Co-Hosted by the Midwest CDISC Users Group
The PharmaSUG/Midwest CDISC Users Group 2016 Chicago Single-Day Event has now concluded. The slides are available for download below. Thanks to AbbVie for hosting the event, and to all who presented and attended. Don't forget that all paid registrants will receive a $75 discount for our annual conference coming in Baltimore in May 2017!
Presentation AbstractsProper Parenting: A Guide in Using ADaM Flag/Criterion Variables and When to Create a Child Dataset
Richann Watson, Experis, and Paul Slagle, inVentiv Health
There has always been confusion as when to use some of the various flags (ANLzzFL, CRITyFL) or category variables (AVALCATy) reserved for ADaM basic data structure (BDS). Although some of these variables can be used interchangeably it might help to come up with rules to help keep a consistency of when to use these variables. Furthermore, there may be some situations where the creation of a new data set would be the better solution. These data sets are referred to as parent-child data sets throughout the paper. This paper will focus on rules that the authors follow when developing ADaM data set specifications for the proper use of ANLzzFL, CRITy/CRITyFL, AVALCATy or when a parent-child data set option is more feasible.
Using CDISC to Support the Healthy Birth, Growth & Development Knowledge Integration (Gates Foundation Project)
Thomas Peppard, Certara
The Healthy Birth Growth and Development knowledge integration (HBGDki) initiative was established to integrate diverse datasets into a multidisciplinary knowledge base for exploration, analysis, modeling and visualization about preterm birth, growth faltering, and impaired neurocognitive development. As of Sept 30, 2016 data from 130 studies have been contributed to the initiative, most of them from birth cohort studies and other observational studies that are clinical in nature. The HBGDki is using CDISC to harmonize contributed data into a common data standard to enable combing data across studies and using standardized tools for visualization and analysis. Considerations when applying CDISC to observational data will be presented.
An Efficient Way to Create the SDTM TS Domain
Jenny Zhang, Astellas
The purpose of SDTM Trial Summary Domain (TS) is to provide a high-level overview for a clinical trial. Submission of TS with other trial design models is required by FDA’s Study Data Technical Conformance Guide version 3.1. Starting from SDTMIG v3.1.3, four new variables TSVALCD, TSCDREF, TSVALNF and TSVCDVER have been added in the model; therefore, the degree of standardization in TS domain has increased significantly. How to populate these variables in an appropriate and efficient way is one of the hot topics among programmers. This presentation provides some suggestions on how to populate these variables efficiently, and demonstrates an example utilizing Excel to create a user-friendly TS template. The template can be used to guide and assist programmers to build the TS dataset faster with CDISC compliance.
DS-Disposition, the Role of One Domain
JJ Hantsch, Parexel
I examine the role of DS in CDISC SDTM’s goals of uniformity, integration and completeness. I describe how I build up the DS domain from data and one report I have used on refreshed clinical trial data for more clinical- and numbers-adverse colleagues.
No USUBJID? No Problem! : Handling OpenCDISC Reports That Don’t Provide USUBJID
Gopi Vegesna, Astellas
The OpenCDISC validation tool has provided reports to identify CDISC non-compliance for many years. Typically programmers run the tool to report out non-compliant findings. The findings may be data-related or programming-related. Potential data-related findings are typically directed to a data management group. Some findings contain the dataset observation number, but do not contain USUBJID and other key identifying information. Locating the record of interest with only the observation number is an inefficient manual task for the programmer.
This presentation describes the process using a macro to replace the manual lookup work around matching a datasets’s observation number with USUBJID. The output of the macro has made our work more efficient and more accurate to identify the issue.
Applying SDTM and ADaM to the Construction of Datamarts in Support of Cross-indication Regulatory Requests
John Knott, Chiltern
The SDTM and ADaM models can be useful when integrating study data from multiple clinical trials into a single data warehouse, commonly referred to as a datamart. Datamarts provide relatively homogeneous and readily available data for responding to regulatory requests. This presentation will discuss some of the complexities involved when integrating study data that spans long periods of time as well as multiple sponsors and indications.
Some Sightings While Traveling the Road of ADaM Spec Review
Nate Freimark, The Griesser Group
The first step to creating complaint ADaM datasets is generally the creation of “dataset specs”. Some of the presenter’s experiences and suggestions will be shown to help clarify what should and should not be done when creating ADaM dataset documentation.
Considerations in ADaM Occurrence Data: Handling Crossover Records for Non-Typical Analysis
Richann Watson, Experis
With the release of the new ADaM Occurrence Data Model for public comment in the first quarter of 2014, the new model is clearly established to encompass adverse events as well as concomitant medications, along with other data into this standard occurrence analysis structure. Commonly used analysis for this type of occurrence structure data can be found utilizing subject counts by category, based on certain criteria (e.g. treatment, cohort, or study period). In most cases, the majority of the analysis data will be in a one-to-one relationship with the source SDTM record.
In this paper, the authors will discuss the creation of ADaM occurrence data for specific cases outside the common or typical analysis where analysis requires a record in SDTM data, which spans across multiple study treatments, periods or phases, to be replicated for inclusion in non-typical analysis with a record being analyzed under multiple study treatments, periods or phases. From the assignment and imputation of key timing variables (i.e. APERIOD, APHASE, ASTDT), through the appropriate derivation of indicator variables and occurrence flags (i.e. ANLzzFL, TRTEMFL, ONTRTFL and AOCCRFL, AOCCPFL, etc.) the authors guide you through the non-typical process in order to maintain efficiency along with ensuring the traceability in the generation of this analysis-ready data structure.
Deconstructing ADRS: Tumor Response Analysis Data Set
Steve Almond, Bayer
From the perspective of someone new to both the oncology therapy area and working with ADaM, this paper describes the process of designing a data set to support the analysis of tumor response data. Rather than focus on the programmatic implementation of particular response criteria (e.g., RECIST, Cheson, etc.), we instead concentrate on the structural features of the ADaM data set. Starting from a simple description of a study’s primary analysis needs, we explore the design impact of additional protocol features such as multiple response criteria algorithms, multiple medical evaluators, and adjudication. With an understanding of the necessary derived parameters to support our analyses, we have a better conceptual link between the collected SDTM RS data and our ADRS analysis data set-- regardless of response criteria being used (the implementation of which is beyond the scope here). We will also touch on some other practical considerations around missing data, interim analyses, further variations in response criteria, and alternate summary table formats which can have an impact on the design of the data set.
To IDB or Not to IDB: That is the Question
Kjersten Offenbecker, Spaulding Clinical Research
In Shakespeare’s Hamlet we hear Prince Hamlet ask the now cliché “To be or not to be” question as he contemplates suicide. How does this relate to ADaM integrated databases (IDBs)? As Hamlet weighs the pros and cons of death, we too must decide whether it is better to stick with the status quo or venture into the unknown world of integrating our ADaMs. We shall examine the pros and cons of ADaM IDBs as well as some of the basic pitfalls we have come across while undertaking this daunting task. Along this journey we will show why we think IDB is the future and why it is better to be on the cutting edge.
ADaM Compliance – Starts With Your Specifications
Trevor Mankus, PRA Health Sciences, and Kent Letourneau, PRA Health Sciences
Later this year the FDA and PMDA will REQUIRE that all new studies included in submissions have their analysis datasets 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 will give some background on this topic and describe the process by which we create datasets that are fully compliant with the ADaM standards. We will 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 datasets.
Presenter BiographiesSteve Almond
Steve Almond is currently a Lead Statistical Analyst for global oncology studies at Bayer and is based in Toronto, Canada. Prior to joining Bayer last year, he was a statistician at GlaxoSmithKline for 10 years, most recently working on multiple successful HIV submissions. He obtained his BMath in Applied Mathematics & Statistics and MMath in Biostatistics from the University of Waterloo.
Nate Freimark is Vice President - Clinical Programming and Data Standards at The Griesser Group. Nate is the current CDISC ADaM team lead, a member of the CDISC TLC (Technical Leadership Committee), one of the ADaM trainers and a member of the SDS Oncology and Compliance subteams. Nate is also a member of several PhUSE teams (working on improving data quality and site selection standards) and the ADaM lead for many CFAST Therapeutic Area User Guides (TAUGs). He has been a member of the ADaM team since 2005, a member of the ADaM Leadership Team since its creation, and has been “doing CDISC” since 2004. Nate has been involved in ADaM Education since its inception from the development of the training material to giving public, private, and FDA ADaM training courses.
JJ Hantsch has been a statistical programmer for the past twenty years and has worked in Japan and New Zealand as well as on the US West Coast, East Coast and several locations in between. He telecommutes for PAREXEL Inc from Wood Dale, IL.
John Knott is a Senior Statistical Programmer for Chiltern International who has seventeen years of SAS programming experience in the Pharmaceutical industry, including eleven years of applying the SDTM and ADaM standards. John's projects have included individual study submissions, integrated summaries of safety and efficacy, datamart development, and regulatory requests.
Kent Letourneau is Executive Director, Global Data Standards at PRA Health Sciences where he has worked for the past 21 years in various leadership roles in programming and statistics. He is an active member of the CDISC ADaM team and part of the Integration, Membership and Traceability sub-teams. In his free time he likes to spend time with his wife and two teenage kids and cheering on Kansas State and Kansas City sports teams.
Trevor Mankus is a Sr. Principal Clinical Data Standards Consultant at PRA Health Sciences. He has been in the industry for 9 years in both CROs and Pharmaceuticals working primarily on CDISC standardizations and submissions. Trevor is currently an active member of the CDISC ADaM Committee and a co-lead of the CDISC ADaM Compliance sub-team. He maintains involvement in PhUSE and regularly attends the Computational Science Symposium (CSS) and is a participant in PhUSE FDA working groups.
Kjersten Offenbecker is the Director of Biometrics at Spaulding Clinical Research where she oversee Data Managers, Programmers, Statisticians and Medical Writers in a Phase I environment. She is a programmer by trade with more than 20 years of experience in the pharmaceutical industry working for large pharma, large and small CROs and as an independent contractor.
Tom Peppard is a Director at Certara Strategic Consulting where he works with the Gates Foundation’s Quantitative Sciences group to help the Foundation’s Global Health program use data to make decisions about program priorities. Tom has worked in drug development for over 20 years as a statistical programmer and statistician, developing and executing statistical analysis plans for clinical trial research.
Bio coming soon.
Gopi Vegesna is currently a lead statistical programming consultant for Astellas Pharma Inc. Gopi has supported statistical programming activities of multiple clinical studies, integrated summaries (safety and efficacy), regulatory submissions and requests in various pharmaceutical companies. His professional interests are to standardize and automate statistical programming.
Richann Watson has been using SAS for over 20 years. She is currently employed at Experis where she is a Delivery Specialist working for a biotechnology pharmaceutical company. She is also a member of the CDISC ADaM team and various sub-teams. In addition, she is the chairperson for the local SAS user group in her area, and is actively involved with other SAS User Groups as well.
Jenny Zhang has twelve years of experience in the pharmaceutical industry. She first started out as a statistical programmer, and currently she works as a Senior Data Standard Manager at Astellas Pharma Global Development, Inc. Her experience includes working on CDISC compliant submissions; providing support to clinical programmers by assisting with application of SDTM and ADaM standards to studies; and developing and maintaining Astellas CDISC standards.