Conference Program

Estimands in Observational Studies: Some Considerations Beyond ICH E9 (R1) (303732)

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Wei-chen Chen, FDA/CDRH 
*Heng Li, FDA/CDRH 
Nelson Lu, FDA/CDRH 
Changhong Song, FDA/CDRH 
Ram Tiwari, Bristol Myers Squibb 
Chenguang Wang, Johns Hopkins University 
Yunling Xu, FDA/CDRH 
Lilly Yue, US Food and Drug Administration 

Keywords: Rubin Causal Model, ATE, ATT, ATO

In this talk we draw attention to one facet of estimand that is not discussed in ICH E9 (R1) but is crucial in the context of observational studies, namely weighting for causal inference. How weighting schemes are connected to estimand, or more specifically to one of its five attributes identified in ICH E9 (R1), the attribute of population, is illustrated using the Rubin Causal Model. Three estimands are examined from both theoretical and practical perspectives. Factors that may be considered in choosing among these estimands are discussed.