Estimand Framework and Statistical Considerations for Integrated Analysis of Clinical Trial Safety Data.

Abstract

Background: Safety analyses play a pivotal role in drug development, ensuring the protection of patients while advancing innovative pharmaceuticals to market. A single study generally does not have sufficient sample size to evaluate all important safety events with reasonable precision and may not cover the full target population for the investigational treatment. Integrated analyses (pooled or meta-analysis) over several studies may be helpful in that regard. But without a structured conscious workflow accompanied with appropriate statistical methods for the integrated analysis, this can easily take a route compromising the interpretation.

Methods: In this article we apply the ICH estimand framework to clinical trial integration and summarize respective critical statistical assumptions to ensure the integrated analyses are interpretable.

Results: The estimand framework is valuable for developing principles for a deeper understanding of the critical statistical aspects of planning an integrated safety analysis. Our principles address the clinical question of interest, estimand and estimation. Special focus was given to the criteria for inclusion and exclusion of the component studies in the integrated analysis, and to integration of estimates pertaining to signal detection.

Conclusion: Performing an integrated analysis and its preparatory steps calls for a good understanding of the clinical question of interest and its estimand, care and sound practice, to enable interpretation and avoid introducing unnecessary bias. It is valuable to use the estimand framework not only for efficacy evaluations, but also for safety evaluations in clinical trials and for integrated safety analyses.