Principal Stratification in Causal Inference

• Published in: Biometrics Vol. 58; no. 1; pp. 21 - 29
• Main Authors: Frangakis, Constantine E., Rubin, Donald B.
• Format: Journal Article
• Language: English
• Published: Oxford, UK Blackwell Publishing Ltd 01.03.2002; International Biometric Society
• Subjects: Biological markers; Biomarker; biomarkers; Biometrics; biometry; Biostatistics; Causal inference; Censoring by death; Censorship; Child; Clinical trials; Contrafactuals; death; Health outcomes; Humans; Inference; Missing data; Models, Statistical; Patient Dropouts; Posttreatment variable; Principal stratification; Quality of life; Randomized Controlled Trials as Topic - methods; Rubin causal model; Surrogate; Treatment compliance; Treatment Outcome; Treatment Refusal; Vitamin A; Vitamin A - administration & dosage
• ISSN: 0006-341X; 1541-0420
• DOI: 10.1111/j.0006-341X.2002.00021.x

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.