Richann Watson

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PharmaSUG SDE

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October 23, 2020

CDISC PAPERS

Proper Parenting:  A Guide in Using ADaM Flag/Criterion Variables and When to Create a Child Dataset

Richann Watson; Karl Miller; Paul Slagle

Determining when to use AVALCATy, ANLzzFL, CRITy/CRITyFL or create a new data set can be challenging.  This paper provides guidance on the proper use of these variables or when a parent-child data set option is more feasible.

Making Lab Toxicity Tables Less Toxic on Your Brain

Lindsey Xie, Jinlin Wang, Jennifer Sun and Rita Lai, Kite Pharma, a Gilead Company; Richann Watson, DataRich Consulting

Processing and presenting lab data is always challenging, especially when lab limits are assessed in two directions. The lab data process becomes even more complicated when multiple baselines are required due to different analysis criteria or are inherent in the study design. This paper discusses an approach to create lab toxicity grade variables in ADLB for lab bi-directional toxicity report. They are mixed variables defined by Clinical Data Interchange Standards Consortium (CDISC) Analysis Data Model Implementation Guide (ADaMIG) v1.1 and draft ADAMIG v1.2 and by sponsor to make ADLB easily interpreted and related summary tables easily produced.

 

This paper is based on the lab Common Terminology Criteria for Adverse Events (CTCAE) toxicity grade summary, taking into account lab tests with abnormal assessment in either increased direction or decreased direction. In this paper, the authors explain and provide examples showing how ADaMIG v1.1 variables ATOXGR, BTOXGR, SHIFTy, ANLzzFL, MCRITy, and BASETYPE, draft ADaMIG v1.2 new variables ATOXGRH(L) and BTOXGRH(L), and sponsor-defined variables ATOXDIR and WAYSHIFT can be utilized and implemented appropriately. In addition, this paper explains how to handle baseline toxicity grade for analysis sets with more than one baseline in ADLB.

 

More Traceability: Clarity in ADaM Metadata and Beyond

Wayne Zhong, Accretion Softworks; Richann Watson, DataRich Consulting; Daphne Ewing, CSL Behring; Jasmine Zhang, Boehringer Ingelheim

One of the fundamental principles of ADaM is that datasets and associated metadata must include traceability to facilitate the understanding of the relationships between analysis results, ADaM datasets, and SDTM datasets. The existing ADaM documents contain isolated elements of traceability, such as including SDTM sequence numbers, creating new records to capture derived analysis values, and providing excerpts of define.xml documentation.

 

An ADaM sub-team is currently developing a Traceability Examples Document with the goal of bringing these separate elements of traceability together and demonstrate how they function in detailed and complete examples. The examples cover a wide variety of practical scenarios; some expand on content from other CDISC documents, while others are developed specifically for the Traceability Examples Document. As members of the Traceability Examples ADaM sub-team, we are including in this PharmaSUG paper a selection of examples to show how traceability can bring transparency and clarity to your analyses.

 

Are you Ready? Preparing and Planning

to Make the Most of your Conference Experience

Richann Watson, DataRich Consulting; Louise Hadden, Abt Associates Inc.

OWhether you are a first-time conference attendee or an experienced conference attendee, this paper can help you in getting the most out of your conference experience. As long-standing conference attendees and volunteers, we have found that there are some things that people just don’t think about when planning their conference attendance. In this paper we will discuss helpful tips such as making the appropriate travel arrangements, what to bring, networking and meeting up with friends and colleagues, and how to prepare for your role at the conference. We will also discuss maintaining a workplace presence with your paying job while at the conference.

Considerations in ADaM Occurrence Data: Handling Crossover Records for Non-Typical Analysis

Karl Miller; Richann Watson

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.

Standardized, Customized or Both? Defining and Implementing (MedDRA) Queries in ADaM Data Sets

Richann Watson; Karl Miller

Standardized MedDRA Queries (SMQs) is a unique feature of MedDRA which provides a consistent and efficient structure to support safety analysis, reporting, and also address important topics for regulatory and industry users.  But there is also the ability outside of the specific SMQs in allowing the ability to develop Customized Queries (CQs).  Within this paper, the authors discuss the proper use of SMQ and CQ and when one should use a CQ instead of an SMQ.

Avoiding Sinkholes: Common Mistakes During ADaM Data Set Implementation

Richann Watson; Karl Miller

When working with ADaM data sets, non-compliance and other related issues are likely the number one source for numerous hours of re-work.  This paper will assist you in creating compliant ADaM data sets, provide the reasoning on why you should avoid “sinkholes”, all of which will help minimize re-work and likely eliminate the need for additional work.

Traceability: Some Thoughts and Examples for ADaM Needs

Sandra Minjoe; Wayne Zhong; Quan (Jenny) Zhou; Kent Letourneau; Richann Watson

One of the fundamental principles of ADaM is that datasets and associated metadata must include traceability as a link between analysis results, ADaM datasets, and SDTM datasets. An ADaM sub-team is currently developing a Traceability Examples Document, showing how traceability can be employed in a wide variety of practical scenarios. As members of the Traceability Examples ADaM sub-team, we are including in this paper a selection of examples that demonstrate the power of traceability in complex analyses.

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©2018 DataRich Consulting | Produced by Philip Gregory

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