PharmaSUG CN 2017 Papers - Index

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

#	Title ×	Authors ×	Abstract ×	Keywords ×	Pages
1	A convenient tool to check the pagination issue in TLF	Yong Cao, Xuan Sun	Pagination issue happens when records are too many to be put in a single page. The common way to check such issue is to open the RTF outputs one by one and review them manually. However, it's time consuming and laborious. Sometimes the issue may be overlooked because of negligence. To solve such problem, a useful tool which can check pagination issue automatically will be introduced in this paper. When using BY statement in `PROC REPORT` within the context of `ODS RTF` (or `ODS` `TAGSETS`.RTF), SAS starts a new section whenever it comes to a new set of BY values. The key to precisely control the contents in a page is to keep all records within the same section contained in a single page. The core of this tool is a piece of VBScript that retrieves number of sections and number of pages from a RTF output. The script is wrapped into a SAS macro so it's convenient to be invoked within SAS program. What's more, the macro also provides functionality to check outputs in batch	macro definition, ods rtf, proc report, sas log, excel, proc step	6
2	Assigning VISITNUM and EPOCH	Na Duan, Ru He	Presentation	laboratory data, sdtm domain, adverse events, per protocol, reviewer guide, proc sort, cdisc	32
3	Constructive Practice of Generating ADaM Datasets	Amos Shu	Many programmers may not know there are actually three ADaM standard data structures because the latest ADaM Implementation Guide (ADaMIG) (v 1.1, 2016) still only describes two of them - the subject-level analysis dataset (ADSL) and the Basic Data Structure (BDS). A new separate document called "ADaM Data Structure for Occurrence Data" (released on 16 Fed 2016) describes the third standard data structure - the Occurrence Data Structure (OCCDS). Questions still remain as to what are the derived and collected variables in each dataset and how many ADaM datasets should be created for a study. It is virtually impossible to have a universal standard format for all ADaM datasets because of the different analysis needs of different therapeutic areas. There is also much more room for personal discretion in creating ADaM datasets than their corresponding SDTM datasets. ADaM datasets contain both source and derived data, so some variables may contain the same values, e.g. AVALC, VSORRES, and VSSTRESC in an ADVS dataset. This tends to confuse programmers, statisticians, and clinicians because they do not like or are not aware of traceability consideration in creation of ADaM datasets. It is also time consuming to go over such long documents as ADaM and ADaMIG. This paper provides a basic guide to generating ADaM datasets	adam dataset, sdtm domain, bds, adsl, class variable, vital signs, adverse events, clinical programming, cdisc	7
4	Create Zero Observation dataset to achieve maximum metadata resolution in CDISC Submission	M K Sinha	In a world of systematic and efficient programming the success of project delivery largely depends on the accuracy of data and implementation of approved standards. The metadata across all the files (analysis/mapping specification file, raw data and final data) need to match for standardization and traceability. CDISC standards for all most all of the data structures clearly states metadata information of a variable, its order of appearance in a dataset, variable length etc. In order to achieve the same metadata as is specified by company or CDISC standard files, programmers need to be very careful while generating the data set at the final stages. Most of time we enter into situations where number of variables are more, or the order or metadata is different from one actually required in the final data set. Also since these data sets (say SDTM, ADaM) in most cases are developed from SAS coding after referring the analysis/mapping specification file, end moment change in the specification file may not be carried over to these data sets. One approach to limit this error and generate the deliverables per standards is to first develop the analysis/mapping speciation file as per guidelines and then import this file into SAS to create a metadata from these analysis/mapping file. Use these metadata to assign the metadata for final data set. This paper elaborates on the technique of first creating a zero observation data set with the metadata as same as specified in the analysis/mapping specification spreadsheet and then to use this data set to create the final data set. In this approach the analysis/mapping specification file needs to be current and the resulting data sets will be generated as per standard. A review of final data set will double check the metadata consistency not only in the resulting data sets but also on the analysis/mapping specification file	macro variable, sample, sas log, cdisc	7
5	Effective Statistical Programming Preparation and Support for CFDA Self-Inspections and Onsite Inspections	Peng Wan	On July 22, 2015, the CFDA requested that all clinical studies for registration purposes in China be audited by CFDI (Center for Food and Drug Inspection, CFDA) during the NDA/sNDA process prior to obtaining the imported drug license. CFDI will ask sponsors to conduct self-inspections in advance and arrange formal on-site inspections to the clinical trials sites or central laboratory subsequently depending on the technical review progress of the NDA/sNDA application by CDE (Center for Drug Evaluation, CFDA). The purpose is to ensure the integrity and quality of the clinical trials and to confirm the associated data reliability or trustworthiness. Statistical Programming teams need to collaborate with other functions (e.g. Statistician, Data Management, Clinical scientist, CRA) to prepare for and support the inspections. The Statistical Programming team plays a critical role in this activity because we are familiar with the clinical trial data, which is the starting point for analysis supporting CSR, and we know the traceability from data collection to accurate data package we submit to the agency. This Paper will introduce the normal process, best practices, and common issues during these inspections, from a statistical programming understanding and perspective	clinical programming, clinical trial, data science, pk parameter, missing value, concomitant medications	5
6	Extract Information from aCRF by using EXCEL VBA	Kai Zhou, Annie Xu	We need to extract information from annotations of aCRF to state the origin of SDTM variables to build the metadata. Such as CRF page, value list and code list. We usually search such information in the PDF file directly and that makes the task very tedious, time consuming and inaccurate. This paper introduces a method to automate and accelerate the process by using `EXCEL` VBA	excel, cdisc	5
7	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	6
8	How to read RTF files into SAS dataset?	Zhao Chunpeng	There are two ways to develop summary tables and listings for SAS programmers. One is through SAS `ODS`. The other is through SAS LISTING (i.e. generate SAS LISTING outputs first. Then convert SAS LISTING outputs into RTF or PDF files). For summary tables and listings generated through SAS `ODS`, Michiel Hagendoorn etc. gave solutions to read RTF files into SAS datasets in the paper "Save Those Eyes: A Quality-Control Utility for Checking RTF Output Immediately And Accurately" already. For summary tables and listings generated through SAS LISTING, how read the data from an indicated RTF file into a SAS data set? This pager will provide a solution	proc report, in=, sas output, create table, proc compare, txt, data cleaning	7
9	Introduction to Therapeutic Area Data Standards User Guide for QT Studies (TAUG-QT)	David Ju	Presentation		28
10	One Step to Produce Shift Table by Using PROC REPORT	Haiqiang Luo	Shift tables play a very important role in clinical trial analysis. A shift table is a table that displays the number of subjects in different range (e.g. low, normal, or high ) or interested grade at baseline and the n shift or transition at selected time points or time intervals. The purpose of the shift table is to illustrate the progress of changing from baseline and help to make reasonable inference. The common programming logic is firstly getting the frequency from `PROC SUMMARY` (or `PROC SQL`, `PROC FREQ`), then calculating percentage in `DATA STEP`, transposing the dataset by `PROC TRANSPOSE`, and last producing the report by `PROC REPORT`. This paper simplifies th ese tedious procedures and use one step `PROC REPORT` to produce the shift table from analysis dataset	shift table, proc report, proc step, sample, class variable, proc sql, proc freq, clinical trial	7
11	Overview of OSI Deliverables from Programming Perspective	Wang Zhang	The Office of Scientific Investigation (OSI) at U.S. FDA is responsible for the verification of the integrity of the clinical efficacy and safety data. It requests the applicants of NDA(s)/BLA(s) and their supplemental filings to submit summary level clinical site data, line listings by site and other deliverables, and it would be able to use specific algorithms to identify high risk sites and conduct site inspections. This paper offers a brief introduction to the contents of these OSI deliverables, and highlights some take-home messages in the preparation of the package from the statistical programming's point of view. Key Words: Office of Scientific Investigation, OSI Deliverables	clinical trial, treatment arm, clinical data, sample, raw data	6
12	Project Standard Implementation	Yun Ma	Presentation	data cleaning	12
13	SAS Studio - The Next Evolution of SAS Programming Environments	Jim Box, Matthew J. Becker	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	9
14	Streamline Table Lookup by Using HASH in FCMP	Qing Liu	SAS provides many methods to perform a table lookup like `MERGE` Statement, `PROC SQL`, Format. Starting with SAS 9.3, Even we can define our custom routine to perform table lookup since hashing is available in user- defined subroutines through the `FCMP` procedure. By embedding a hash object in `PROC FCMP` functions, simplicity in program can be enhanced. This paper introduces table lookup using HASH in `PROC FCMP` and demonstrate a practical application in using this technic to solve real world problems	lookup, sas function, proc step, proc fcmp, hash table, proc sql	8
15	Strip Special & Non-Printable Characters in Your Data Set	Ying Liu	Clinical data can be collected and analyzed by internal and external vendors. Various ways of importing data may bring out special and non-printable characters in clinical data, accompanied with potential issues in developing quality data sets and deliverables. There are major issues such as incorrect statistical analysis in tables, listings or figures, or minor issues like incorrect breaks in lines or words. In order to manage data effectively, this paper will encode special and non-printable characters by ASCII code in clinical data to understand the essential reasons. Moreover, corresponding solutions to handle these issues will be provided in this paper	clinical data, clinical trial, compress function, dataset, macro variable	6

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