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PrincipalStratification inCausal Inference Constantine FrangakisDonald B. Rubin2002 год

Principal Stratification in Causal Inference
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Аннотация: Summary. Many scientific problems require that treatment comparisons be adjusted for posttreatment variables, but the estimands underlying standard methods are not causal effects. To address this deficiency, we propose a general framework for comparing treatments adjusting for posttreatment variables that yields principal effects based on principal stratification. Principal stratification with respect to a posttreatment variable is a cross‐classification of subjects defined by the joint potential values of that posttreatment variable under each of the treatments being compared. Principal effects are causal effects within a principal stratum. The key property of principal strata is that they are not affected by treatment assignment and therefore can be used just as any pretreatment covariate, such as age category. As a result, the central property of our principal effects is that they are always causal effects and do not suffer from the complications of standard posttreatment‐adjusted estimands. We discuss briefly that such principal causal effects are the link between three recent applications with adjustment for posttreatment variables: (i) treatment noncompliance, (ii) missing outcomes (dropout) following treatment noncompliance, and (iii) censoring by death. We then attack the problem of surrogate or biomarker endpoints, where we show, using principal causal effects, that all current definitions of surrogacy, even when perfectly true, do not generally have the desired interpretation as causal effects of treatment on outcome. We go on to formulate estimands based on principal stratification and principal causal effects and show their superiority.
Год издания: 2002
Авторы: Constantine Frangakis, Donald B. Rubin
Издательство: Oxford University Press
Источник: Biometrics
Ключевые слова: Advanced Causal Inference Techniques, Statistical Methods and Bayesian Inference, Statistical Methods and Inference
Другие ссылки: Biometrics (HTML)
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PubMed Central (HTML)
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