The paper selection process for PharmaSUG 2019 is still underway. In the meantime, while you wait for the full program, here is a small preview of some papers we expect to feature in the conference! (subject to change)
And It's not too late to submit your own paper proposal!
Incremental Changes: ADaMIG v1.2 Update
Nancy Brucken, Syneos Health
Deb Goodfellow, Covance
Brian Harris, MedImmune
Terek Peterson, Covance
Alyssa Wittle, Covance
The ADaM Implementation Guide (ADaMIG) has now been available to industry since 2009, providing a standardized way to communicate and analyze study data. Improvements and clarifications were added in 2016 with the release of v1.1. Since that release, the ADaM team has been working on some items that were not yet ready for v1.1 but are now ready for the next release, v1.2. These items include important clarifications to existing text, standard nomenclature for stratification variables within ADSL, and a recommended approach for bi-directional toxicity grades. In addition, an update on the removal of the new suggested permissible variable within the Basic Data Structure (BDS) called PARQUAL will be discussed. The ADaMIG v1.2 will be discussed from both the perspective of changes from v1.1 as well as changes made since the public review of v1.2.
Practical Guidance for ADaM Dataset Specifications and Define.xml
Jack Shostak, Duke Clinical Research Institute
The goal of this paper is to provide practical guidance for how to specify ADaM analysis datasets within the confines of define.xml nomenclature. This paper’s guidance is presented in a tool and software agnostic manner. The audience of this paper is for staff that produce ADaM datasets and the associated define.xml file, who have little to moderate prior experience in doing so. Firstly, comparing and contrasting define file specifications versus programmer ETL specifications will be addressed in detail. Then, a series of practical issues will be explored with suggested routes of action. Guidance for object derivation length is presented, and when and how links should be employed for those objects. For Origin definitions, “derived” versus “assigned” is discussed at length as to which to use when. A suggested specification strategy for logically linked and synonymous items, such as PARAM/PARAMCD/PARAMN, is presented. There is often a discussion of how much parameter value level metadata is needed, so a discussion of that is presented. That is followed by discussing how you reconcile parameter level metadata with variable level metadata for the same objects. Guidance on when and why to create user defined controlled terminology is given. For date and datetime variables, a discussion around format selection is presented. Appropriate text for object derivations and comments is explored. Finally, suggested guidance is given for other miscellaneous issues around specifying ADaM datasets.
Considerations when Representing Multiple Subject Enrollments in SDTM
Mike Hamidi, CDISC
Kristin Kelly, Pinnacle 21
In clinical trials, it has become more common for a study design to allow subjects to re-enroll in the same study or subsequent studies within a submission. For studies that allow subjects to re-screen for the same study, it may be difficult to determine how to represent the data for multiple enrollments in SDTM. There are a number of approaches seen in industry, but many pose issues. An example of this is creating multiple records in DM with the same USUBJID to represent each enrollment. Though this may seem the most straightforward approach, many tools used at FDA are configured to expect one record per subject and thus, the data may not readily load into their tools. Another approach is to assign different USUBJID values for the same subject within a study and across studies. This also creates issues for review because it is difficult to track the same subject across studies. This paper will focus on examples from industry as well as proposed solutions for representing this data in SDTM.