data Technical Conformance Guide, first released in December, 2014, there are a couple of paragraphs on the topic of the two subject identifier variables defined in SDTM and some details of how FDA would like to see them implemented. Implicit in this section is the request that data from screen failure subjects be included in the study SDTM data. Although not a formal requirement (yet), FDA is very interested in receiving information about a study's screen failures/rescreen attempts and has been quite persistent in continuing to ask to be provided that data by sponsors. This paper discusses steps taken at different companies to meet that request. It may provide some guidance of its own if you are thinking about but have not yet taken any steps to capture and report information about your screen failure subjects | study day, treatment arm, clinical trial, cdisc | source | 9 |
| 81 | Gray Areas of ADaM and Ways to Provide Color | Susan J. Kenny, Sandra Minjoe, Nate Freimark, Jack Shostak, John K. Troxell, Deborah J. Bauer |
Presentation | adam dataset, imputation, data standard, phase i, sdtm domain, cdisc | source | 38 |
| 82 | Harmonizing CDISC Data Standards across Companies: A Practical Overview with Examples | Keith Shusterman, Prathima Surabhi, Binoy Varghese |
Whether due to the fact that standardized data are more useful, or that submission of CDISC data is now required, many companies have worked hard to establish local CDISC interpretation guides. Those guides support consistent application of CDISC SDTM and ADaM standards within a company. When companies merge or collaborate, company-specific data standards need to be harmonized to ensure a smooth hand-off of data for the purposes of analysis and reporting. We will share a gene ral overview of the issues encountered in harmonization and present strategies on how to effectively plan and harmonize company-specific CDISC implementation standards. We will walk through a few representative examples from a recent harmonization effort | adam dataset, controlled terminology, crf, cdisc controlled terminology, sdtm domain, study day, excel, cdisc | source | 6 |
| 83 | Harnessing the Power of the Manifest File in the SAS Life Science Analytics Framework | Kevin Clark | The job manifest file is a very useful file that contains valuable information about a job created using the SAS Life Sciences Analytic Framework. Unfortunately, many users of SAS Life Science Analytics Framework do not fully understand the purpose of the job manifest file and how it can be used. The purpose of this paper is to describe the types of information that are contained within the job manifest file and demonstrate a step-by-step process for using this file to recreate a job. In the SAS Life Science Analytics Framework, a job refers to a set of one or more SAS programs, the inputs required to run these programs, and the locations in which to store the outputs of the programs. Jobs can be utilized by users to run tasks that must be performed on a regular basis. By parsing the job manifest file for information about a job and its associated tasks, inputs, outputs, and parameters, it is relatively simple to recreate the job | life sciences, sas log | source | 3 |
| 84 | Hashtag #Efficiency! An Exploration of Hash Tables and Other Techniques | Lakshmi Nirmala Bavirisetty, Kaushal Chaudhary, Deanna Naomi Schreiber-Gregory |
Have you ever had to walk away from your computer during an analysis? Have you wondered if there is a way to increase your efficiency, save time, and be able to answer more questions? Hash tables to the rescue! This paper covers a brief introduction to the use of hash tables, their definition, benefits, concept, and theory. It also includes a review of some more applied approaches to hash table usage through code examples and applications that illustrate how using of hash tables can help improve performance time and coding efficiency. This paper will wrap up by providing a comparison of performance times between hash tables and traditional lookup and join/merge methods. An additional discussion of instances in which hash tables may not be the most effective method will also be explored. This paper is intended for any level of SAS user who would like to learn about how hash tables can help process efficiency! | hash table, cpu time, proc step, proc sql, memory usage, merge statement, proc means, sas log, attrib statement, hash iterator, conditional do loop | source | 9 |
| 85 | Here Comes The Smart Mock Table: A Novel Way of Creating Clinical Summary Tables Without Any Table Programming (No Kidding!) | Joseph W. Hinson | The creation of a clinical summary table typically involves two main phases: the statistical analyses of data, and the presentation of the analyses results onto a layout predefined by a mock table. The first phase can be pretty straightforward, simply involving the calling of SAS statistical procedures plus a few DATA steps. The second phase, a relatively time-consuming part, would constitute taking those procedure outputs and programming their placements in specified positions to become the summary table. It turns out that this second phase might not even be necessary. The mock table can be made to populate itself with analyses results! Such a "smart" mock table can be made by embedding macro calls directly in the RTF mock document. These macros would contain SAS procedures or DATA STEP codes wrapped inside a DOSUBL function and called by a %SYSFUNC to generate single macro variables |
proc step, macro call, macro variable, macro function, dosubl, data science, &variable | source | 19 |
| 86 | How to Create a Journal-Quality Forest Plot with SAS 9.4 | John R. O'Leary, Warren F. Kuhfeld |
This paper offers a solution for creating a high quality forest plot that is befitting even the most prestigious scientific journals. Building on code developed by Sanjay Matange and Prashant Hebbar, this paper provides intermediate SAS programmers further versatility through a few simple enhancements. In particular, SG annotation is used to display text and handle special characters through its Unicode support (see Heath 2011). Additionally, error bar markers are sized based on subgroup population sizes to show treatment effectiveness, and hazard ratios (HR) are properly displayed on a log scale. These enhancements became the basis for a forest plot figure that was published in the New England Journal of Medicine (NEJ, see Kernan et al., 2016) for the Insulin Resistance Intervention after Stroke (IRIS) study, a double-blind randomized control trial (RCT) in 3,876 participants designed to prevent secondary stroke or myocardial infarction in patients with insulin resistance | sg annotation, forest plot, proc step, scatter plot, confidence interval | source | 11 |
| 87 | How to define Treatment Emergent Adverse Event (TEAE) in crossover clinical trials | Mengya Yin, Wen Tan |
Treatment Emergent Adverse Event, TEAE, defines as "an event that emerges during treatment, having been absent pretreatment, or worsens relative to the pretreatment state" according to the E9 guideline. In crossover clinical trials, TEAE can be more complicated due to several factors such as occurrence in washout period, severity change and partial dates. In this paper, we present these AE scenarios and give suggest solutions. General considerations as well as a conservative method for handling such scenarios are also discussed | clinical trial, washout period, crossover study, imputation, trial elements, adverse events | source | 6 |
| 88 | How to find the best MDR solution for your organization | Kevin Lee | Are you satisfied with the current Metadata Repository (MDR) solution strategy at your organization? Are you looking for a more efficient, beneficial MDR platform or to be more educated on the crucial factors of MDR solution? Metadata Repository (MDR) has become essential for Life Sciences industries to store and manage standards (e.g., CDISC and company specific standards) and terminologies. However, many of organizations are struggling with current Metadata Repository (MDR) solutions because of version controls of standards, instabilities within systems, resistance of study teams and other issues. The paper will elucidate common challenges, concerns and typical user complications encountered during the MDR selection and implementation process. The paper will demonstrate various solutions to these frequent issues | proof of concept, clinical trial, cdisc | source | 3 |
| 89 | How will FDA Reject non-CDISC submission? | Kevin Lee | Beginning Dec 18, 2016, all clinical trial and nonclinical trial studies must use standards (e.g., CDISC) for submission data and beginning May 5, 2017, NDA, ANDA, and BLA submissions must follow eCTD format for submission documents. In order to enforce these standards mandates, the FDA also released "Technical Rejection Criteria for Study Data" in FDA eCTD website on October 3, 2016. FDA also implemented a rejection process for submissions that do not conform to the required study data standards. The paper will discuss how these new FDA mandates impact the electronic submission and the required preparation for CDISC and eCTD complaint submission package such as SDTM, ADaM, Define.xml, SDTM annotated eCRF, SDRG, ADRG and SAS programs. The paper will introduce the current FDA submission process, including the current FDA rejection processes - "Technical Rejection" and "Refuse-to-File" and discuss how FDA uses "Technical Rejection" and "Refuse-to-File" to reject submission | data cleaning, data validation rules, cdisc | source | 6 |
| 90 | I've Got to Hand It to You; Portable Programming Techniques | Arthur L. Carpenter | As technology expands, we have the need to create programs that can be handed off - to clients, to regulatory agencies, to parent companies, or to other projects, and handed off with little or no modification by the recipient. Minimizing modification by the recipient often requires the program itself to self-modify. To some extent the program must be aware of its own operating environment and what it needs to do to adapt to it. There are a great many tools available to the SAS programmer, which will allow the program to self-adjust to its own surroundings. These include location-detection routines, batch files based on folder contents, the ability to detect the version and location of SAS, programs that discern and adjust to the current operating system and the corre sponding folder structure, the use of automatic and user defined environmental variables, and macro functions that use and modify system information. Need to create a portable program? We can hand you the tools | macro variable, options statement, sas log, automatic macro variable, macro put, macro function, macro definition, autocall macro, macro flow control, libname statement, put statement, macro call, sas enterprise guide, macro facility | source | 21 |
| 91 | Implementation of STDM Pharmacogenomics/Genetics Domains on Genetic Variation Data | Linghui Zhang | Pharmacogenomics (PGx) explores how gene expression and genetic makeup affect individual responses to drugs. It has been recognized since the early 1960s that inherited variation contributes to drug responses. Currently PGx studies are widely used at various stages of drug development and labeling, such as stratifying patients in clinical trials, improving drug safety and optimizing doses. Although the Guidance for Industry: Pharmacogenomics Data Submissions was officially issued by FDA in 2005, due to the complexity and specificity of PGx data, the Clinical Data Interchange Standard Consortium (CDISC) published the clinical data standard for genomics and biomarker data, Study Data Tabulation Model Implementation Guide: Pharmacogenomics/Genetics (SDTMIG -PGx) in May 2015 | sample, clinical trial, clinical data, cdisc | source | 12 |
| 92 | Implementing Patient Report Outcome data in clinical trials analysis | Qi Wang | Over the recent decades, Health-Related Quality of Life (HRQoL) end points have been increasingly adopted in oncology and hematology clinical trials. This paper will focus on analyzing HRQoL data accumulated from the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30) and the Acute Lymphoblastic Leukemia Symptom Scale (ALLSS). This paper will introduce various statistical methods to analyze the HRQoL data; and present some challenges and considerations in developing ADaM data specifications and performing statistical analysis | missing value, imputation, data science, clinical trial, cdisc | source | 7 |
| 93 | Important and Valuable Things You Can Do with SAS Metadata DICTIONARY Tables and SASHELP Views | Kirk Paul Lafler | SAS users can easily and quickly access metadata content with a number of read-only SAS data sets called DICTIONARY tables or their counterparts, SASHELP views. During a SAS session, information (known as metadata) is captured including SAS system options along with their default values, assigned librefs, table names, column names and attributes, formats, indexes, and more. This presentation explores how metadata can be used as input into a SAS code generator or a SAS macro to produce the desired results, the application of specific DICTIONARY table and SASHELP view content, examples related to the creation of dynamic code, and the creation of a data dictionary | dictionary tables, proc sql, proc step, create table, sas library, options statement, select | source | 8 |
| 94 | Importing CSV Data to All Character Variables | Arthur L. Carpenter | Have you ever needed to import data from a CSV file and found that some of the variables have been incorrectly assigned to be numeric? When this happens to us we may lose information and our data may be incomplete. When using PROC IMPORT on an EXCEL file we can avoid this problem by specifying the MIXED=YES option to force all the variables to be character. This option is not available when using IMPORT to read a CSV file. Increasing GUESSINGROWS can help, but what if it is not sufficient? It is possible to force IMPORT to only create character variables. Although there is no option to do this, you can create a process that only creates character variables, and the process is easily automated so that no intervention is required on the user's part |
proc import, proc step, dataset, character variable | source | 5 |
| 95 | In-Licensing from a Programming Perspective | Asif Karbhari | Over the past few years, the Pharmaceutical industry has seen many acquisitions or divestments of products so companies can focus on strategic niches. As a result, the receiving company finds itself inheriting another company's clinical work. To make that a success, it is key to understand what clinical data are already available and what needs to be done at its current status. The programming function plays a valuable role in ensuring that inherited data are workable/reusable and assessing how closely it aligns with Clinical Data Interchange Standards Consortium (CDISC) standards to help plan for future deliverables. This paper shares some experiences, challenges faced, and lessons learned as a programmer in the "in-licensing" process for two projects at different transition stages, including topics on transferring files, inventorying what has been received, assessing submission readiness, and quickly becoming data experts | pharmaceutical industry, data science, cdisc | source | 5 |
| 96 | Innovation and Regulatory Review What is JumpStart? | Alan Shapiro | data cleaning, high-level group term, system organ class, adverse events, clinical trial, data science, proc means | source | 24 | |
| 97 | Insight into Offsites: creating a productive workflow with remote employees. | Maddy Wilks | As the proportion of on to off-site employees shifts within a workplace, it becomes increasingly important to have tools in place to make communication between the two parties efficient. This paper prompts discussion about what to consider when working with remotes, and answers questions ab out how to create a productive and healthy workspace from a "blended" employee population. Careful planning of communication tools, working hours, time off, and availability of each employee is important. Expectations for both on-site and remote programmers should be clearly defined. Time zone differences should be addressed. Timely recording and dissemination of project information is very important. Verbal and non - verbal communication that is second nature on-site needs to be intentionally addressed to off-site employees | source | 4 | |
| 98 | Integration of multiple files into a study report using Word VBA macros | Heather Wood, Jack Shostak |
This paper provides an overview of a report integration process which uses Word Visual Basic for Applications (VBA) macros and takes multiple individual output files that have been produced by SAS programs, or by other means, and compiles them all into an integrated study report. The process begins with a text file (.TXT) which lists the names and paths of all the outputs that are desired in the study report, in the order in which they need to appear. The report integration VBA macros use this text file to identify and process each output for the study report. The section breaks which SAS PROC REPORT puts at the end of every page of an output are replaced with page breaks. Then the outputs are all compiled together with a new section break separator between each output. This results in a study report compiled from several outputs with one section break between each output, rather than one section break between each page of the report |
txt, proc step | source | 10 |
| 99 | Is There a Date In That Comment? Use SAS To Find Out. | Keith Hibbetts | When anonymizing data from clinical trials, it's critical to identify any dates contained in the data. This can be a challenge when dealing with data that came from free form text entry, such as comments, where a date could be one small piece of a larger text string. This paper will show how utilizing Perl Regular Expressions in SAS code can identify records that likely contain a date | sample, clinical trial | source | 4 |
| 100 | Laboratory Data Standardization with SAS | Charley Z. Wu, Dona-Lyn Wales |
In clinical trials, laboratory data usually come from central laboratories and/or many different local laboratories. Different laboratories usually have different original reported laboratory test names, units and reference ranges. In order to pool and analyze these reported data efficiently and correctly, central/local laboratory test names, units and results must be converted to a standard format. A SAS-based laboratory standardization application was built in order to standardize original reported laboratory data to SDTM standards and associated controlled terminology | laboratory data, controlled terminology, outlier, data review, clinical trial, cdisc | source | 12 |
| 101 | LBTEST/LBSTRESU and ADaM lab parameters: The dilemma of mapping one-to-many or many-to-one | Michelle Barrick | Lab results for clinical trials need to be uniquely identified to allow them to be combined for analysis. Multiple source laboratories may have varying names or identifiers for the same test, so standardization is needed to identify lab tests that are the same. Industry wide, there are efforts to standardize lab codes via LOINC but that is still a work in progress and not a mandate. Many pharma companies have their own internal code system to uniquely identify lab tests. These internal codes are mapped to SDTM CDISC terms for LBTESTCD/LBTEST and associated units. Mapping those tests to ADaM parameters becomes a bit more complex because PARAMCD/PARAM must uniquely and unequivocally describe what is in AVAL or AVALC. It is not enough to just look at the SDTM LBTEST (and associated unit) to determine what your parameter should be. This paper will explain some of the levels of detail that needs to be assessed to correctly define an ADaM lab PARAMCD/PARAM | clinical trial, raw data, cdisc | source | 5 |
| 102 | Lead Programmer Needs Help: Dedicated Programming Project Manager to the Rescue! | Gloria Boye, Aparna Poona, Bhavin Busa |
The Statistical Programming team functions as the engine that drives major components of statistical deliverables for a study such as CDISC compliant datasets (SDTM/ADaM), TLFs, and associated submission documents. A typical late stage clinical study could require a sizeable programming team, i.e. anywhere between 6 to 10 full-time resources. For such a study, there is an assigned Lead Programmer who is responsible for monitoring progress of the programming tasks and to ensure quality of the deliverables. In addition, he/she has to direct and distribute programming activities to the study team who are either full-time or contract hires. Essentially, the Lead Programmer becomes responsible for both the technical as well as the resource management aspects of the project. However, coupling these responsibilities with managing a team which is largely virtual can be overwhelming for a single resource | clinical programming, clinical trial, adam dataset, cdisc | source | 7 |
| 103 | Let-s Check Data Integrity Using Statistical (SAS) Programmers with SAS | Harivardhan Jampala | The success of any clinical trial depends on the accuracy and integrity of the study conduct and the data produced from the trial. As in any experiment, data plays the central role and almost everybody involved in a clinical trial generates, maintains, or explains data. Hence, we can all agree: It is vital that the data is clean and, more importantly, that it is fully utilized to make everyone's job easier and efficient. A plethora of software, programing languages, and tools are employed by various contributors in clinical research across the industry to help make sense of clinical and operational data. The scope of and investment in such tools depends on the budget and organizational priorities. There are organizat ions that operate with moderate or minimalistic software resources. This paper will explore various ways in which SAS programmers (either statistical programmers or clinical programmers) can help other departments in such organizations | statistical programmer, adverse events, clinical trial, data governance, proc step, clinical data | source | 17 |
| 104 | Leveraging Centralized Programming Resources Using SAS Integration Technologies | Tony Chang | Remote access from multiple locations to centralized SAS services can greatly benefit to the globalization of pharmaceutical and bio-tech industry. SAS Integration Technologies allows client application to interact with SAS services from any remote sites where different system and infrastructure may be used. This paper describes a custom client application that we developed using SAS Integration Technologies. The application was used for a subsidiary company running medical dictionary coding for their clinical study data by leveraging SAS modules that invoke Amgen's centralized coding system. The tasks accomplished by our client application are explained in this paper, which includes dynamically generating SAS program by the client application, running SAS program on the remote server, and transferring data sets between the client machine and the SAS server. The benefits of the client application using SAS Integration Technologies are also discussed | clinical trial, sas log, csv, macro definition | source | 8 |
| 105 | Leveraging metadata when mapping to CDISC Standards with SAS machine learning in a Results as a Service plus Model (RAAS+) | Ben E. Bocchicchio, Sandeep Juneja, Preetesh Parikh |
Mapping raw Electronic Data Capture ( EDC) data to a Study Data Tabulation Model (SDTM) or Analysis Data Model (ADaM) standard is a very time consuming process. As the evolution of standards continues to develop, the ability to collect and reuse metadata on how raw variables get mapped into standardized variables can help reduce the time to standard ize subsequent studies. The reduction in time and effort comes from the ability to reuse the mapping by analyzing the raw and standardized metadata. Using the power of Machine Learning algorithms can further help derive and predict possible mappings. Instead of a customer having to install software locally and making the necessary configurations for it to run, SAS can offer this as a hosted solution. This means that customer can upload data and standards, run the process, and results of the process will be SAS code containing the mapping information that can be utilized in the customer environment | raw data, machine learning, cdisc | source | 6 |
| 106 | Leveraging Study Data Reviewer-s Guide (SDRG) in Building FDA-s Confidence in Sponsor-s Submitted Datasets | Xiangchen (Bob) Cui, Min Chen, Letan (Cleo) Lin |
FDA issued Study Data Technical Conformance Guide [1] in October 2016, which stipulates "The SDRG should describe any special considerations or directions that may facilitate an FDA reviewer's use of the submitted data and may help the reviewer understand the relationships between the study report and the data." Hence SDRG not only supports regulatory review and analysis, but also establishes the traceability of tabulation datasets (SDTM), and source data (raw data). FDA reviewers consider traceability as an important component of a regulatory review [1]. Confidence in submitted datasets (SDTM and ADaM) can be established through traceability from the datasets, their define files and reviewer's guides (SDRG and ADRG). PhUSE released SDRG Package v1.2 [2] on January 26, 2015, which provides a step-by-step template that helps sponsors to prepare SDRG. This paper presents the readers how to build the traceability in SDRG, further build the FDA's confidence in sponsor's submitted datasets | if-then-else, clinical trial, csv, protocol deviations, sdtm domain, raw data, edc, missing value, annotated crf, phase i, sdtm programming, cdisc | source | 10 |
| 107 | Make it personal: Upgrade your figures by adding individual patient data to the common figure types | Yuliia Bahatska, Christiane Ahlers |
Figures are believed to be the most flexible way of data presentation in the world of clinical trials. Indeed, with the abilities of the GTL and graphic procedures in SAS today it is only the power of the statistician's imagination that defines the complexity of the figures in the analysis. In the clinical study reports it is usually referred to summary statistics tables for the general overview and more detailed information about a certain patient can be found in the listings. Figures can present both of these. In this paper we would like to share with you our experience of creation of the figures that contain both the combination of summary statistics and individual patient data. The main idea is to enable the reviewer to trace how the values for a single patient changed throughout the study and at the same time put them into context with the derived statistics or other patients' values and also to identify outliers | scatter plot, proc freq, proc means, box plot, series plot, clinical trial, sample | source | 20 |
| 108 | Making Documents "Intelligent" with Embedded Macro Calls, DOSUBL and Proc STREAM: An example with the CONSORT Flow Diagram | Joseph W. Hinson | Documents can be considered 'intelligent' if they can self-process parts of themselves. One way is to embed them with macro elements. Such macro-laden documents can then be placed on the SAS Input Stack for macro processing. However documents like RTF, XML, and HTML, tend to have extraneous codes that would violate SAS syntax if placed on the Input Stack as is. Placing non-SAS documents on the Input Stack therefore requires the STREAM Procedure, which by disabling the SAS Compiler can allow the intact document codes mixed with macro elements. Once the Macro Facility has resolved all the macro elements, the document is streamed back to a file location. Having document templates embedded with macro variables is nothing new, but until now, the role had been limited to just text substitutions. By embedding documents with actual macro calls, self-processing of documents becomes possible |
proc step, macro call, macro variable, dosubl, proc sql, macro function, &variable | source | 15 |
| 109 | Managing conflicts across Cross Functional and Global Virtual Teams | Arun Raj Vidhyadharan, Sunil Mohan Jairath |
Almost all of us work with people situated around the globe, and some of those people are home based and some are office based. For various reasons like improve efficiency, process flow, decision making, etc. organizations are now moving to teams that are made up of repres entatives from various functions and these teams are called as Cross Functional teams. The representatives from various functions on the team work with people from other functions and they are expected to blend in their collective expertise to come up with well thought out solutions, ideas and decisions. Quite often such teams run into problems and are unable to work to their full potential because of inherent relationship issues. The lack of co- operation soon becomes apparent when the team members work at cross purposes. In reality, functions or departments gradually become so full of themselves that they become a mini -organization within an organization | source | 8 | |
| 110 | Mapping Participants to the Closest Medical Center | David W. Franklin | "How far are patients from Clinics?" That was the question which was asked on a wintery afternoon. Recorded in the database was the ZIP codes of each participant, but how could we use that data to find the closest distance from the nearest clinic? "And would it not be nice if we could map it?" This paper looks at calculating the distance from a participant ZIP code to a Medical Center using the GEODIST function, finding the smallest distance for each participant to attend, and finally color-coding the plots depending on the distance calculated using the GMAP procedure. Also helpful is producing reports, one of which will be the those participants who are farthest away and the distance |
proc gmap, sashelp | source | 8 |
| 111 | Mean and Individual Subject Graphs of Concentration vs. Time Data Using PROC SGPLOT | Pooja Trivedi | In clinical research Industry, Clinical Study Report (CSR) is very important document because it contains all the trial related activities, results and conclusion of trial. Graphs are also one of the important parts of the CSR as graphs are visual representation of the data and help us to understand the data in better way. The main purpose of this paper is to create highly presentable graphs which can be directly incorporated into CSR or as appendices. The mean and individual subject's graphs of concentration vs. time data are most common graphs which show change in drug concentration in the body with respect to time | proc sgplot, proc step, output delivery system, ods rtf, macro variable | source | 9 |
| 112 | Mentoring and Oversight of Programmers across Cultures and Time Zones | Chad W. Melson | Positions that provide SAS programmers the opportunity to do both programming and managing are not always available in the world of consulting. Fortunately, I was offered the role of liaison between clinical SAS programmers located in Ukraine and a client located on the US West Coast, while also maintaining my own project responsibilities as a programmer. I also had the opportunity to mentor students from a University training program for Clinical SAS programmers in Ukraine. These positions offered me a chance to mentor Ukrainian colleagues and apply and develop my technical and managerial skills. The liaison role was a new model for the client and there was some apprehension about the time difference and the unknowns of working with a team from a global region they had no prior experience with | clinical sas, lag function, clinical programming, sas programmer, data science | source | 7 |
| 113 | Merge with Caution: How to Avoid Common Problems when Combining SAS Datasets | Joshua M. Horstman | Although merging is one of the most frequently performed operations when manipulating SAS datasets, there are many problems which can occur, some of which can be rather subtle. This paper examines several common issues, provides examples to illustrate what can go wrong and why, and discusses best practices to avoid unintended consequences when merging | merge statement, out=, in=, by group processing, pdv, length statement, dataset | source | 10 |
| 114 | Metadata integrated programming | Jesper Zeth, Jan Skowronski |
With the growing complexity of pharmaceutical projects it is becoming increasingly relevant to address how we are programming, and how we ensure that our programming is as flexible as possible with regards to implementing changes and re-using programs across projects. It is essential to be able to implement requested changes as fast as possible, with certainty that the changes are implemented everywhere needed, and that we are able to scale changes across projects, trials and subjects. One of the important tools that can be used in this context is integrating metadat a in both data and output programming. When designed properly the use of metadata in programming provides the ability to implement a variety of changes across datasets and outputs, by only changing the actual metadata, making changes both very fast and uniform. The purpose of this paper is to illustrate how metadata can be integrated in both data and output programming tasks | macro variable, proc template, proc step, dictionary tables, excel | source | 12 |
| 115 | Metadata of Titles, Footnotes, and Proc Report Details | Julius Kirui | Biostatisticians devote numerous hours of research time to the development of tables, figures , and listings (TFL) mock shells. The information provided by the mock shells include titles, footnotes, and formatting details, such as column labels, alignments, and widths. Statistical SAS programmers are tasked with transferring this information word by word into the final TFLs. Manual transfer of this metadata can be challenging, labor-intensive, and prone to errors. Several SAS users have written and shared their thoughts on how titles and footnotes can be adopted for TFL production with minimal manual involvement. This paper will discuss and provide a SAS 9.4 macro program to generate ready to use metadata of titles, footnotes, and TFL formatting details from a standard set of TFL mock shells. Also, TFL reporting details such as output labels and program names will be discussed | mock shell, excel | source | 11 |
| 116 | Migration to SAS Grid: Steps, Successes, and Obstacles for Performance Qualification Script Testing | Amanda Lopuski, Yongxuan Mike Tan |
Through detailed planning and preparation, we set our sights on a successful migration of our global macros and Unix utilities to a Linux based system on SAS Grid. But how did our planning and preparations fare when it was go time? In this paper we will share the steps taken to prepare for performance qualification testing of our global macro and utility scripts. We will reflect back on the successes and obstacles faced in this new environment during testing, the methods we developed to mitigate issues as they occurred, and how we prepared for clean execution in the VAL environment, then conclude with takeaways from this experience. This paper will be useful to any group with sights similar to ours: migration to SAS Grid. It will provide valuable, first-hand insight into the migration with emphasis on performance qualification (PQ) testing of global macros and utilities | sas grid, proc sql | source | 6 |
| 117 | Mindfulness at Work: Handling Stress and Changes Gracefully and Be the Leader You Were Born To Be | Helena Ho | In the pharmaceutical industry, change has become the norm and the pace is getting faster. Based on a PwC National survey, chances are you are part of the 1/3 of Americans who feel burnt out at work. But considering we spend about half of our waking life at work, why not change the attitude? In this paper, we discuss some of the latest research surrounding Mindfulness and how we can use this method to help us break unhelpful stress patterns, deal with changes gracefully and to let the leader within you shin e forth in even the most stressful times | source | 7 | |
| 118 | Multi-Dimensional Arrays: Add Derived Parameters | Siddharth Kumar Mogra | Multidimensional arrays are useful when Data needs to be grouped into a 'table like' arrangement for processing. Often we are required to derive and add records in analysis datasets. When there are large numbers of parameters to be added, the use of repeated if-then statements are prone to error and time consuming. Effective use of Multi-Dimensional Arrays or Nested Arrays can increase efficiency of program. It can minimize the code and makes program efficient. This paper demonstrates situation where use of Multi-Dimensional Arrays is practical and appropriate in Clinical Trials | adam dataset, data step, array, clinical trial | source | 7 |
| 119 | Multinomial Logistic Regression Models With Sas Proc Surveylogistic | Marina Komaroff | Proportional odds l ogistic regressions are popular models to analyze data from the complex population survey design that includes strata, clusters, and weights. However, when the proportional odds assumption is violated (p -value < .05 for chi -square statistic), the use of multinomial logistic regression models for survey designs becomes challenging. This paper provides guidance in using multinomial logistic regression models to estimate and correctly interpret the relationships b etween predictor and multiple levels of nominal outcome with and without interaction term. The author developed a SAS MACRO utilizing PROC SYRVEYLOGISTIC that will help researchers to conduct statistical analyses. The U.S. National Health and Nutrition Exa mination Survey (NHANES) is a probability sample of the US population . These data sets were used in the examples of multinomial logistic regression modeling techniques |
multinomial logistic regression, logistic regression, regression analysis, logistic distribution, odds ratio, proc surveylogistic, interaction effect, response variable, proc step, predictor variable, data science, relative risk | source | 7 |
| 120 | Multipage Adverse Event Reports Using PROC SGPLOT | Warren F. Kuhfeld, Mary Beth Herring |
Researchers and safety monitoring committees need to track adverse events (AEs) among clinical trial participants. Adverse events are commonly reported as a tabular summary by system organ class and preferred term for each treatment group. But it can be difficult to identify trends or potential safety warnings simply by reviewing numbers in a summary table. Trends are much easier to identify when the frequency of each AE is displayed in a graph. The SGPLOT procedure can use axis tables to display tabular information next to scatter plots, but multipage printed reports require additional programming. This paper shows how to use PROC SGPLOT to create elegant multipage adverse event reports. Most of the work involves preparing the data so that page breaks occur in reasonable places and so that groups of adverse events and continuations onto a new page are both properly labeled. PROC SGPLOT along with a BY statement produces the final report |
proc sgplot, proc step, adverse events | source | 17 |
| 121 | Multiple Imputation: A Statistical Programming Story | Christopher J. Smith, Scott Kosten |
Multiple imputation (MI) is a technique for handling missing data. MI is becoming an increasingly popular method for sensitivity analyses in order to assess the impact of missing data. The statistical theory behind MI is a very intense and evolving field of research for statisticians. It is important, as statistical programmers, to understand the technique in order to collaborate with statisticians on the recommended MI method. In SAS/STAT software, MI is done using the MI and MIANALYZE procedures in conjunction with other standard analysis procedures (e.g. FREQ, GENMOD or MIXED procedures). We will describe the 3-step process in order to perform MI analyses. Our goal is to remove some of the mystery behind these procedures and to address typical misunderstandings of the MI process. We will also illustrate how multiply imputed data can be represented using the ADaM standards and principals through an example - driven discussion |
imputation, proc step, proc mi, missing value, proc mianalyze, interval variable, regression analysis, multiple imputation, by group processing, confidence interval, proc print, proc sort, proc calis, clinical trial, response variable, statistical programmer, sample, proc mixed, cdisc | source | 16 |
| 122 | Multiplicity Controlled Analyses Using SAS/IML | Xingxing Wu, Hangtao Xu |
Multiplicity controlled analyses are very important for the clinical trials with more than one objectives. Graphical approach proposed recently is becoming popular because of many advantages. Related tools implemented in R programming language have been developed to apply this approach to clinical trials. Even though the code developed in SAS/IML is available to apply this approach in some straightforward cases, there is still lack of a comprehensive tool to fully apply this approach into multipli city controlled analyses in clinical trials. In this paper, the program implemented in SAS/IML is presented because of the popularity of SAS. This program can not only be applied in the case when the p-value of each endpoint is available, it can also estimate the endpoint p-values and predict the success rate (power) of each endpoint through simulation to help the early decision-making |
clinical trial, multiplicity, null hypothesis, significance level, effect size | source | 8 |
| 123 | My bag of SAS lifehacks | Dmytro Hasan | Have you ever come across anything special in another person's code that made your brain freeze? If your answer is yes, then I hope this article will be of interest to you. Sometimes it can be very difficult to grab the message the programmer wanted to convey in their code lines. In this article, I would like to share my remarks about the issues that I find the most interesting. I have caught sight of some of them in other people's SAS programs or read up in different books and used them in my programs. We will consider a missing RUN statement at the end of the DATA STEP and calling procedure(s) without specifying the dataset, which decreases the length of the code. Some features of PROC SQL will be analy zed which may seem non-trivial for the programmers who are less experienced with this procedure. We will go back to the beginning and try to find out what COMMENT does and in what ways it can be helpful |
proc sql, proc step, run, macro variable, missing value, create table | source | 10 |
| 124 | New for SAS 9.4: Including Text and Graphics in Your Microsoft Excel Workbooks, Part 2 | Vincent DelGobbo | A new ODS destination for creating Microsoft EXCEL workbooks is available starting in the third maintenance release for SAS 9.4. This destination creates native Microsoft EXCEL XLSX files, supports graphic images, and offers other advantages over the older ExcelXP tagset. In this presentation you will learn step-by-step techniques for quickly and easily creating attractive multi-sheet EXCEL workbooks that contain your SAS output. The techniques can be used regardless of the platform on which SAS software is installed. You can even use them on a mainframe! Creating and delivering your workbooks on-demand and in real-time using SAS server technology is discussed. Although the title is similar to previous presentations by this author, this presentation contains new and revised material not previously presented. Using earlier versions of SAS to create multi-sheet workbooks is also discussed |
excel, xls, adverse events, proc report, sas output, proc step, sas base, dataset, automatic variable, bar chart, proc tabulate, options statement, proc freq, sample | source | 26 |
| 125 | Obtaining an Automated Summary of PROC COMPARE Results: &SYSINFO and VB Script | Umesh Gautam, Jeff Roberts |
In the life cycle of a clinical analysis project, it's often necessary to do complete validation of datasets and outputs multiple times as datasets and outputs are created more than once for draft, dry run, data cuts, etc. During program development there's no substitute for solid program construction and close attention to the comparison results of double programming validation. However once robust production and validation programs are in place, the validation of programs and outputs against new data may be obtained much more efficiently with automated comparison and summary of the results. This paper will show how such a process may be implemented using SAS automatic variables and VBScript | proc compare, data cleaning, proc step | source | 5 |
| 126 | One Project, Two Teams: The Unblind Leading the Blind | Kristen Reece Harrington | In the pharmaceutical world, there are instances where multiple independent programming teams exist to ensure blinded treatments are maintained by the appropriate parties. For the purposes of this discussion, blinded corresponds to fake data and unblinded corresponds to actual data. Within these projects, blinded programmers use temporary fake data to create programs which produce both blinded and unblinded results. To ensure the blind is maintained and ethics are upheld, only the blinded programming team produces and modifies the programs. While robust programming is key, another major contributing factor for success is communication. This discussion will explore the process of initializing a project supported by blinded and unblinded teams, successful communication techniques when real data comes into play, and ways to effectively troubleshoot validation issues without providing unblinding information | data cleaning, macro variable, sdtm domain, cdisc | source | 5 |
| 127 | Overview and Application of the HCV Vertical Resistance Analysis Template | Yan Xie | FDA proposed an updated hepatitis C virus (HCV) vertical resistance draft template on March 2016, which is quite different from the existing guidance for submitting HCV resistance data. The new vertical template is not published yet and is still under review, but FDA encourages the sponsors to submit resistance data using this new template. Abbvie received this guidance directly from FDA. Compared to the previous horizontal template which contained at least five hundred variables, the new vertical template, with only about hundred variables, is more advanced because: 1. it is compatible with SDTM and ADaM standards, 2. it reduces number of variables by applying the streamlined and simplified vertical format, 3. it can hold the Next Generation Sequencing (NGS) data, and 4. it can hold multiple targets and HCV subtypes in one dataset | randomization, sample, adam dataset, flag variable, study day, proc freq, cdisc | source | 5 |
| 128 | Pilot Meta-Analysis of HPA Axis Suppression Studies on Topical Corticosteroids using ADaM Datasets derived from Legacy Data | Lillian Qiu, Hon-Sum Ko |
Topical corticosteroids are widely used in dermatologic therapy because of their anti -inflammatory and anti-pruritic effects. They are categorized into "potency" classes according to their effect on dermal erythema, which is based on vasoconstrictive activity. There is suggestion that such "potency" may be related to systemic effects such as the suppression of hypothalamic-pituitary-adrenal (HPA) axis. Six HPA axis suppression studies have been included for a pilot safety meta-analysis for topical corticosteroid products. These studies involved the use of topical corticosteroids across a spectrum of "potencies" in patients with psoriasis or atopic dermatitis. ACTH stimulation and plasma cortisol measurements were conducted before and after a period of use of the topical corticosteroid. To perform the pilot safety meta-analysis, ADaM ADLB datasets, derived via SAS 9.3 for the conversion of legacy data of these studies, were combined, also using SAS programming | confidence interval, sas programming, clinical trial, cdisc | source | 5 |
| 129 | Planning to Pool SDTM by Creating and Maintaining a Sponsor-Specific Controlled Terminology Database | Cori Kramer, Ragini Hari, Keith Shusterman |
When SDTM data are consistently standardized, data can be easily pooled across studies. To consistently standardize raw data to SDTM across st udies, there must be an assigned place for each collection field and each collection field must be in its assigned place. Following the SDTM Implementation Guide and applyin g Controlled Terminology (CT) as specified is sufficient to support fairly consistent SDTM mapping across studies. To enhance the consistency, sponsor guidelines must be established and maintained. Establishing, maintaining and enforcing sponsor-defined controlled terminology offers a simple, effect ive way to ensure that SDTM domains are consistently ma pped across studies. We will share an overview of the benefits of leveraging a sponsor-specific controlled terminology database using examples and will suggest how a sponsor-specific CT database can be efficiently developed and used | cdisc controlled terminology, controlled terminology, proc sql, sdtm domain, sdtm programming, excel, proc step, cdisc | source | 7 |
| 130 | Point-and-Click Programming Using SAS Enterprise Guide | Kirk Paul Lafler, Mira Shapiro, Ryan Paul Lafler |
SAS Enterprise Guide empowers organizations, programmers, business analysts, statisticians and end-users with all the capabilities that SAS has to offer. This presentation describes the built-in wizards to perform reporting and analytical tasks, access to multi-platform enterprise data sources, the delivery of data and results to a variety of mediums and outlets, data manipulation without the need to learn complex coding constructs, and support for data management and documentation requirements. Attendees see the graphical user interface (GUI) to access tab-delimited and EXCEL input files; subset and summarize data; join (or merge) two tables together; flexibly export results to HTML, PDF and EXCEL; and visually manage projects using flowcharts and diagrams |
excel, sas enterprise guide, dataset, proc means, data governance, delimited file, sas output, proc sql | source | 18 |
| 131 | Population PK/PD Analysis - SAS with R and NONMEM Make Customization Easy | Sharmeen Reza | Population pharmacokinetics/pharmacodynamics (pop-PK/PD) modeling and analysis are typically exploratory in nature. Different levels of customization are needed for modeling at various steps, where the core is driven by NONMEM software requiring that a structured file be passed onto it. Development and validation methods of such a NONMEM-ready data file rely heavily on the firmness of PK specs. Multiple tools, including SAS and R, are described in this paper for creating a data set. An interface - based solution reduces programming dependencies but has limitations as its backend logic is constructed on a single version of PK specs. Other parts of the analysis, pre-processing of data file and post- processing of NONMEM software output, are performed in SAS and/or R; the ch oice of tools is determined by availability and user competency | pd, data cleaning, imputation, population pk | source | 11 |
| 132 | Power of Interleaving and RETAIN Combination in Data Manipulation | Zongming Pan | DATA STEP with MERGE statement and BY statement is a popular tool in data manipulation. Two or more sorted datasets can also be combined into a new dataset by a SET statement (instead of MERGE statement) and an accompanying BY statement. This process is called interleaving. It has been suggested that interleaving a dataset with itself is a useful technique to finish some kinds of tasks (Schreier, H., 2003). The most popular one is the processing based on group summarization via a sum statement. This 'looking forward' or 'forward looking' character via sum statement can be applied in data manipulation. RETAIN statement which causes the values of new variables to be retained from one iteration of the DATA STEP to the next can amplify the 'looking forward' or 'forward looking' character. Incorporating a RETAIN statement into the interleaving process can reduce the use of PROC SORT to a single instance |
by group processing, in=, merge statement, set statement, retain, proc sort, proc means, proc step | source | 10 |
| 133 | Preparing Analysis Data Model (ADaM) Data Sets and Related Files for FDA Submission | Sandra Minjoe | This paper compiles information from documents produced by the U.S. Food and Drug Administration (FDA), the Clinical Data Interchange Standards Consortium (CDISC), and Computational Sciences Symposium (CSS) workgroups to identify what analysis data and other documentation is to be included in submissions and where it all needs to go. The paper not only describes requirements, but also includes recommendations for things that aren't so cut-and-dried. It applies to New Drug Application (NDA) submissions and the subset of Biologic License Application (BLA) submissions that are described in specific FDA binding guidance documents plus other related FDA documents | adam dataset, dataset, length statement, proc step, character variable, proc copy, data science, cdisc | source | 12 |
| 134 | PROC DOC III: Self-generating Codebooks Using SAS | Louise S. Hadden | This paper will demonstrate how to use good documentation practices and SAS to easily produce attractive, camera-ready data codebooks (and accompanying materials such as label statements, format assignment statements, etc.) Four primary steps in the codebook production process will be explored: use of SAS metadata to produce a master documentation spreadsheet for a file; review and modification of the master documentation spreadsheet; import and manipulation of the metadata in the master documentation spreadsheet to self-generate code to be included to generate a codebook; and use of the documentation metadata to self-generate other helpful code such as label statements. Full code for the example shown (using the SASHELP.HEART data base) will be provided upon request | proc step, proc export | source | 8 |
| 135 | Programming Efficiency in the Creation of ADaM BDS Datasets | Ellen (Zihui) Lin | The ADaM Basic Data Structure (BDS) has become one of the most prominent and widely implemented dataset structures in the industry since the CDISC ADaM Implementation Guide V1.0 was published in 2009. The strictly vertical data design of the BDS brings two common challenges to statistical programming: (1) BDS datasets often quickly grow very large, especially in larger clinical trials. (2) Metadata for BDS datasets become more difficult to develop and understand due to the use of value- level metadata (VLM) for describing variables by PARAM/CD and the use of multiple BASETYPEs and other derived data records within the same dataset. This paper will describe the programming challenges specific to BDS and illustrate with examples how to achieve better programming efficiency and quality | lookup, raw data, imputation, adam dataset, data wrangling, clinical trial, cdisc | source | 11 |
| 136 | Programming LYRIC Response in Immunomodulatory Therapy Trials | Yang Wang | The LYmphoma Response to Immunomodulatory therapy Criteria (LYRIC) is a newly proposed tumor response criteria for immunomodulatory therapies such as immune checkpoint inhibitors. The current Lugano criteria work well for traditional chemotherapeutic regimens and chemoimmunotherapeutic regimens. It includes complete remission (CR), partial remission (PR), complete remission unconfirmed (CRu), stable disease (SD), relapsed disease (RD) and progressive disease (PD). However, Lymphoma therapy with immune mechanisms may cause tumor flares. These tumor flares can be associated with clinical and imaging findings suggesting progressive disease (PD). Since tumor flares generally occur during the first two or three weeks of treatment, without a more flexible interpretation, it is possible that some patients can be prematurely removed from a potentially benefi cial treatment, thus leading to underestimation of the magnitude of the clinical benefit of the testing agent | pd, complete response | source | 5 |
| 137 | Put the "K" in Your KAB: Know How to Efficiently Program with Knowledge, Attitudes, and Behavior Survey Data | Cara Lacson, Jasmeen Hirachan |
Data from Knowledge, Attitudes, and Behavior surveys can present challenges, but these can be alleviated by a solid, efficient programming strategy. Producing KAB tables, listings, and figures can be very tedious to program, from the number of SAS data set variables that the survey data requires, to skip logic within the survey, and the volume and detail of question and respon se text that must be reported. With lots of typing involved, this is certainly something you want to do only once within your project, and in one location, to reduce the chance for error. This paper presents the basic concepts of a KAB survey from a programming perspective: what to expect in your data, recommendations for your analysis data set structure, and how to handle permitted skip questions. This paper also explores a solution to storing long question text, responses, and correct answers in one central location for use throughout all of your SAS programs in a KAB study | dataset, confidence interval, proc step, numeric variable, format statement, proc format, proc freq, sample, macro loop | source | 16 |
| 138 | Quality Check your CDISC Data Submission Folder Before It Is Too Late! | Bhavin Busa | The standardized clinical study datasets will be required in submissions for clinical and non-clinical studies that start on or after December 17, 2016. FDA has added a technical rejection criteria to the existing eCTD validation criteria to enforce the deadlines. The FDA may refuse to file for NDAs, an electronic submission that does not have study data in conformance to the required standards specified in the FDA Data Standards Catalog. This means that all studies going forward must utilize CDISC SDTM/ADaM standards and should consist of associated submission documents (aCRF.pdf, define.xml/define.pdf, cSDRG.pdf, ADRG.pdf) per the Study Data Technical Conformance Guide (TCG). The submission of these study datasets and documents should be organized into a specific file directory structure per the eCTD requirements. As Sponsor is preparing for their NDA submission, it is critical for them to verify the content and validity of the dataset folder per the FDA submission requirements, i.e | data cleaning, clinical trial, data standard, trial summary, adam dataset, cdisc | source | 9 |
| 139 | Real Time Clinical Trial Oversight with SAS | Ashok Gunuganti | A clinical trial is an expensive and complex undertaking with multiple teams working together to accomplish study startup, conduct, data collection, monitoring and data cleaning. It is critical that the study is implemented as specified in the protocol and the data is clean and of good quality for a successful study. As most EDC system s are setup to export data as SAS datasets , a clinical p rogramming team can easily leverage these datasets to provide the study team with metrics and tools to help focus and modulate monitoring and data cleaning efforts in real time | edc, data validation rules, sample, excel, output delivery system, macro definition, proc step, adverse events, dataset, data cleaning | source | 7 |
| 140 | REDCAP Ins and Outs | Leanne Goldstein | REDCap (Research Electronic Data Capture) is making waves around the world as the latest way to capture data at academic institutions. Its greatest benefit is that it can provide multi-user, web based created databases, with audit trails and reporting. It is an improvement to using MS Access databases and MS EXCEL, which investigators have historically used to capture data in academic settings. Application Programming Interfaces (APIs) have been developed for programmers to get data in and out of REDCAPs, but the SAS community has been somewhat left behind. This paper discusses an introduction to REDCAP for the SAS programmer and discusses methods of getting data into and out of REDCAP databases |
proc iml, api, dataset, proc step | source | 10 |
| 141 | RELREC - SDTM Programmer-s Bermuda Triangle | Charumathy Sreeraman | The CDISC Study Data Tabulation Model (SDTM) provides framework for organizing and converting clinical trial data into standard formats. This supports easy interpretation and maintaining consistency across trials. Sometimes, it becomes vital to establish relationship between records/datasets in SDTM to facilitate linking process at the time of conversion. Logic of relationship is either identified by profile/outliers in data (ex: PC and PP) or by identifying the data link between domains to examine associated information from individual domains collectively (ex: TU, TR and RS). Related Records (RELREC) - Special Purpose Relationship Domain - can capture these explicit and inexplicit relationship(s) to aid further in -depth exploration of data collected durin g trials. Often, perceived as a challenging zone, this beauty is yet to be explored to its maximum potential | clinical trial, concomitant medications, sdtm domain, clinical data, sample, pk parameter, cdisc | source | 12 |
| 142 | Remove the Error: Variable length is too long for actual data | Eric Larson | Have you ever seen the error from Pinnacle 21 output 'Variable length is too long for actual data'? I have created a macro that will go through the character variables and identify the minimum length to hold the longest value of the data, and apply that length to the dataset just before cr eating a SAS transport file from it | character variable, length statement, proc step, create table, macro variable, proc sql, macro definition | source | 9 |
| 143 | Removing the Mask of Average Treatment Effects in Chronic Lyme Disease Research Using Big Data and Sub-Group Analysis | Mira Shapiro, Lorraine Johnson |
There is considerable controversy in the medical community surrounding chronic Lyme disease (CLD). CLD sufferers have persistent Lyme symptoms resulting from being untreated, under-treated or, having a lack of response to their antibiotic treatment protocol. Some of the past Lyme disease studies that reported on average treatment effects were unable to identify treatment successes. Using patient-reported outcome data collected by LymeDisease.org via their online registry, MyLymeData, we will show that using sub-group analysis techniques can unmask valuable information about treatment effectiveness | treatment effect, sample, sample size | source | 9 |
| 144 | Reporting Non-Printable and Special Characters for Review in Excel | Abhinav Srivastva | Data in clinical trials can be transmitted in various formats such as EXCEL, CSV, tab -delimited, ASCII files or via any Electronic Data Capture (EDC) tool. A potential problem arises when data has embedded special characters or even non-printable characters which affects all downstream analysis and reporting; in addition to being non -compliant with CDISC standards. The paper will briefly present a discussion on these characters and how to identify them but the emphasis will be on creating an EXCEL report which summarizes these in a way that can be easily reviewed and appropriate action can be planned. Creating a summary report as this can be used by the programmers as initial steps in data cleaning activities with each data transfer. Some of the features of the EXCEL report include traffic -lighting effects, hyperlinks and tool-tips for providing additional information |
excel, macro definition, cdisc | source | 11 |
| 145 | Roadmap for Managing Multiple CRO Vendors | Veena Nataraj, Karin LaPann |
When working with CROs, the relationship established between the two organizations is critical for successful outcomes. Increasingly, sponsors and CROs exist in the same ecosystem, looking to share standards, processes, and people. Many times, a sponsor works with multiple CROs, part nering with some while having a study-to-study relationship with others. Based on the type of the relationship, a CRO can provide certain functions to the sponsor. As with any relationship, investing time to understand and establish ground rules will help in the building of a healthy relationship. This paper discusses the relationship of a sponsor to a CRO as a partner with high-level information on how the information flows back and forth. Also discussed are implementations of CDISC standards using tools a vailable such as published standards, sponsor interpretation guides and document templates. It also notes advantages and pain points, and how these can be managed to ensure a successful partnership | clinical trial, define-xml, cdisc | source | 7 |
| 146 | Same statistical method different results? Don-t panic the reason might be obvious. | Iryna Kotenko | Modern statisticians and statistical programmers are able to use a variety of analytics tools to perform a statistical analysis. Performing quality control checks or verification analysis using different statistical packages for the same statistical method one may receive unexpectedly unequal results. So the question comes: what is wrong with the calculations? The answer might disconcert: the calculations are valid and the root of discrepancies is the difference in computational methods and default settings that are implemented in each statistical package. The aim of this article is to bring an awareness to the auditory about known inconsistency in computational methods in commonly used analytics tools: SAS, Python, R, and SPSS | normality test, sample, chi-square test, probability distribution, anova, proc step, proc glm, proc freq | source | 8 |
| 147 | SAS and ISO8601: A practical approach | Derek P. Morgan | The ISO 8601 standard for dates and times has long been adopted by regulatory agencies around the world for clinical data. While there are many homemade solutions for working in this standard, SAS has many built-in solutions, from formats and informats that even take care of time zone specification, to the IS8601_CONVERT routine, which painlessly handles durations and intervals. These built-in capabilities, available in SAS 9.2 and above, will streamline your code and improve efficiency and accuracy. This paper also assumes the use of SAS version 9.2 and above | datetime, sysdate9, imputation, character variable, date variable | source | 10 |
| 148 | SAS and R Playing Nice Together | David Edwards, Bella Feng, Brian Schultheiss |
Like many other statistical programming departments in the pharma/biotech industry, Amgen primarily uses SAS for data manipulation, data analysis and reporting. In recent years, other statistical software, such as R, has matured significantly and in some situations, provides a compelling alternative to SAS. To provide statistical programmers with a seamless integration between SAS and R, Amgen has considered several different approaches. However, because at Amgen our SAS Grid and Qualified R environments are hosted on different physical servers, none of the existing techniques offered an ideal solution. Therefore, Amgen decided to leverage the Microsoft DeployR package to embed the results of R routines within the SAS programs we use to analyze and report Clinical Trial Data. This paper will provide an overview of how Amgen implemented their integration between SAS and R and touch briefly on other use cases for DeployR | data science, web service, macro definition, statistical programmer | source | 3 |
| 149 | SAS Application to Automate a Comprehensive Review of DEFINE and All of its Components | Walter H. Hufford Jr., Vincent Guo, Mijun Hu |
The DEFINE is a large electronic document comprised of many different but interrelated components such as annotated Case Report Form, Data Reviewer's Guide and metadata. Further complicating the situation is that most electronic submissions contain several studies and an SCS and an SCE, each of which requires their own DEFINE. Reviewing the DEFINE to ensure consistency, accuracy and completeness within a single DEFINE as well as across DEFINEs is both time consuming and resource intensive (and often mind-numbing if you have a large submission with many DEFINEs to review). Automating review of the DEFINE can be achieved with a few simple, easy to develop, SAS macros. The result is a much quicker review requiring substantially less resources. In addition, findings are noted in a standardized manner allowing for quicker issue resolution and tracking | crf, controlled terminology, excel, proc step, proc import, cdisc | source | 8 |
| 150 | SAS Debugging 101 | Kirk Paul Lafler | SAS users are almost always surprised to discover their programs contain bugs. In fact, when asked users will emphatically stand by their programs and logic by saying they are bug free. But, the vast number of experiences along with the realities of writing code says otherwise. Bugs in software can appear anywhere; whether accidentally built into the software by developers, or introduced by programmers when writing code. No matter where the origins of bugs occur, the one thing that all SAS users know is that debugging SAS program errors and warnings can be a daunting, and humbling, task. This presentation explores the world of SAS bugs, providing essential information about the types of bugs, how bugs are created, the symptoms of bugs, and how to locate bugs. Attendees learn how to apply effective techniques to better understand, identify, and repair bugs and enable program code to work as intended | options statement, sas log, proc step, error message | source | 13 |
| 151 | SAS Macro for Derivation of Best Overall Response per RECIST v. 1.1 | Bob Zhong, Jiangfan Li, Hong Xie, Peter De Porre, Kenneth Maahs, Kyounghwa Bae |
Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST 1.1) has been quickly adopted since its publication in 2009 as an efficacy assessment standard in oncology clinical trials in solid tumors. However, the rules for determining best overall response (BOR) need to take into considerations of practical situations and certain clarifications in details are needed to handle real-world data. We addressed some ambiguities in RECIST 1.1 and provided detailed handlings required to develop a SAS macro for an accurate derivation of BOR. BOR is derived from time-point overall responses of CR, PR, SD, PD, NE, and Unknown. To derive BOR, responses that do not affect derivation of BOR are taken out first. After this step of data manipulation, only the first three remaining time-point overall responses are needed to derive BOR. Complicated cases such as PR-SD-PR and unconfirmed CR or PR are calculated in more detail | interim analysis, procedures, macro definition, standard deviation, best response, final analysis, clinical trial, complete response, sample, randomization | source | 9 |
| 152 | SAS Programmer-s Guide to Life on the SAS Grid | Eric C. Brinsfield | With the goal of utilizing computing power and support staff more efficiently, many organizations are moving all or large portions of their SAS computing to a SAS Grid platform using SAS Grid Manager. This often forces many SAS programmers to move from Windows to Linux, from local computing to server computing, and from personal resources to shared resources. This shift can be accomplished if you make slight changes in behavior, practice and expectation. This presentation offers many suggestions for not only adapting to the SAS Grid but taking advantage of the parallel processing for longer running programs. Sample programs are provided to demonstrate best practices for developing programs on the SAS Grid. Program optimization and program performance analysis techniques are discussed in detail | sas grid, sas enterprise guide, sas studio, sas log, parallel processing, batch job, sysrc, proc step, cpu time, proc printto, sas programmer, elapsed time | source | 18 |
| 153 | SAS Studio: We Program | Jim Box | Have you investigated SAS Studio? From the 1980s into the 2010s I used SAS Display Manager (PC SAS front end) for all of my clinical table, listing, figure and database program development. I became accustomed to the program editor, log window, output window and being able to view my working and saved datasets via this programming IDE. I resisted new coding editors through the years UNTIL SAS Studio came to fruition. SAS Studio is a web-based application that accesses your SAS environment - cloud, local server(s), grid or PC. With the environment, you can access your data libraries, files and existing programs … and write new programs! Additionally, SAS Studio contains pre-defined tasks that generate code for you. Have a specific set of clinical programming code you always use? Snippets! Want to define a personal or global task for AE table summarization? Define it within SAS Studio! | sas studio, error message | source | 9 |
| 154 | SAS vs Tableau: Creating Adverse Event/ Concomitant Medication Time line plot | Liling Wei, Kathy Chen |
The Adverse Event (AE) Concomitant Medication (CM) time line plot displays the events and medications for subject by severity and time. In this paper, we use data visualization software Tableau Desktop to build the graph using drag-and-drop steps to analyze the time-based data. The SGPLOT procedure HIGHLOW plot from SAS9.4 also makes it easy to generate the combined AE and CM graph. This paper compares the two different approaches |
line chart, adverse events, proc sgplot, concomitant medications | source | 6 |
| 155 | Schoveing Series 3: Living by Knowing When to STOP | Priscilla Gathoni | Would changing the way you prioritize your life help ensure a successful and fruitful life? Would you like to renew the way you live, think, feel, and will? Is the senselessness of demanding more and exhausting yourself in the pursuit of what keeps you trapped in a vicious cycle of "striving and never arriving" keeping you awake? Do you know when to stop and determine what is important and valuable to your life? Do you have daily habits that keep you in a comfort zone that you would like to break? Would you like to stop working and start living? Does parenting make you nervous and question loving? How many of you procrastinate and never have time to start focusing? Are you so consumed in your mind that you take over all conversations and never listen to what the other person is saying? Do you live your life thinking that fate is all you have and nothing else can stop your circumstances from continuing? Is fear guiding you? Are you apprehensive of what will happen tomorrow and have not | source | 13 | |
| 156 | SDTM - Just a Walk in the (Theme) Park, Exploring SDTM in the Most Magical Place on Earth | Christine McNichol | By now, most in the industry know the basic concepts of SDTM, now let us infuse some magic and take a virtual vacation to look at SDTM from a perspective inspired by one of the happiest places on earth. Some of the basic as well as more complicated pieces of SDTM can be shown in examples of things encountered during a day at a theme park in Central Florida. With a little imagination, we can equate our virtual vacation to a clinical trial. Come along as our vacationers (subjects) take part in our vacation (study) where they will experience various rides and attractions (treatments). We will take a look at how these experiences could be mapped to SDTM, from describing the plan of the virtual vacation in the TDM through capturing the events of the day in SDTM domains. Within this trip, we will look at some more common but important domain examples such as DM, AE, VS and more | clinical trial, sdtm domain, adverse events, cdisc | source | 9 |
| 157 | SDTM Cartography - Learn To Create SDTM Mapping Specifications | Donna Sattler, Mike Lozano |
You may have a very good idea of what you need to deliver your clinical trial to regulatory agencies. However, the journey you take to collect, clean, combine, and migrate from multiple data sources can be overwhelming. This paper will explore the Data Integration or Standardization mapping process. It will demonstrate that knowing your destination is harder than it seems when multiple paths are presented. Do you take the path less traveled because you think it better suits your travel plans or should you stick with what the tried and true path proves? | clinical trial, clinical data | source | 7 |
| 158 | Show Me The Job! | Richelle Serrano, Kevin Lee |
The movie , Jerry Maguire , spawned several popular quotations, including "Show Me the Money!" shouted repeatedly in a phone exchange between Rod Tidwell and Jerry Maguire (played by Tom Cruise). It took Jerry Maguire almost a year to find his client (the job seeker) the best job without compromises. Find ing the best job is not a short and effortless process. The paper will speak to the challenges faced by Biometrics job seekers today, and show how landing the best job, can be far less painful, and more effective, by partnering with a technical recruiter. The paper begins with what 'not' to fear in a technical resume. Statistical programming hiring manager s in pharmaceutical organizations are looking for individuals with well-defined technical capabilities and a solid understanding of the clinical trial process. The paper will show what to consider when preparing your resume and what you can gain from a recruiter that understands your background and skills | advanced sas, clinical trial | source | 4 |
| 159 | Simplifying Your %DO Loop with CALL EXECUTE | Arthur X. Li | One often uses an iterative %DO loop to execute a section of a macro repetitively. An alternative method is to utilize the implicit loop in the DATA STEP with the EXECUTE routine to generate a series of macro calls. One of the advantages in the latter approach is eliminating the needs of using indirect referencing. To better understand the use of the CALL EXECUTE, it is essential for programmers to understand the mechanism and the timing of macro processing to avoid programming errors. These technical issues will be discussed in detail in this paper |
call execute, macro loop, macro call, do loop, macro facility | source | 5 |
| 160 | Single File Deliverables: Next Steps | Bill Coar | Creating Tables, Listings, and Figures (TLFs) continues to be part of daily tasks in a statistical programming group in the Pharmaceutical Industry. Trends continue to move toward electronic review and distribution, and the end users of the TLFs are often requesting a single file rather than sending each output individually. In the recent years, the use of item stores has been introduced as a viable option to create a single file deliverable. In 2016, a hands on training was offered to demonstrate their usefulness in our industry setting. During this workshop (Combining TLFs into a Single File Deliverable, PharmaSUG 2016) attendees gained experience with creating, replaying, and restructuring item stores to obtain a single file containing a set of TLFs. Single File Deliverables: Next Steps will build upon what was presented in 2016 working towards more complex implementation and automation | proc step, proc report, sample, by group processing, rename statement, macro variable, where statement, adverse events, dataset, ods pdf, pharmaceutical industry, macro definition | source | 19 |
| 161 | Special Symbols in Graphs: Multiple Solutions | Abhinav Srivastva | It is not uncommon in Graphs to include special symbols at various places like axes, legends, titles and footnotes, and practically anywhere in the plot area. The paper discusses multiple ways how special symbols can be inserted as applicable in SAS/GRAPH, Graph Annotations, ODS Graphics - SG Procedures, SG Annotations and Graph Template Language (GTL). There will be some examples presented which leverage the power of Formats to put these into action. The techniques will vary depending on the version of SAS and the type of procedure (PROC) used |
proc step, csr | source | 12 |
| 162 | Sponsor Oversight of CROs Data Management and Biostatistical Abilities | Lois Lynn | Sponsors of phase I, II and III Clinical Trials who partner with full-service CROs for the management of a clinical trial are responsible for data quality and integrity; choosing a CRO is a critical decision on the way to FDA submission. In my experience, full service CROs tend to be chosen based on their competence in their clinical operations department that recruits study sites and cares for patients. The mission critical oversight and connection with the biostatistics and data management departments tends to be an afterthought, yet these teams provide the study documents and data submission package required for pharmaceutical compound submission to the FDA. This paper highlights key considerations for data management and biostatistical services from a full service CRO starting with the bid defense meeting through study conduct and final deliverables | clinical trial, data validation rules, mock shell, adverse events | source | 7 |
| 163 | Stakeholder Management: How to be an effective lead SAS programmer | Aakar Shah | As a beginner lead programmer, the traditional focus is to work with given programming team, study statistician, study data manager to deliver outputs per given timelines. Eventually the experienced lead would engage in timeline discussion, resource discussion, and CRO management. This traditional approach has its limitation in terms of perception of success, burned bridges with other functional groups, lack of communication, and lack of satisfaction in some areas etc. This paper takes holistic view of stakeholder management and how to use available processes, tools and techniques to ensure whole success - tangible and intangible. We will learn about creating Stakeholder Management Plan which will include following four processes, their inputs, tools and techniques and outputs. 1. Identify Stakeholders 2. Plan Stakeholder Management 3. Manage Stakeholder Engagement 4. Control Stakeholder Engagement This paper is intended for a lead SAS programmer | source | 8 | |
| 164 | Standardized, Customized or Both? Defining and Implementing (MedDRA) Queries in ADaM Data Sets | Richann J. Watson, Karl Miller |
Investigation of drug safety issues for clinical development will consistently revolve around the experience and impact of important medical occurrences throughout the conduct of a clinical trial. As a first step in the data analysis process, Standardized MedDRA Queries (SMQs), a unique feature of MedDRA, provide a consistent and efficient structure to support safety analysis, reporting, and also address important topics for regulatory and industry users. A variance in working with SMQs is the ability to limit the scope for the analysis need (e.g., "Broad" or "Narrow") but there is also the ability outside of the specific SMQs in allowing the ability to develop Customized Queries (CQs). With the introduction of the ADaM Occurrence Data Structure (OCCDS) standard structure, the incorporation of these SMQs, along with potential CQs, solidified the need for consistent implementation, not only across studies, but across drug compounds and even within a company itself | adverse events, lookup, clinical trial, sensitivity analysis, sample, data science, cdisc | source | 7 |
| 165 | Statistical Review and Data Standards: It's Gettin' Better All The Time | Steven A. Wilson | source | 30 | ||
| 166 | Statistician's secret weapon: 20 ways of detecting raw data issues | Lixiang Larry Liu | Unclean clinical raw data is always statistician and statistical programmer's nightmare for all the downstream SDTM, ADaM and TFLs development work. Raw data issues could mess up the programming logic, create OpenCDISC reject, error, warning messages, and worst of all, if incorrect data is analyzed, study team could draw wrong or inaccurate conclusions regarding drug's safety and efficacy, which could put patients' safety in jeopardy and have significant impact on company's financial status. This paper will review 20 effective ways of detecting raw data issues. Since they are applied to the drug dispense, labs, and safety related CRF data (Including adverse event, medical history, concomitant therapy, drug exposure etc.), which are common for all clinical trials, these methods and their associated SAS programs could be easily used for clinical trial studies across different therapeutic areas | data governance, clinical trial, database lock, raw data, adverse events, exposure, visit, protocol deviations, statistical programmer, create table, date variable, data validation rules, medical history, randomization, proc step, concomitant medications, cdisc | source | 8 |
| 167 | Stop waiting - Get notification email at the end of SAS code execution using SAS EMAIL Engine | Nirav Darji | DATA STEP and statements like FILENAME, FILE and PUT can be used for automatically sending custom e-mails. This utility can help programmers in cases where SAS codes take lot of time to complete the execution and we kept waiting for completion. It can also help when the teams are working in different time zones/shifts. The team working in another time zone or next shift can get notified once the execution gets completed. We will see how to send email with or without attachment (HTML, EXCEL, SAS files etc.) or personalized email and how to send email when it fulfills certain conditions |
put statement, filename statement, file statement, options statement | source | 11 |
| 168 | Strategies and decisions for marketing yourself as an independent contractor | Kathy Bradrick | Independent contractors have the responsibility of marketing themselves, in other words doing their own business development. This session will explore how to market yourself, what project aspects to consider, how to make yourself appealing to recruiters and employers, and things to think about as you consider going back to permanent employment | source | 3 | |
| 169 | Supporting the CDISC Validation Life-Cycle with Microsoft Excel VBA | Eric Crockett | Clinical research is increasingly based on standardized Clinical Data Interchange Standards Consortium (CDISC) data. Evaluating whether the data conforms to the applicable CDISC standards is required and is often an iterative process done over the course of a study. Documenting the explanations for issues that cannot be resolved and tracking the trends in conformance findings over the life -cycle of a study can easily turn into a burdensome manual process repeated with each Pinnacle 21 report. Utilizing M icrosoft EXCEL Visual Basic for Applications (VBA) macro code that will be provided and discussed, successive validation reports can be compared and explanatory comments can be migrated. Leveraging this automated application can dramatically reduce the time spent tracking, evaluating and documenting conformance findings |
excel, data cleaning, cdisc | source | 7 |
| 170 | Survival 101 - Just Learning to Survive | Leanne Goldstein, Rebecca Ottesen |
Analysis of time to event data is common in biostatistics and epidemiology but can be extended to a variety of settings such as engineering, economics and even sociology. While the statistical methodology behind time to event analysis can be quite complex and difficult to understand, the basic survival analysis is fairly easy to conduct and interpret. This workshop is designed to provide an introduction to time to event analyses, survival analysis and assumptions, appropriate graphics, building multivariable models, and dealing with time dependent covariates. The emphasis will be on applied survival analysis for beginners in the health sciences setting | kaplan-meier plot, proc step, hazard ratio, proc phreg, survival analysis, proportional hazards assumption, binary variable, log-rank test, rank test, nominal variable, interval variable, stepwise selection, time-dependent covariate, maximum likelihood estimation, confidence interval, proc calis, likelihood function, regression analysis | source | 20 |
| 171 | Takeaways from Integrating Studies Conducted by Bristol-Myers Squibb (BMS) and ONO | Yan Wang | In addition to merging and acquisition happening in the pharmaceutical industry, more international companies have been connected by working together to seek NDA or sNDA approvals. Currently, both BMS and Japan ONO have been independently working collaboratively on an oncology drug for cancer treatments, and the two companies have formed a strategic partnership that includes co-development, co-commercialization and co-promotion of multiple immunotherapies for patients with cancer. As a programmer, I have been heavily involved preparing the submissions of the integrated study results to FDA and Europe agencies. Throughout this data integration submission process, I have developed a deep understanding of the differences on presenting SDTM/AdAM datasets between the two organizations, and I am very pleased to share our approach on harmonizing the differences, and our lessons learned and suggestions on preparing data integration from different sponsors | annotated crf, database lock, cdisc | source | 2 |
| 172 | The %NEWSURV Family of Macros: An Update on the Survival Plotting Macro %NEWSURV and an Introduction to Expansion Macros | Jeffrey P. Meyers | Time-to-event endpoints such as overall survival are commonly used as outcomes in oncology clinical trials, and one of the best graphical displays of these outcomes is the Kaplan-Meier curve. The macro NEWSURV, which has been presented at SAS conferences in 2014 and 2015, has become popular as a powerful tool for creating highly customizable publication quality Kaplan-Meier plots. The positive feedback for the NEWSURV macro has led to the program being updated with additional features and improvements, and has led to additional macros being created for the situations that NEWSURV does not cover. A pair of macros, NEWSURV_ADJ_INVWTS and NEWSURV_ADJ_DIRECT, create adjusted Kaplan-Meier curves using two specific methods: inverse weights and direct adjustment. The macro NEWSURV_DATA allows the user to provide their own dataset with pre-calculated survival function estimates that the program will then use to create a high quality plot in the NEWSURV style | class variable, survival function, hazard ratio, confidence interval, macro call, proc phreg, logistic distribution, in=, macro parameter, numeric variable, proc logistic, data step, sample, clinical trial, competing risks, logistic regression, time variable, kaplan-meier plot | source | 21 |
| 173 | The basics of Graphics Template Language and output by PROC DOCUMENT | Fangping Chen | ODS GRAPH TEMPLATE LANGUAGE (GTL) is a powerful language in SAS 9.2. PROC TEMPLATE procedure in GTL can create customized graphs independently from DATA STEP. Rendered sophisticated figures to ODS DOCUMENT destination, user can rearrange the individual components of the output and modify the output report at the very last step without touching DATA steps. This paper will present the basic PROC TEMPLATE procedure and ODS DOCUMENT destination with PROC DOCUMENT procedure step by step for those who touch those procedures for the first time |
proc step, proc template | source | 14 |
| 174 | The Benefits of Traceability Beyond Just From SDTM to ADaM in CDISC Standards | Maggie Ci Jiang | Since FDA released the Analysis Data Model Implementation Guide (ADaMIG)[1] for public review in 2014, traceability in CDISC ADaM data is well known for its characteristics between SDTM and ADaM. However, in CDISC ADaM development, the ability of traceability is more than just the connection between SDTM and ADaM. When it comes to a more complex analysis, an ADaM data value is not necessarily immediately related to a SDTM source data variable, some or more derivation procedures may be required to achieve the purpose, such as the derivation of an analysis-ready variable, parameter or record, to do it in the ADaM data or to program it in the analytical report instead? It appears that programmers can have an option. The decision often ends up with the one that the programmer favors over the other | adam dataset, sdtm domain, adverse events, cdisc | source | 8 |
| 175 | The CDISC SDTM Exposure Domains (EX & EC) Demystified. How EC Helps You Produce A Better (more compliant) EX. | Thomas Guinter | The EC (Exposure as Collected) domain was introduced in the CDISC SDTMIG (Study Data Tabulation Model Implementation Guide for Human Clinical Trials) version 3.2 as a means to h elp sponsors produce a more compliant and usable EX (Exposure) domain. It's a new concept in the SDTM as it establishes a new rule for related/paired domains, and/or breaks the established principle that a single intervention or event should have a single intervention or event record in the SDTM. The EC domain can loosely be considered part of the audit trail for the EX domain, which is recognized as at least a partially derived domain. This paper reviews some of the rationale for the EC/EX combination of domains and provides recommendations on how sponsors should leverage the EC domain to produce a better, more usable EX domain | audit trail, dose, cdisc | source | 9 |
| 176 | The CDISC Trial Design Model (TDM), the EPOCH variable, and the Treatment Emergent Flag: How to Leverage these to Improve Review | Thomas Guinter | Many of us view the SDTM TDM (Trial Design Model), addition of the EPOCH variable to General - Observation-Class domains and potentially adding the AETRTEM Supplemental Qualifier as eSubmission overhead. Essentially something we must do to check-off that our eSubmission is compliant. We create them because CDISC and/or regulatory guidance requests them, and we put some time into designing their values, but unfortunately we don't look at them from a reviewers/consumers perspective and design the values to facilitate the review. This paper reviews some of the rationale for deciding the appropriate granularity of the TDM, EPOCH and AETRTEM to provide reviewers/consumers with information that should facilitate the review | trial elements, sample, cdisc | source | 4 |
| 177 | The Cross Border Program - Strengthening the Sponsor-Partner Offshore Experience | Kenneth Bauer, Girish Havildar |
Pharmaceutical statistical programming outsourcing is a comparatively late entrant to the India offshore market. Merck Statistical Programming initiated the outsourcing of limited statistical programming deliverables in 2007, partnering with small companies in India and China. Lessons learned during this early phase allowed us to development a more effective model as we expanded scope in subsequent years with a new partner. One of the programs we established in 2015 was the Cross Border Program (CBP) where partner staff visit Merck US sites for a period of time. Objectives of this program include keeping intact the strong India team we have invested in, providing team members with a sense of a global team concept, assisting the partner in managing attrition, and providing growth opportunities for partner staff. This is a joint commitment program between Merck and our Partner, including budget sharing and onsite logistics support | clinical programming, pharmaceutical industry | source | 5 |
| 178 | The Do-s/Don-ts, An SDTM Validation Perspective. The should/shouldn-t when explaining issues in the Study Data Reviewers Guide | Thomas Guinter | Sponsors collecting clinical trial data, and subsequently submitting that data to regulators, are expected to ensure: 1. The data is of sufficient quality and completeness to support evaluation of the clinical trial results relative to the study objectives, 2. The data format is compliant with the regulatory and industry standards accepted for eSubmission. We typically consider both points together, often describing the combination as validation. But this really overloads the term validation, we should think of it separately as validation and (standards) compliance checking, and consider other issues such as perspective. Different individual roles using and/or preparing the data have very different perspectives on what the validation and compliance should provide. This paper reviews some of the common issues sponsors face in the validation and compliance checking activities when preparing data for eSubmission to regulators | data cleaning, data validation rules, clinical trial, clinical data, cdisc | source | 8 |
| 179 | The Elusive Data Scientist: Real-World Analytic Competencies | Gregory S. Barnes Nelson, Monica Horvath |
You've all seen the job posting which looks more like an advertisement for the ever-elusive unicorn. They begin by outlining the required skills that include a mixture of tools, technologies and masterful "things that you should be able to do." Unfortunately, many such postings begin with restrictions to those with advanced degrees in math, science, statistics, or computer science and experience in your specific industry. They must be able to perform predictive modeling, natural language processing and, for good measure, candidates should only apply if they know artificial intelligence, cognitive computing, and machine learning. The candidate should be proficient in SAS, R, Python, Hadoop, ETL, real-time, in-cloud, in-memory, in-database and must be a master storyteller. I know of no-one who would be able to fit that description and still be able to hold a normal conversation with another human | machine learning, artificial intelligence | source | 14 |
| 180 | The Proc Transpose Cookbook | Douglas Zirbel | PROC TRANSPOSE rearranges columns and rows of SAS datasets, but its documentation and behavior can be difficult to comprehend. For common input files, this paper will show a variety of desired output files, plus code and explanations |
proc step, proc transpose, dataset, by group processing | source | 12 |
| 181 | The REPORT Procedure and ODS Destination for Microsoft Excel: The Smarter, Faster Way to Create First-Rate Excel Reports | Jane Eslinger | Does your job require you to create reports in Microsoft EXCEL on a quarterly, monthly, or even weekly basis? Are you creating all or part of these reports by hand, referencing another sheet containing rows and rows of data? If so, stop! There is a better way! The new ODS destination for EXCEL enables you to create native EXCEL files (XLSX) directly from SAS. Now you can include just the data you need, create great-looking tabular output, and do it all in a fraction of the time! This paper shows you how to use PROC REPORT to create polished tables that contain formulas, colored cells, and other customized formatting. Also presented in the paper are the destination options used to create various workbook structures, such as multiple tables per worksheet. Using these techniques to automate the creation of your EXCEL reports will save you hours of time and frustration, enabling you to pursue other endeavors |
excel, proc report, proc step, character variable, numeric variable | source | 20 |
| 182 | The Road Not Often Taken | Janet E. Stuelpner | Is there a simple career path to working in the life science industry? Do you need to study programming in college. Do you need to be a statistics major? What kind of skills can I learn while still at the university so that I can work in the life sciences? If I major in political science, can I still work in pharma or biotech? What if I am a history major, will that work? As with anything that you learn while you are in school, it is not always the content that will advance you to your final career. In this paper, I will discuss my path to having a successful career as a programmer. You will see how the twists and turns of my life helped me to get where I wanted to go. My story is not unique or unusual. I will talk about how an unusual or unique path can bring you success, no matter what your background may be | life sciences, banking | source | 2 |
| 183 | The Road Not Taken: SAS for Pharmacometrics | Vishak Subramoney, Sree Harsha Sreerama Reddy |
This paper aims to introduce SAS specialists from diverse backgrounds to the exciti ng new world of Pharmacometrics and explores the limitless possibilities in this cutting edge field. Pharmacometrics is the science of using math ematical models to integrate knowledge of biology, pharmacology and physiology to quantify the relationship between exposure and response. Model Based Drug Development (MBDD) holds the key to personalized healthcare and is the clear future of Drug Research and Development. As you can imagine, data is the crux of any scientific endeavor. Preparing high quality datasets for Pharmacokinetic and Pharmacodynamic mo deling is the primary remit of a PK Programmer. Other responsibilities include using innovative ways of data exploration (e.g. graphical analysis), ensuring the smooth transition of knowledge from SDTM/ADAM datasets to a modeling ready dataset. As a SAS programmer, Pharmacometrics is not the first thing that comes to our minds as a career choice | clinical research, adam dataset, cdisc | source | 4 |
| 184 | The Untapped Potential of the Protocol Representation Model | Frank C. DiIorio, Jeffrey M. Abolafia |
Recent FDA guidances have established CDISC models such as ADaM and SDTM as submission standards. As a result, most organizations have focused CDISC implementation strategies on SDTM and ADaM, which have usually led to higher costs and longer timelines. Moving standards implementation "upstream" can maximize the value obtained from CDISC standards. One largely overlooked standard is the Protocol Representation Model (PRM), the beginning "end" of "end-to-end." The PRM has the content and potential to streamline research throughout the entire product life cycle. This paper is an overview of the PRM. It describes what it is; discusses the business case for its implementation; describes Rho's implementation strategy; demonstrates how its use at Rho has improved operations; and presents strategies for collecting and storing PRM metadata. The paper should give the reader an appreciation of the content and scope of the PRM and its use beyond simply storing protocol items as metadata | clinical trial, data governance, sdtm domain, clinical research, cdisc | source | 10 |
| 185 | Three Issues and Corresponding Work-Around Solution for Generating Define.xml 2.0 Using Pinnacle 21 Enterprise | Jeff Xia, Lugang Larry Xie, Shari Lisnov |
This paper discusses three technical issues that occur in define.xml 2.0 files generated by Pinnacle 21 Enterprise, explains their causes, and presents an integrated work-around solution to resolve them with a simple SAS macro. As per the latest "Standards Catalog" released by the FDA, the agency will stop accepting define.xml 1.0 in NDA/BLA submissions starting from March 2018, which will require the industry to move to define.xml 2.0. The web-based application Pinnacle 21 Enterprise, is emerging as a useful tool in generating define.xml 2.0 in a high quality format that meets the agency's expectations for submissions | txt, put statement, cdisc | source | 6 |
| 186 | Tips and Best Practices using SAS Analytics | Tho H. Nguyen, Paul Segal |
Many analytical computing solutions and large databases use new techniques because they provide significant performance improvements over the traditional methods. With SAS in-database and in-memory analytics for Teradata, SAS users have the ability to prepare the data, develop complex data models and score the model in the data warehouse. By doing so, it removes the need to either move or extract the data to a SAS environment or convert the analytical code to something that could be executed on the data platform. This paper discusses some tips and best practices with SAS/ACCESS, SAS formats, data quality, DS2, model development, model scoring, Hadoop and Visual Analytics - all integrated with the data warehouse |
proc step, data wrangling, big data, in-memory analytics, in-database processing, teradata, data warehouse, data standard, data mining | source | 7 |
| 187 | Topology-based Clinical Data Mining for Discovery of Hidden Patterns in Multidimensional Data | Sergey Glushakov, Iryna Kotenko, Andrey Rekalo |
Clinical trial data is notoriously heterogeneous, incomplete, noisy, and multidimensional. These data may contain valuable information, and novel insights may be encapsulated in various patterns hidden deep within it. While the majority of approaches to mining clinical trial data focus on univariate relationships between a handful of variables, there is a lack of data integration and visualization tools that can improve our understanding of the entire data set. The aim of this paper is to describe the application of a holistic, topology-based clinical data mining (TCDM) methodology to discover multivariate patterns in clinical trial outcomes. This geometric, data- driven approach allows researchers to identify meaningful relationships in data that would otherwise be left unidentified by traditional biostatistical approaches | clinical trial, sample, clinical data, euclidean distance, data science, data mining, clinical endpoint, clinical research, feature extraction, proc freq, data integration, histogram, adverse events, treatment arm, proc princomp | source | 20 |
| 188 | Translating Statistics into Knowledge by Examples Using SAS Graphic Procedures | Tao Shu, Jianfei Jiang |
In a clinical trial, sponsors are required to communicate safety and efficacy data clearly and efficiently to regulatory agencies and to the public. Application of report and expl oratory graphic visualization is crucial in summarizing clinical trial data in ways that can facilitate the precise conveyance of the statistical analysis with compelling visual stories. Here , we employ three different examples from Lilly Rheumatoid Arthri tis clinical trials to demonstrate how SAS 9.4 graphic al procedures can translate statistical analysis into vivid graphical forms. In the first example, using the SGPLOT procedure, we efficiently present the p-value in line graph and bar chart graphical reports. The clinically important effect of the Lilly compound (Baricitinib), as revealed by changes in American College of Rheumatology 20% response and modified Total Sharp Score, is clearly demonstrated at multiple time points when compared to placebo |
change from baseline, proc step, clinical trial, proc sgpanel, sas output, treatment arm, proc genmod, proc sgplot, proc mixed, data science, poisson distribution, adverse events | source | 11 |
| 189 | Two Roads Diverged in a Narrow Dataset-When Coarsened Exact Matching is More Appropriate than Propensity Score Matching | Aran Canes | Coarsened Exact Matching (CEM) is a relatively new causal inference technique that allows the researcher to non-parametrically create a matched dataset to evaluate the effect of a treatment. Propensity Score Matching (PSM) is the older, more established technique in the literature. Both methods have been turned into SAS macros which are used by many SAS data scientists. In one particular instance, I was tasked to deal with a dataset generated from pharmacy data where it seemed that, because PSM would not work due to the ratio of cases to controls, the only viable alternative would be the use of regression. However, this alternative was unappealing as it imposes a linear model on the data. Remarkably, I found an N:N CEM was able to match 100% of the case population even though the ratio of cases to controls was close to 1:1. In this paper, I show why PSM was not feasible in this particular instance and why CEM provided a non - parametric alternative to regression | causal inference, propensity score, propensity score matching, regression analysis, treatment effect | source | 7 |
| 190 | Update on Data Standards | Ronald Fitzmartin | clinical research, data cleaning | source | 25 | |
| 191 | Usage of Pinnacle 21 Community Toolset 2.x.x for Clinical Programmers | Sergiy Sirichenko, Michael DiGiantomasso |
All programmers have their own toolsets like a collection of macros, helpful applications, favorite books or websites. Pinnacle 21 Community is a free and easy to use toolset, which is useful for clinical programmers who work with CDISC standards. In this Hands-On Workshop (HOW) we'll provide an overview of installation, tuning, usage and automation of Pinnacle 21 Community applications including: Validator - ensure your data is CDISC compliant and FDA submission ready, Define.xml Generator - create metadata in standardized define.xml v2.0 format, Data Converter - generate EXCEL, CSV or Dataset-XML format from SAS XPT, and ClinicalTrials.gov Miner - find information across all existing clinical trials |
data cleaning, excel, data validation rules, clinical trial, controlled terminology, cdisc controlled terminology, clinical research, cdisc | source | 23 |
| 192 | Use of Traceability Chains in Study Data and Metadata for Regulatory Electronic Submission | Tianshu Li | Traceability is one of the fundamental requirements for electronic submission. It helps the FDA or other regulatory agencies to understand the data's lineage and the relationships among the process of the data collection, SDTM and ADaM data generation, Metadata, and analyses results. The establishment of a clear and unambiguous traceability chain will show the transparency of the electronic submission (e- sub) package and build confidence in the quality of the analyses results and statistical conclusions. Ultimately, it will help expedite the review and approval process. Based on oncology data as an illustration, this paper describes the type, elements and relationships of good traceability chains, and some important considerations in the process | raw data, bds, cdisc | source | 8 |
| 193 | Using Animated Graphics to Show PKPD Relationships in SAS 9.4 | Andrew Mccarthy | The relationship between pharmacodynamics and pharmacokinetics is often complex and it can be difficult to conceptualize how drug induced changes relate to drug levels. Animated, multivariate visualisations can provide insight into these relationships. Using the features of SAS 9.4R⃝, spectacular animated plots can be produced from SAS Graph Template Language (GTL) procedures simply with the use of a by statement. This paper will demonstrate how to produce a graphic that shows multivariate changes in electroencephalo- graphic (EEG) spectral activity and the relationship these have with drug plasma exposure | by group processing, graph template language, proc freq | source | 8 |
| 194 | Using Hash tables for AE search strategies | Vinodita Bongarala, Liz Thomas |
As part of adverse event safety analysis, adverse events of special interest (AESI) are identified within a study by a variety of means. Most commonly, AESIs can be identified with standardized MedDRA Queries (SMQs), MedDRA System Organ Classes (SOCs), or a customized collection of MedDRA preferred terms (PTs) or lower level terms (LLTs). Analysis of AESIs is similar, regardless of the search strategy used to identify them. Identifying AESIs may involve merging the AE dataset with multiple datasets on keys of different levels (LLT, PT or SOC). Using a hash table as opposed to multiple sorted or unsorted merges can simplify both the code and execution time. Faster processing with SAS hash tables has considerable utility in large safety databases. The efficiency gain of using hash tables over other methods of merging (both sorted and unsorted) has been well characterized | hash table, adverse events, proc sql | source | 6 |
| 195 | Using SAS for Application Programming Interface Requests | Mike Jadoo | Application Programming Interface (API) is a method of requesting data and has been around for some time. Their primary uses are for front-end webs developers who wish to use these respective data sets for charts, tables, and maps. However, this method of data request can also be useful for data processing and analysis as fewer steps in the data products process can be achieved. In the paper we will discover how an API request can remove steps in the data production process and how to make a request for data from a major statistical accounts producer. Moreover, some useful tips will be revealed when using this method | dataset, proc http, proc step, macro variable, api, web service, data wrangling | source | 9 |
| 196 | Using SAS ODS EXCEL Destination with SAS University Edition to send graphs to Excel | William E. Benjamin Jr. | Students now have access to a SAS learning tool called SAS University Edition. This online tool is freely available to all, for non-commercial use. This means it is basically a free version of SAS that can be used to teach yourself or someone else how to use SAS. Since a large part of my body of writings has focused upon moving data between SAS and Microsoft EXCEL, I thought I would take some time to highlight the tasks that permit movement of data between SAS and EXCEL using SAS University Edition. This paper is directed toward sending graphs to EXCEL using the new ODS EXCEL destination software |
excel, xls, sas studio, output delivery system, proc step, ods excel, cdisc | source | 15 |
| 197 | Visual Basic for Applications: A Solution for Handling Non-ASCII Removal and Replacement | Eric Crockett | CDISC (or Clinical Data Interchange Standards Consortium) submission data and documentation should not include non-ASCII or non-printable characters. However, the source information used to support the data and documentation often (and unavoidably) includes such characters. For the Trial Design domains such as TI or TS, it is common for companies to leverage SAS macros to address this issue. When the source information is present in the mapping specification the best approach may be to programmatically address the issue within the specification. An example of how special characters can be removed and replaced at the specification level using a Microsoft Office Visual Basic for Applications (VBA) macro will be shared and the practical advantages discussed | cpu time, excel, cdisc | source | 4 |
| 198 | Visualizing Enrollment Over Time | Laura Gruetzner | Taking a closer look at patient enrollment over time provides valuable insight and can support strategic decisions or further analysis for just about any type of clinical trial. You can often visualize early-phase studies with small sample sizes on the patient level, resulting in a compact and comprehensive summary of enrollment and events like progression, crossover, or adverse reactions. For larger trials with multiple treatment arms, area charts or simple line plots of patients per cohort over time let you keep track of the developing trial population, periods of slow enrollment, and proportions between arms. This paper explores how to graphically inspect enrollment patterns over time for early - and late-phase clinical trials in an example-based approach. Focus is placed on different ways to visualize enrollment for dose escalation, randomized controlled two-arm, and basket (or umbrella) trials using SAS 9.4 ODS Graphics |
treatment arm, dose escalation, group by, proc step, interim analysis, bar chart, clinical trial, proc sgplot, randomization | source | 11 |
| 199 | What Are Occurrence Flags Good For Anyway? | Nancy Brucken | The ADaM Structure for Occurrence Data (OCCDS) includes a series of permissible variables known as occurrence flags. These are optional Y/null flags indicating the first occurrence of a particular type of record within a subject. This paper shows how occurrence flags can be used with PROC SQL to easily produce tables summarizing adverse events (AEs) by System Order Class (SOC) and dictionary preferred term |
proc sql, proc step, adverse events | source | 4 |
| 200 | What Makes a Candidate Stand Out? | Edward Slezinger | Curious how to move ahead or move on? Wondering how to make your resume stand out from others with equal experience? Wanting to find something that you can do or say that will keep you in the forefront of a hiring manager' mind? We will cover these questions and more to help you find a path to whatever you would like your future to look like | sample, clinical research | source | 3 |
| 201 | When one is not enough or multi-celled plots: comparison of different approaches | Vladlen Ivanushkin | Data visualization is a very powerful way to present data and therefore has been used for a long time in clinical trials. However, sometimes it happens that the number of plots required for a project is huge, or maybe you just don't want to scroll pages in a document, or open many files to compare a couple of plots. In such situations, it's a common practice to put several plots on one page to spare some space, to have better comparison possibilities, or just for a better overview. Some years ago a programmer would need to write tons of code to create some non-standard multi- celled graphics. However, the progress does not stand still and many tasks, which used to require much time and effort in the past, can now be replaced with just few lines of code | regression analysis, scatter plot, proc step, proc sgplot, proc greplay | source | 12 |
| 202 | Where Is the Link Broken - Another Look at SDTM Oncology Tumor Packages | Hong Wang, Ke Xiao |
CDISC published three oncology tumor related domains - Tumor Identification (TU), Tumor Results (TR) and Disease Response (RS) in SDTM IG v3.2 (2013). A general guideline is provided for data regulation and standardization for oncology studies based on RECIST criteria and/or its modifications as well as other assessment criteria (Cheson or Hallek). Essentially three domains do not function independently; instead they have inherent connections and have been linked through -LNKIDs and -LNKGRPs. For example, TRLNKID/TULNKID is used to link assessment records in TR domain with corresponding identification records in TU domain. However, using RSLNKGRP and TRLNKGRP to connect RS and TR might not be sufficient; neither is it clear to group records within RS domain. As an illustration, solely using -LNKGRPs is feasible to connect all the measurements, including those for target/non-target/new lesion responses, in TR domain with respective overall assessment in RS domain at a measurement point | pd, tumor identification, tumor results, cdisc | source | 12 |
| 203 | Woops, I Didn-t Know! An Elegant Solution to Let Your Entire Department Benefit from Individual Lessons Learned | Michael Hagendoorn, Annia Way, Tim Yerington, Rachel Bowman |
After completing a clinical trial analysis, individual programming and biostatistics teams often produce lessons learned (LLs) based on the project they just finished. Such LLs are typically captured in slide decks or Word files, shared once as a presentation within the small team, and then stored in the individual study directory to soon be forgotten about | database lock, excel | source | 9 |
| 204 | Work with me here-(or there or anywhere): Practical Cloud Collaboration with JMP Clinical for Clinical Trial Data Reviews | Kelci J. Miclaus, Drew Foglia |
JMP Clinical is a solution built on powerful analytics and data visualization of both SAS and JMP software. It provides a rich environment for generating clinical trials reviews driven by CDISC data standards (SDTM/ADaM) for safety analysis, fraud detection and data integrity/quality, operational monitoring and oversight, and data management. Configuration options to enable study and review sharing allow the solution to be a part of collaborative architecture for clinical tr ials data analysis and review. A scenario of such sharing using mapped network drives will be shown for generating clinical trial safety analysis reports including patient profiles and auto-generated patient narratives for medical monitor review. With the increasing popularity and acceptance of cloud-based storage services, collaborating with colleagues, partners, regulatory agencies, and customers has never been easier | clinical trial, adverse events, data review, data science, clinical data, cdisc | source | 14 |
| 205 | Writing Efficient Queries in SAS Using PROC SQL with Teradata | Mina Chen | The emergence of big data, as well as advancements in data science approaches and technology, is providing pharmaceutical companies with an opportunity to gain novel insights that can enhance and accelerate drug research and development. The pharmaceuticals industry has seen an explosion in the amount of available data beyond that collected from traditional, tightly controlled clinical trial environments. Investing in data enrichment, integration, and management will allow the industry to combine real-time and historical information for deeper insight as well as competitive advantage. On the other hand, pharmaceutical companies are faced with the unique big data challenges collecting more data than ever before. There has never been a greater need for efficient data analytical approach. Together, SAS and Teradata provide a combined analytics solution that helps big data analysis and reporting | proc sql, sql pass-through, proc step, implicit sql pass-through, relational database, libname statement, big data, clinical research, create table, data science, parallel processing, select | source | 5 |
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