Summary of Traceability References
Table below summarizes and interprets traceability references found in the public domain (e.g. conference papers), including FDA docs (e.g. Common Issues Document)
|Methods of Building Traceability for ADaM Data||PharmaSUG 2011||Four methods of building traceability in ADaM datasets through examples of questionnaires|
|CDER Common Data Standards Issues Document (Version 1.1/December 2011)||FDA|| Page #6 -
Analysis datasets should be derivable from the SDTM datasets, in order to enable traceability from analysis results presented in the study reports back to the original data elements collected in the case report form and represented in the SDTM datasets.
Comment: FDA seems to expect sponsor to create Analysis dataset from SDTM not from Raw data
|CDER/CBER’s Top 7 CDISC Standards Issues||FDA|| #18 and #19:
6. Traceability Need linkage: CRF -> SDTM -> ADaM -> CSR SDTM datasets should be created from CRFs If instead CRFs -> Raw -> SDTM, your analysis (and hopefully ADaM) datasets should be created from those same SDTM datasets, not the raw datasets Features exist in the ADaM standard that allow for traceability of analyses to ADaM to SDTM Creating SDTM and Analysis data from the raw data is incorrect (especially when submitting only SDTM and analysis data Raw data should create SDTM, and SDTM should then create Analysis
Comment: FDA seems to expect sponsor to create Analysis dataset from SDTM not from Raw data
|Traceability between the clinical database and analysis datasets for a submission||public meeting||Legacy data conversion process well described|
|Traceability between SDTM and ADaM converted analysis datasets||PhUSE 2010||QC process of ADaM conversion well described|
|ADaM Implications from the “CDER Data Standards Common Issues” and SDTM Amendment 1 Documents||SCSUG 2012||Relationship between CDER Data Standards Common Issues document and SDTM IG well elaborated.|
|ADaM or SDTM? A Comparison of Pooling Strategies for Integrated Analyses in the Age of CDISC||PhUSE 2012||Data pooling strategy well described. Details traceability from single study SDTM to single study results by adding additional records to the integrated database with a harmonized parameter.|
|Electronic Common Technical Document Specification V3.2.2||ICH||Multiple citation of navigation and its functional purposes.|
|ICH GCP Guideline for Good Clinical Practice E6(R1)||ICH||Traceability is one of the 13 principles of ICH GCP, Section 2.10, page 9: All clinical trial information should be recorded, handled, and stored in a way that allows its accurate reporting , interpretation and verification.|
|Reflection paper on expectations for electronic source data and data transcribed to electronic data collection tools in clinical trials||EMA|| Comments on traceability:
|EMEA implementation of electronic-only submission and eCTD submission: Questions and answers relating to practical and technical aspects of the implementation||EMA|| Hyperlinks (navigation) are useful when only they add values (Question 3, page 28):
|Data Standards Strategy V1.0||FDA||Traceability is mentioned in the context of legacy data conversion. The agency will "provide technical guidance (nonbinding) to industry for conversion from legacy data to SDTM compliant datasets." (Section 8.2, page 8).|
|Define.xml Version 2.0||CDISC|| Traceability is mentioned in the context of (1) annotated CRF page numbers, (2) Computational Method Definitions, and (3) def:Origin Element:
|CDISC ADaM IG version 1.0||CDISC|| Traceability is required, not optional.|
ADaM has 2 levels of traceability: metadata and data-point.
Intermediate analysis datasets can be included to help describe complex derivations.
Because ADaM requires we include all observed and derived rows for a parameter, we can use ANLzzFL to distinguish which are used in analysis. Doing so provides traceability between the dataset and the results.
|ADaM Datasets for Graphs||PharmaSUG 2013||Example of a clear path from SDTM VISITNUM to ADaM AVISITN and X-Axis on summary figure is provided in Table 2 and Figures 1 and 2.|
|Traceability in the ADaM Standard||PharmaSUG 2013||Basic SDTM to ADaM traceability practices described. ADaM to ADaM traceability practices described. ADaM to table/figure output traceability practices described through the use of analysis flags.|
|Considerations in Data Modeling when Creating Supplemental Qualifiers Datasets in SDTM-Based Submissions||PharmaSUG 2013||Explains the basics of Supplemental Qualifiers in SDTM data. The paper also provides alternative methods for representing data in custom SDTM domains while preserving the relationship between the data in the custom domain and the other collected data.|
|Clinical Data Acquisition Standards Harmonization (CDASH) User Guide||CDISC|| 2.4.2 Using Fields That Do Exist in CDASH
The goal is to have end-to-end traceability of the variable name from the data capture system to the SDTM datasets.
2.4.3 Creating Fields That Do Not Exist in CDASH
|Tame the Traceability Monster||PhUSE SDE 2013||
Presentation at phuse SDE in Copenhagen 28th May 2013.
|Study Data Specifications||FDA||
|Draft eStudy Data Guidance||FDA||
Page #10, line 251:
|Considerations in Creating Transparent SDTM-Based Datasets||PhUSE 2014||Look at a number of specific areas where incorrect or misleading mapping may compromise the goal of 'traceability'.|
|Traceability: Plan Ahead for Future Needs||PhUSE 2014||Describes some simple ways to incorporate traceability into the dataset and output development process, and elaborates on some of the benefits seen when traceability is incorporated. It includes references to documents that we, as two of the co-leads of the Computational Sciences Symposium working group on Traceability and Data Flow, helped develop and post to the wiki.|
|A Day in the Life of a Data Sharing Specialist||PhUSE 2014||ClinicalStudyDataRequest.com website allows data requests/transfers between and among Bayer, Boehringer Ingelheim, GSK, Lilly, Novartis, Roche, Sanofi, Takeda, UCB and ViiV Healthcare.|
|Data Transparency: Moving from Bad Pharma to Good Science||PhUSE 2014||Enhanced data sharing with researchers, public access to data, patients who participate in trials, certifying procedures to share data and reaffirming commitments to publish clinical trial results.|
|Data Transparency Through Metadata Management||PhUSE 2014||Data transparency requires assurance that reported data are accurate and are coming from the official source. Currently however, the data resides in a myriad of systems and formats, making it difficult to maintain the lineage from data collection through analysis and reporting. By managing data about the data or metadata across the enterprise, organizations can provide full data lineage for regulatory compliance and improve business efficiency at the same time.|
|The Capish Information Model - Simplify Access to Your Data||PhUSE 2014||Outlines opportunities for creating clinical data transparency by integrating data to a well-defined, source-independent information model. Also how challenges in protecting patient privacy and intellectual properties can be overcome.|
|Transparency in the Time of Constant Change||PhUSE 2014||Discuss some more trending ways, such as reporting results to CT.GOV, of both creating and presenting data in ways that ensure it is consumable and can be understood not only for analysis/submission purposes but also that post-approval it is transparent and that everyone who has a vested stake can review the data in an appropriate way.|
|Multi-Sponsor Data Transparency: A Group Approach to Sharing||PhUSE 2014||Decisions to implement transparency systems were initially guided by proposed EMA regulations, and are now proceeding under their own momentum as biopharmaceutical companies strive to show that they have nothing to hide regarding their clinical research programs. Additionally, many of the companies that have launched their corporate transparency implementations are going one giant step further, and offering independent researchers the opportunity to readily investigate clinical trials data that spans multiple manufacturers. Fewer than 18 months ago, most biopharmaceutical companies viewed their clinical trial data as strictly proprietary. Today, these companies are actively enabling independent researchers to explore the trial data directly. During this session, you’ll learn more about this important industry initiative, and how your organization can support its success.|
|Draft Functional specifications for the EU portal and EU database to be audited||EMA||The EU portal and the EU database and associated workspace are all designed to provide users with access to data. This will provide traceability and transparency to submitted data.|
|Building Traceability for End Points in Analysis Datasets Using SRCDOM, SRCVAR, and SRCSEQ Triplet||SAS Global Forum 2013||To be compliant with ADaM Implementation Guide V1.0, traceability feature should be incorporated to possible extent in study analysis datasets. There are two types of traceability: (1) Metadata Traceability (2) Data Point Traceability. Data Point Traceability provides clear link in the dataset to specific input data values used to derive analysis values. SRCDOM, SRCVAR, and SRCSEQ triplet is one among many ways suggested by CDISC to establish data point traceability in ADaM datasets. This poster provides various examples of applying SRCDOM, SRCVAR, and SRCSEQ triplet to establish traceability in efficacy ADaM datasets from Cystic Fibrosis therapeutic area.|
|Bertha.sas – User friendly ways to get traceability||PhUSE 2012||Presentation looks at different ways to obtain and document traceability. Final recommendation: Embed SAS programs within RTRACELOC option to document all input and output files|
|Examples of Building Traceability in CDISC ADaM Datasets for FDA Submission||SAS Global Forum 2012||This paper provides examples in applying the inherent traceability features available in ADaM Basic Data Structures (BDS), adding SRCDOM, SRCVAR, and SRCSEQ variables and with examples about adding Relation Criteria and Relation Factor variables in ADaM datasets.|