Working More Effectively With Statisticians by Deborah M. Anderson. (Fall 2012 Newsletter of Society for Clinical Data Management, pages 5-8)
Abstract:
The role of the clinical trial biostatistician is to lend scientific expertise to the goal of demonstrating safety and efficacy of investigative treatments. Their success, and the outcome of the clinical trial, is predicated on adequate data quality, among other factors. Consequently, the clinical data manager plays a critical role in the statistical analysis of clinical trial data. In order to better fulfill this role, data managers must work together with the biostatisticians and be aligned in their understanding of data quality. This article proposes ten specific recommendations for data managers in order to facilitate more effective collaboration with biostatisticians.
See the article for the details but the recommendations are generally applicable to all data collection projects:
Recommendation #1: Communicate early and often with the biostatistician and provide frequent data extracts for review.
Recommendation #2: Employ caution when advising sites or interactive voice/web recognition (IVR/IVW) vendors on handling of randomization errors.
Recommendation #3: Collect the actual investigational treatment and dose group for each subject.
Recommendation #4: Think carefully and consult the biostatistician about the best way to structure investigational treatment exposure and accountability data.
Recommendation #5: Clarify in electronic data capture (EDC) specifications whether a question is only a “prompt” screen or whether the answer to the question will be collected explicitly in the database.
Recommendation #6: Recognize the most critical data items from a statistical analysis perspective and apply the highest quality standards to them.
Recommendation #7: Be alert to protocol deviations/violations (PDVs).
Recommendation #8: Plan for a database freeze and final review before database lock.
Recommendation #9: Archive a snapshot of the clinical database at key analysis milestones and at the end of the study.
Recommendation #10: Educate yourself about fundamental statistical principles whenever the opportunity arises.
I first saw this at John Johnson’s Data cleaning is harder than statistical analysis.