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Principal stratification is a causal framework to analyze randomizedexperiments with a post-treatment variable between the treatment and endpointvariables. Because the principal strata defined by the potential outcomes ofthe post-treatment variable are not observable, we generally cannot identifythe causal effects within principal strata. Motivated by a real data set ofphase III adjuvant colon clinical trials, we propose approaches to identifyingand estimating the principal causal effects via multiple trials. For theidentifiability, we remove the commonly-used exclusion restriction assumptionby stipulating that the principal causal effects are homogeneous across thesetrials. To remove another commonly-used monotonicity assumption, we give anecessary condition for the local identifiability, which requires at leastthree trials. Applying our approaches to the data from adjuvant colon clinicaltrials, we find that the commonly-used monotonicity assumption is untenable,and disease-free survival with three-year follow-up is a valid surrogateendpoint for overall survival with five-year follow-up, which satisfies boththe causal necessity and the causal sufficiency. We also propose a sensitivityanalysis approach based on Bayesian hierarchical models to investigate theimpact of the deviation from the homogeneity assumption.

Principal causal effect identification and surrogate endpoint evaluation by multiple trials