Roadmap for Causal Inference in Safety Analysis (Quantitative Evaluation of Safety in Drug Development, 2015)
Jordan C. Brooks, Alan S. Go, Daniel E. Singer, and Mark J. van der LaanSafety analysis involves assessing the causal effect of a treatment or exposure regimen on one or more outcomes of interest based on observing a sample of subjects over time. The observed data might constitute an observational study or a randomized controlled trial or even a collection of observational or randomized studies. Since randomized trials are typically not poweredCONTENTS10.1 Introduction and Motivation ... 173 10.2 Roadmap for Safety Analysis ... 17710.2.1 Observed Data Structure ... 177 10.2.2 Structural Causal Model ... 177 10.2.3 Causal Quantities ... 178 10.2.4 Identiability Result ... 179 10.2.5 Statistical Model, Statistical Target Parameter,and Estimation Problem ... 179 10.2.6 Inuence Curve for Inference ... 180 10.2.7 Multiple Testing Based on Joint Distribution ... 180 10.2.8 Sensitivity Analysis ... 18110.3 TMLE ... 181 10.3.1 Super Learning ... 18210.4 TMLE of Intervention-Specic Mean ... 183 10.5 Safety Analysis of Warfarin: The Causal Effect on NonstrokeDeath in Atrial Fibrillation ... 185 10.5.1 ATRIA-1 Cohort Data ... 186 10.5.2 Causal Effect of Warfarin Parameter ... 187 10.5.3 TMLE Implementation ... 187 10.5.4 Results ... 18710.6 Concluding Remark ... 189 References ... 189for safety outcomes, it is not uncommon that one combines various randomized studies, possibly augmented with observational studies, in order to evaluate safety signals in the data.