PharmaSUG CN 2015 Papers - Index

38 papers. Source: PharmaSUG (Pharmaceutical Software Users Group) conference proceedings.

#	Title ×	Authors ×	Abstract ×	Keywords ×	Pages
1	A Fully Automated Approach to Concatenate RTF outputs and Create TOC	Zhiping Yan, Lugang Larry Xie	Statistical reports for clinical studies usually contain huge amounts of tables, listings and figures. As `ODS RTF` has gained its popularity in clinical trial reporting, it is extremely useful to concatenate RTF reports and create a hyperlinked table of contents (TOC) to facilitate review. However, the approaches introduced by the existing papers have issues in one way or another. For instance, manual process must be involved - TOC field is updated by pressing F9; anchor ID must be inserted in the reports by each individual SAS programmer, etc. This paper introduces a relatively simple and very efficient SAS approach to concatenate reports generated by `ODS RTF` and create a nicely formatted hyperlinked table of contents. It utilizes the bookmark/hyperlink features of RTF, allowing the generation of TOC to involve no manual process and hidden text. At the end, this paper also attempts to discuss on potential issues and future improvement	ods rtf, proc step	6
2	A Macro to Add Variables to SDTM Standard Domains	Xianhua Zeng	In SDTM domains, all character variables are limited to a maximum of 200 characters due to FDA requiring datasets in SAS v5 transport format. Text more than 200 characters long should be stored as a record in the SUPP--dataset. But Comments (CO) and Trial Summary (TS) domains are allowed to add variables for the purpose of handling text exceeding 200 characters sections. To improve readability the text should be split between words not just broken the text into 200-character - i.e., when text is longer than 200 characters in CO domain, additional variables COVAL1- COVALn will be derived. The first 200 characters of the comment will be in COVAL, the next 200 in COVAL1, and additional text stored as needed to COVALn. This paper presents the AddVar macro, which splits a long text variable into a set of smaller variables without truncating an intact word and automatically generate COVAL- COVALn in CO domain or TSVAL- TSVALn in TS domain. AddVar checks that the user inputs are valid and that the specified split character does not already exist in the input variable. The newly created variables are added to the output data set	character variable, prxchange, cdisc	5
3	A Macro to Automatically Select Covariates from Prognostic Factors and Exploratory Factors for Multivariate Cox PH Model	Yu Cheng	Multivariate Cox PH model is a widely used analysis to estimate hazard ratio by constructing a model based on the selected variables among a large number of factors. The selection algorithm is normally simplified as: 1) use a full model to include all potential prognostic factors and exploratory variables, 2) select covariates which are significant at a pre-specified alpha level based on certain selection method, 3) fit a reduced model with only selected variables and variables forced into the final model, 4) repeat the above steps and construct other models on different combination of covariates. This paper presents a macro to automatically go through the process and generate final reports for clinical trial reporting use. From the examples described, the purpose is to provide a thought that other programmers can use to automatically generate a batch of analysis reports in the shortest possible time	macro variable, hazard ratio, regression analysis, proc phreg, clinical trial, macro parameter, stepwise selection	6
4	A proposal for intervention and event partial date time imputation	Zhao Chunpeng	Presentation	imputation, sas function, clinical trial	13
5	Application of Output Delivery System in Creating Customized Targets	Yuan Wang	The default results from SAS STAT `PROC` contains many useful information. Organizing these output objects in SAS can be confusing, customizing the analysis summary results into required clinical output would be more tedious. In fact SAS Output Delivery System (`ODS`) is a powerful tool in dealing with all these issues. In this paper, we will present how to use SAS `ODS` to select a statistic output, convert a processed output into a dataset, organize them into a pretty RTF output and add some special symbols to explain the statistic model	output delivery system, proc step, out=, ods rtf, proc template, proc report	6
6	Approaches to Missing Data in the Analysis of SpondyloArthritis International Society (ASAS 20) Response and the Creation of the Related CDISC Compliant Analysis Datasets	Christine Joy Dureza	Randomized controlled trials (RCTs) is one of the preferred study designs for evaluating interventions. Unlike observational studies, the randomization of patients to interventions means that a direct causal association can be made between an intervention and its effect. In order to measure the treatment effect, there is a need to at least measure the patient's response at the end of the trial. In most instances, a series of responses will be measured at baseline and throughout follow -up. However, it is not al ways possible to collect all the intended data on each individual and in all given timepoints. Thus, analysis of the data that were collected without any further reflection generally leads to misleading conclusions. When the data is incomplete, conclusions between the intervention and response is compromised. In this paper, several approaches to data "missingness' is discussed in the analysis of ASAS 20 response. This response is used as an outcome measure, a composite measure, in the investigation of ankylosing sponydylitis (AS). This is a chronic disease characterized by ankylosis (stiffening and immobility) of the spine, and inflammation at the insertions of tendons. To be fulfilled, ASAS 20 response criteria requires ≥ 20% improvement or at least ≥ 1 unit reduction in a 0 to 10 unit scale in at least 3 of 4 domains, with no worse ning in the fourth domain of ≥ 20% or ≥ 1 unit in a 0 to 10 unit scale . The 4 domains refer to patient global assessment, pain, physical function, and inflammation (morning stiffness), or duration of morning stiffness. The missing data approaches such as non -responder imputation (NRI), last observation carried forward (LOCF) , and baseline observation carried forward (BOCF) are presented in this paper and considerations and recommentations on how to prepare a CDISC compliant analysis data sets that includes the abovementioned approaches are provided	missing value, imputation, clinical trial, change from baseline, treatment effect, cdisc, last observation carried forward	12
7	Automatic generating blankcrf.pdf for Rave Study	Haiqiang Luo	blankcrf.pdf is a critical component of eCRT for NDA submission. It is a blank CRF with annotations that documents the location and mapping of CRF data with corresponding da ta set names and variables names in the tabulation data set. Manual creating blankcrf.pdf is a common practice. However, it is a te dious repetitive task which can lead to inconsistence issue among sibling studies. The standardize d structure of CRF produced by Medidata Rave System makes it feasible to automate the mapping process. Th is paper describes a method of generating blankcrf.pdf automatically for this kind of studies	dataset, order by, crf, edc	8
8	Build Child Growth Charts Using SAS GTL	Rajesh Babu Moorakonda	Creating child growth charts in SAS with different requirements such as plotting the different lines for P5, Median and P95 for each treatment group by distinguishing the treatment period with multi-pattern lines, unequal axis intervals and printing axis aligned statistics table with same treatment color codes below graph within graphics area is daunting task in child nutritional trials. This paper will explain the difficulties faced during the development of a growth chart and their step-by-step programming solutions using the SAS 9.3 Graph Template Language along with an example	treatment period, graph template language	8
9	Clinical trials optimization: Monte Carlo Simulation modeling and SAS applications	Ye Meng	Modeling and clinical trial simulation is a tool that is being used by pharmaceutical companies and FDA to improve the efficiency of drug development. Monte Carlo Simulation is a modern and computationally efficient algorithm. Therefore it is a brilliant technique in terms of patient recruitment process and dose calculation in clinical design. The purpose of this paper is to describe how Monte Carlo simulations are tasked with evaluating parameter distributions and its applications in `PROC MI`. Here we also describe two approaches, BY command / `PROC IML` each with a presented example in SAS that leads to short and fast simulation programs that can be used in many practically situations. The SAS V9 products used in this paper are SAS BASE, `SAS/STAT`, and SAS /GRAPH on a PC Windows platform	sample, clinical trial, monte carlo, proc step, missing value, proc mi, clinical research, sample size, imputation, proc mixed	5
10	Comparison of Three Methods in Calculating Confidence Intervals for Rate Differences in Non-inferior Clinical Trials	Yaohua Huang	Non-inferior clinical trials are widely used in pharmaceutical and medical device products development. While the primary endpoints are categorical variables, confidence intervals for rate differences are usually calculated. These confidence intervals are compared with pre-specified non-inferior margin to judge whether non-inferiority assumption can be reached. Approximately Normal, Exacted Probability, and Newcobe-Wilson Score methods are introduced in this paper to calculate the confidence intervals for rate differences. In addition, power analysis of these methods will be conducted with Monte Carlo simulation. If sample size is planned using Approximately Normal, all 3 methods can provide enough power if at least one group's rate is neither 0% or 100%. If both groups' rates are 0% or 100% only Newcombe-Wilson Score method can provide the confidence interval but the power is not sufficient. Key words: Confidence Intervals, Newcombe-Wilson Score, Power	confidence interval, sample, sample size, clinical trial, proc calis	4
11	Configuring SAS Business Intelligence (BI) client with the SAS server to support multilingual data	Wei Zheng	As the pharmaceutical industry has become an increasingly global enterprise, more and more companies are processing multilingual data. In order for a SAS Business Intelligence client to support this type of data, the language environment must be configured correctly between the SAS client and the SAS server. Proper configuration includes the correct locale setting on the client side and the correct encoding on the server side. This paper, written for SAS users and SAS administrators, introduces several methods for configuring an effective and optimized multilingual working environment for SAS clients such as SAS Enterprise Guide, SAS Data Integration Studio, and SAS Enterprise Miner. One item of particular interest in the paper is a unique approach to setting the workspace server to different encoding values on a per-user basis. The methods that are discussed apply to SAS servers in both the Microsoft Windows and UNIX operating environments. Each method's advantages and disadvantages are detailed, along with an example scenario that illustrates when the method is best used. SAS Enterprise Guide 7.11 and SAS 9.4 Unicode Server are used, respectively, as the client and server in the examples	sas enterprise guide, sas administrator, options statement, business intelligence, data mining	13
12	Creating Binary Tables in a Snap	Yan Liu	Summary tables for binary variables are very common in clinical trial analysis. Conventional methods usually require stacking a bunch of rows of binary variable analytical outputs to get the results desired. It is time-consuming and inefficient from the programming point of view. This paper introduces a convenient SAS macro that can create binary tables with one simple macro call and is capable of using either subject level data set (ADSL) or BDS (Basic Data Structure) ADaM data sets as inputs or a combination of these two types of data sets in case of mixed requirements.	binary variable, macro definition	5
13	Effectively Utilizing Loops and Arrays in the DATA Step	Arthur X. Li	The implicit loop refers to the `DATA STEP` repetitively reading data and creating observations, one at a time. The explicit loop, which utilizes the iterative DO, DO WHILE, or DO UNTIL statements, is used to repetitively execute certain SAS statements within each iteration of the `DATA STEP` execution. Utilizing explicit loops is often used to simulate data and to perform a certain computation repetitively. However, when an explicit loop is used along with array processing, the applications are extended widely, which includes transposing data, performing computations across variables, etc. Being able to write a successful program that uses loops and arrays, one needs to know the contents in the program data vector (PDV) during the `DATA STEP` execution, which is the fundamental concept of `DATA STEP` programming. This paper will cover the basic concepts of the PDV, which is often ignored by novice programmers, and then will illustrate how utilizing loops and arrays to transform lengthy code into more efficient programs	do loop, output, execution phase, array, in=, conditional do loop, out=, ranuni, dataset, missing value, compilation phase, array bounds, implicit loop, temporary array, set statement, standard deviation, automatic variable, class variable, normal distribution	17
14	Fifty Shades of Sorting	Haibin Shu, Elena Rojco, John He	The title of 'Fifty Shades of Grey' means a lot of facets of the main character Grey's personality, likewise, the tricks and tips of sorting variables in SAS go way beyond the syntax of `PROC SORT`. In many circumstances a good order does matter therefore customized sorting variables are to be adopted for ac hieving a desirable sorting effect. A variety of examples will be presented to illustrate in details why customized sorting variables are needed in these specific situations and how they are derived accordingly. Further, the ideas can be generalized to prototype/establish a systematic approach. For the consideration of the effect of reporting and presenting information that often depends on whether a good order is in place SAS programmers may use `PROC PRINT`, `PROC REPORT`, or even a DATA SET with sorting techniques to consistently solve certain challenging issues	proc step, proc sort, numeric variable, proc report, character variable, proc sql, adae	6
15	Generating Define.xml Using SAS By Element-by-Element And Domain-by-Domian Mechanism	Lina Qin	An element-by-element and domain-by-domain mechanism is introduced for generating define.xml using SAS. Based on CDISC Define-XML Specification, each element in define.xml can be generated by applying a set of templates instead of writing a great deal of "put" statements. This will make programs more succinct and flexible. The define.xml file can be generated simply by combining, in proper order, all of the relevant elements. Moreover, as each element related to a certain domain can be separately created, it is possible to generate a single-domain define.xml by combining all relevant elements of that domain. This mechanism greatly facilitates generating and validating define.xml.	raw data, annotated crf, define xml, sdtm domain, cdisc	12
16	Generic Macros for Data Mapping	Qian Zhao, John Wang, Ruofei Hao	Data mapping and its QC can be a daunting task. When data are mapped, e.g., to SDTM, the variable values, variable names, variable types, and data structures, etc., may change. Writing generic macros to perform data mapping and QC of the mapping that work for any data set may seem impossible. This paper explores a technique to use the combination of external control files and generic macros to perform data mapping and QC. This technique can be extended to other uses. This paper uses two examples to illustrate the concept. In the first example, we will demonstrate how to QC a data set mapped from a horizontal structure to a vertical structure by isolating a single variable at a time and circulating through all variables. In the second example, we will demonstrate how to write a macro against data mapping specification to generate mapping program	data mapping, vital signs, macro definition, cdisc	9
17	Handling multiple y axes using SAS Graphs	Mina Chen, Peter W. Eberhardt	Graphics are a powerful way to display clinical trial data. Graphs are widely generated in the pharmaceutical industry to review the results of clinical trials. For example, mean plot is often used to show laboratory results change over time. Sometimes there's a need to display the changes of two laboratory parameters over time within one mean plot. A second y-axis is useful for plotting data with different scales on the same plot. This paper will show, by example, how such graphs can easily be created using the `SAS/GRAPH`	clinical trial, line chart, clinical data, proc step, bar chart, proc template	5
18	Hands-On ADaM ADAE Development	Sandra Minjoe	The Analysis Data Model (ADaM) Data Structure for Adverse Event Analysis1 was released by the Clinical Data Interchange Standards Consortium (CDISC) ADaM team in May, 2012. This document is an appendix to the ADaM Implementation Guide (IG)1 v1.0, and describes the standard structure of the analysis dataset used for most of our typical adverse event reporting needs. This hands-on training focuses on creating metadata for a typical adverse event (AE) dataset. Attendees will work with sample SDTM and ADaM data, finding information needed to create the results specified in a sample set of table mock-ups. Variable specifications, including coding algorithms, will be written. Some familiarity with SDTM data, AE reporting needs, SAS `DATA STEP` programming, and Microsoft `EXCEL` is expected. Attendees will also learn how to apply the data structure for analyses similar to adverse events, such as concomitant medications. The ADaM Occurrence Data Structure, currently in draft form at the time of this writing but potentially available in final form by the time of the conference, will also be referenced	adverse events, adam dataset, concomitant medications, system organ class, in=, medical history, imputation, cdisc	7
19	Hands-on Tutorial for Piecewise Linear Mixed-effects Models Using SAS PROC MIXED	Qinlei Huang	Clinical trials and public health studies are focused on studying changes associated with interventions, events, or critical periods in human development. Evaluati ng the effect of critical or high -risk events/periods in longitudinal studies of growth may provide clues to the long -term effects of life events and efficacies of preventive/therapeutic interventions. Conventional linear longitudinal models typically involve a single growth profile to represent linear changes in an outcome variable across time, which sometimes does not f it the empirical data. The piecewise linear mixed-effects models allow different linear functions of time corresponding to the pre - and post -critical time point trends. This step-by-step, hands-on tutorial first introduces the reader to how to perform piec ewise linear mixed effects models by using SAS `PROC MIXED` in the context of a clinical trial with 2 intervention arms and a predictive covariate-of-interest. Second, it illustrates how to obtain the slopes and corresponding p -values of intervention and control groups during pre- and post-critical periods, conditional on different values of the predictive covariate . Third, it explains how to make meaningful compa risons and present results in a scientific manuscript . Illustrative SAS commands are provided to fit piecewise linear mixed-effects models and to generate the summary tables, assisting in the interpretation of the results. Keywords: Longitudinal data, Piecewise Linear Mixed-effects Model, `PROC MIXED`	proc mixed, proc step, estimate, covariance, treatment effect, clinical trial, fixed effects, by group processing, spaghetti plot, between-subject variability, covariance structure, correlation, response variable	15
20	How to Build an "Offshore" Team with "Onshore" Quality	Lulu Swei, Margaret Li	In today's competitive global market, many pharmaceutical companies and CROs are building offshore clinical programming teams for various reasons. Often times, we heard cases about offshore outsourcing deals gone bad. For the past year and half, PRA Health Sciences (PRAHS) successfully build a clinical programming team through a joint venture with WuXi Apptec in China. Currently we have clinical programming teams in Shanghai, Beijing and Wuhan. They truly have become an extension of our global programming team, and enable us to provide around the clock service for our sponsors. This paper will discuss the challenges we faced and solutions throughout the process in the following area: 1. Recruitment and Retention 2. Training and Certification 3. Global and Local Management Support 4. Resourcing Process and Policy 5. Project Governance	clinical programming, data transformation	5
21	How to validate clinical data more efficiently with SAS Clinical Standards Toolkit	Vivian Feng	How do you ensure good quality of your clinical data? Currently there are many tools that can handle simple validation, but if we meet cross-standard validation or other complicated validation, the work becomes very complex. On the other hand, although you can develop validation tools by yourself, it's hard to maintain, you have to do a lot of work to modify existing codes when you need to customize your own validation. This paper presents SAS Clinical Standards Toolkit (CST), a powerful and flexible toolkit which can help you validating your clinical data, as well as supporting other work in clinical research activities. CST validation assesses the compliance of data, and the metadata describing the data. It assesses the consistency of values in a specific column, between columns, across records in a specific data set, and across data sets. After the validation process, it shows accurate and clear results to you. In addition, it can customize the solutions easily to meet your needs. CST is a separately orderable component that is available at no additional charge to currently licensed SAS customers. It's free as long as you have SAS Foundation. The latest version 1.7 is available on SAS 9.4	data cleaning, sample, macro variable, clinical data, raw data, proc sql, sas programmer, proc step	11
22	It's Not Just Fast Merging, It's Powerful Hashing	Mijun Hu	Ever since hash object was introduced in SAS 9, a bunch of papers have commended its high merging efficiency but however overlooked its other powerful functions. In fact, besides the widely used find method of performing the merge, we can also utilize check, find_next, add and output method, etc. to achieve certain purposes with higher efficiency and readability but less coding. The advantage is utterly obvious in large database such as ADLB because it eliminates several times of entire database copying in terms of manipulating multiple hashing within one `DATA STEP`. This paper is intended for any intermediate level hash users who mainly only use find method but would like to explore other functionalities of hash object in real examples	hash table, data step, hashing function	4
23	Managing the analysis programming effort for an NDA submission	Yu Cheng, Quan Zhou	Preparing for an NDA submission takes a lot of work, and statistical programming plays a key role in making sure sufficient and accurate information is provided to the regulatory agencies. This paper presents the experiences and lessons learned from managing the statistical programming effort to support a recent NDA submission to the FDA. We will present the challenges, and share what we did to reduce risk and ensure efficiency and quality. We hope the topics covered can apply and be beneficial to most other programming projects	database lock, adam dataset, clinical programming, data cleaning, cdisc	8
24	Multilingual data support in Dataset-XML with SAS Clinical Data Integration	Jing Gao	Dataset-XML is a CDISC XML format for exchanging clinical study data between any two entities. That is, in addition to support the transport of datasets as part of a submission to the FDA, it may also be used to facilitate other data interchange use cases. For example, the Dataset-XML data format can be used by a CRO to transmit SDTM or AdaM datasets to a sponsor organization. Dataset-XML can represent any tabular dataset including SDTM, ADaM, SEND, or non-standard legacy datasets. With the growing trends in the globalization of Drug Development, there are increasing clinical trials conducted in various countries. So clinical trial data that comes from various countries using different languages may need to be processed. In other hand, CDISC standards are becoming more accepted outside the USA, especially, SDTM is used in many countries that use other character encodings (e.g. Shift-JIS in Japan) for submissions to local regulatory authorities. In this context, one of the advantages of the Dataset-XML format is highlighted: Dataset-XML supports all language encodings supported by XML. This requires that the related industry solutions not only support US-ASCII characters, but also support non-ASCII characters in Dataset-XML. This presentation will introduce: 1) how to create Dataset-XML files with multiple encodings (UTF-8, ISO-8859-1, Shift-JIS, etc.) from SAS datasets using SAS Clinical Data Integration (CDI); 2) how to choose the appropriate encoding for the particular languages in Dataset-XML; 3) the SAS Macros called by CDI to create Dataset-XML; 4) Lastly, let's look into the non-ASCII characters whether are supported by the Dataset-XML Tools (OpenCDISC, XPT2DatasetXML, etc.)	dataset, clinical trial, cdisc	12
25	Not Just Merge - Complex Derivation Made Easy by Hash Object	Lu Zhang	Hash object is known as a data look -up technique widely used in data steps for many of its advantages. Before SAS 9.2, hash object was mostly used to accomplish efficient data merg ing in a `DATA STEP`. However, the hash object did not allow storing and retrieving duplicate keys before SAS 9.2 . This constraint was eliminated in SAS 9.2 where in this version h ash objects in `DATA STEP` can perform data look -up even though the keys are not unique. With this improvement, many complex derivations in our daily work become more straightforward and simpler than before. In this paper, the added features to hash object in SAS 9.2 will be d iscussed. Also examples in analysis database derivations will be given to illustrate how hash object works to improve the implementation efficiency of complicated derivation algorithms	hash table, merge statement, unique key, adverse events, data merging	5
26	Reading and Resolving OpenCIDSC Message	Yu Pang	Presentation	missing value, length statement, adverse events, data standard, trial elements, proc freq, randomization	23
27	SAS End-to-End solutions in Clinical Trial	Emma Liu	Are you in trouble with executing timely queries against historical or ongoing clinical trials data? Is there confusion of retrieving data from particular trials beyond generating standard reports and analysis across different systems? Are you looking to build a robust integration routine to bring diverse sources of clinical data together repeatedly for different trials? How do you build up a fluent collaboration model with external clinical data management partners? If you answered 'YES' to any of these questions, then you need to look at the SAS End-to-End solutions in Clinical Trial	clinical trial, clinical data, data cleaning, data science, data governance, data standardization, adverse events, clinical research, edc, outlier, pharmaceutical industry, macro definition, proc sql, concomitant medications, life sciences, macro variable	17
28	SAS Longitudinal Data Techniques - From Change from Baseline to Change from Previous Visits	Chao Wang	Longitudinal data is often collected in clinical trials to examine the effect of treatment on the disease process over time. The most common operation against this type data is to calculate the change from baseline (CFB). The proven ways to implement this calculation in SAS may include data splitting, one pass DOW (Dorfman-Whitlock DO) - Loop, retain plus LAG function. Sometimes, in addition to CFB, change from previous visits (CFP) could be interesting endpoints for a trial. This paper demonstrates how CFP can be derived with minimum coding by using SAS LAG (N) Function	longitudinal data, change from baseline, sample, lag function	5
29	SAS Tools for Working with Dataset-XML files	Lex Jansen	Dataset-XML defines a new CDISC XML standard format for transporting tabular data in XML between any two entities. That is, in addition to supporting the transport of data sets as part of a submission to the FDA, it may also be used to facilitate other data interchange use cases. For example, the Dataset -XML data format can be used by a CRO to transmit SDTM or ADaM data sets to a sponsor organization. Dataset-XML supports SDTM, ADaM, and SEND CDISC data sets but can also be used to exchange any other type of tabular data set. In 2014 the FDA conducted a pilot to evaluate Dataset -XML as a solution to the challenges of the SAS XPORT v5 transport format. The metadata for a data set contained within a CDISC Dataset -XML document must be specified using the CDISC Define- XML standard. Each CDISC Dataset-XML document contains data for a single data set, but a single CDISC Define-XML fi le describes all the data sets included in the folder. Both CDISC Define-XML v1.0 and CDISC Define-XML v2.0 are supported for use with CDISC Dataset-XML. This paper will introduce the Dataset-XML standard and present SAS based tools to transform between SA S data sets and Dataset-XML documents, including validation. This paper assumes that the reader is familiar with some basic XML concepts, and also with the CDISC Define-XML standard for exchanging metadata. Reference [1] contains both a short overview of the XML needed to understand this paper, and also an overview of the structure of a define.xml file based on Define-XML version 1.0.0. A detailed overview of differences between CRT-DDS version 1.0.0 and Define-XML version 2.0.0 can be found in "Define-XML v2 - What's New" [2]. Keywords: CDISC, Operational Data Model, Dataset-XML, Define-XML, define.xml, metadata, SAS Clinical Standards Toolkit	dataset, sas log, proc compare, data cleaning, character variable, format statement, proc step, numeric variable, label statement, clinical trial, macro call, macro parameter, reference data, missing value, sas base, variable, cdisc, define xml	18
30	SDTM Electronic Submissions to FDA: Guidelines and Best Practices	Christina Chang, Kyle Chang	Electronic data submission is the future of clinical trials. United States Food and Drug Administration (FDA) released several submission guidance documents since last year. The guidance of "Study Data Technical Conformance Guide" provides specifications, recommendations, and general considerations on how to submit standardized study data using FDA-supported data standards. It was developed in an effort to combine the existing Common Issues, Study Data Specifications and Traceability Guidance documents, as well as Validation Rules, in order to offer one technical document that coordinates all these sources for the industry. This will reduce the likelihood of the FDA requesting data to be represented in a manner that contradicts CDISC rules. It also provides technical recommendations to sponsors for the submission of study data and related information in a standardized electronic format. This paper elaborates on the following fundamental and core components to be considered for FDA submissions: Study Data Submission Format, Terminology, Electronic Submission Format, Data Validation and Traceability	data cleaning, data validation rules, sdtm domain, annotated crf, sas base, clinical trial, cdisc	6
31	Statistics And Programming In The Globally Evolving Landscape Of Clinical Trial Registration And Results (CTRR) Disclosure	Paul Ngai, Joyce Hauze	Following up on last year's topic, we discuss recent changes in the globally evolving landscape of Clinical Trial Registration and Results (CTRR) disclosure. The presentation is informative for statisticians and management with beginner to advanced skill levels. CTRR disclosure is a cross-disciplinary topic relating to preparation and regulatory standards, management and career development, data analysis and visualization, and statistics including pharmacokinetics. A recent survey of the DIA Clinical Trial Disclosure (CTD) community returned results that show CTRR disclosure is an activity that will continue to grow and require innovative programming shifts and changes throughout the next few years. Governing bodies are rolling out new rules and regulations while we try to comply with them, using new and ever-changing database environments. In the United States, the US Department of Health and Human Services (DHHS) invited comments on their Notice of Proposed Rulemaking that outlined the changes they intend to include in the Final Rule before the end of 2015, which will affect how results are disclosed on ClinicalTrials.gov. [Note: The anticipated date for the Final Rule recently was revised to February 2016 (or earlier, or later)]. The EMA released Policy 70 that serves as a bridge between where we are today and where we will need to be to comply with the Clinical Trials Regulation in 2016. EudraCT will be changing dramatically because the new portal will not be ready to "go live" until sometime in 2017. The China Food and Drug Administration (CFDA) is keeping up with change. The original Chinese Clinical Trial Register (ChiCTR) was retired in December 2014. The World Health Organization has accepted the new website as a clinical trial register. We will discuss some of their new processes and standards as well	clinical trial, raw data, gcp, adverse events, dataset, data cleaning	8
32	Summary level clinical site data for data integrity review and inspection planning in NDA and BLA submission	Jingwei Gao, Nancy Bauer	Presentation	raw data, adverse events, standard deviation, clinical trial, secondary endpoint, clinical programming, treatment arm	41
33	Survival Plots using SAS PROC LIFETEST, GPLOT, and SGPLOT: What are their difference?	Ka Chun Chong, Benny Chung-Ying Zee	Kaplan Meier survival curve is a useful non-parametric approach to summarizing the time-to-event data such as the overall survivals in cancer studies. In the SAS system, LIFETEST, `GPLOT`, and `SGPLOT` procedures are common ways to generate the survival curves. However, the logistics in SAS programming are different among these procedures. In this paper, the difference will be demonstrated by making use of some examples	proc step, kaplan-meier plot, overall survival, proc gplot, proc sgplot, censoring, proc phreg	8
34	The Application of Tolerance Interval in Defining Drug Response for Biomarker	Jing Pan	This paper intends to introduce the tolerance intervals and how the tolerance limit can be generated using the `CAPABILITY` Procedure. Also it uses an example to describe how tolerance intervals can be applied to define a drug response by calculating a specific cutpoint for a certain biomarker	tolerance interval, change from baseline, confidence level, histogram, qq plot	7
35	The loop within: A macro on looping variables and observations	Eduard Joseph Siquioco	Producing the same outputs for different treatments, patients, phases can be tedious when typing a lot of codes when in fact you only need one. This looping macro will help in producing these kinds of outputs for SDTM or TLFs. This macro will also show a p rocedure where variables can be used in the looping calls for a more customized transposition of data set . The paper will introduce the basics of looping and iterating inside macros and will also include a walkthrough of the macro being used for variables, and observations. Keywords: Macro, Looping, Dynamic loop ends	macro loop, macro call, macro variable, date variable, proc step, do loop, data step, cdisc	9
36	Tips for efficient CDISC eCRT production	Lanting Li, Yu Zhu, Yuejuan Meng, Huan Zhu	CDISC Case Report Tabulation Data Definition Specification (Define-XML) is one of primary documents required by FDA for electronic submission, which describes the content and structure of the included data within a submission. eCRT production is helpful to increase the level of automation and improve efficiency of the Regulatory Review process. In most cases, the original datasets and specifications may not be completely 'ready' for direct eCRT development. This will cause repetitive modification of the contents, and even have negative impact on the quality. The objective of this paper is to present tips on improving the efficiency in eCRT production for two kinds of models (Study Data Tabulation Model (SDTM), Analysis Data Tabulation Model (ADaM)) and Integration Summary of Safety and Efficacy (ISS/ISE)). It includes raw data readine ss check before creating define, tips in CRF annotation, data specification requirements to be used for eCRT purpose, tips in define creation, supplemental materials preparation and validation methods. In addition, this paper also broaches the standard wor kflow of development which can be helpful to accomplish eCRT production automation. Key words: CDISC eCRT, specification, define.xml, OpenCDISC	data cleaning, sdtm domain, length statement, variable list, adam dataset, cdisc controlled terminology, raw data, clinical trial, imputation, cdisc	11
37	Use of SAS for Risk-Based Monitoring of Survival Endpoints and Adverse Event Data in Clinical Trials	Zhizhuo Zhang, Sichan Tang, Ka Chun Chong, Benny Chung-Ying Zee	Risk-based monitoring (RBM) could greatly reduce the cost of a clinical trial while protecting safety of a patient and quality of data. Primary endpoints and protocol-required safety measurements are usually identified as critical data for risk assessments. In this paper, we present how to use the basic tools of SAS to detect potential errors for survivals and adverse events data among different sites during centralizing monitoring. The types of errors are generally recognized as more important than others as they could profoundly affect study results and patient safety. This demonstration of RBM could thus enhance the overall efficiency for monitoring activities	macro definition, adverse events, clinical trial, sample, time-to-event endpoint, poisson distribution	5
38	Why Statistical Analysis Plan (SAP) should be comprehensive.	Riddhi Merchant, Ranjith Prayankotveettil	A Statistical Analysis Plan (SAP) is one of the key Regulatory documents which comprises of detailed Statistical methods and techniques used for data analysis. It serves to inform read ers about clinical study design, the execution of statistical methods/techniques, data analysis and reporting. But do we think , "Are the Statistical Analysis Plan always comprehensive enough to proceed with the Data Analysis?" As a Statistician do we write SAP , keeping in mind all the statistical methods, in depth that best correspond to the research hypothesis? This paper will discuss stud y statistician's responsibilities for preparation of SAP in which he should describe detailed procedures for executing the statistical analysis of the primary and secondary variables which is required in conveying statistical results obtained from stated statistical methods used	data science, logistic distribution, proc logistic, proc step, predictor variable, sample, clinical trial, median survival, feature selection, response variable, logistic regression, significance level, likelihood function, statistical programmer, regression analysis, missing value, interim analysis, proc ttest, proc lifetest, proc mixed, censoring	6

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