Causal Inference in a Placebo-Controlled Clinical Trial with Binary Outcome and Ordered Compliance

• Published in: Journal of the American Statistical Association Vol. 91; no. 435; pp. 928 - 934
• Main Authors: Goetghebeur, Els, Molenberghs, Geert
• Format: Journal Article
• Language: English
• Published: Alexandria, VA Taylor & Francis Group 01.09.1996; American Statistical Association; Taylor & Francis Ltd
• Subjects: Applications and Case Studies; Cholesterols; Clinical trials; Coarsening; Compliance certificates; Counterfactual framework; Dosage; Dose response relationship; Efficacy; Exact sciences and technology; Intention to treat; Interval censoring; Lipids; Mathematics; Nonparametric inference; Ordinal data; Parametric inference; Parametric models; Patient compliance; Placebos; Preventive medicine; Probability and statistics; Sciences and techniques of general use; Securities and Exchange Commission regulation; Statistical methods; Statistics; Treatment compliance; Causal inference; Parameter estimation; Efficiency; Binary outcome; Placebo; Clinical trial; Non parametric estimation; Likelihood method; Ordered compliance
• ISSN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.1996.10476962

We propose a likelihood-based method to analyze the causal effect of partial compliance (i.e., unplanned partial exposure to treatment or placebo) in the LRC-CPPT data, a prevention trial with long term follow-up previously analyzed by Efron and Feldman. Initially, we construct ordered compliance categories and dichotomize response. Assuming increased exposure to cholestyramine does not increase cholesterol, we estimate exposure-response curves in different compliance subsets. Subjects in different arms with similar levels of compliance to the assignment may have a different placebo prognosis (i.e., success probability under a possible zero exposure level). The sole assumption that the placebo group reflects response to zero exposure for the treatment group as a whole allows estimation of a causal parameter in a special case only. When a single parameter represents the association between responses to possible treatment exposures and treatment compliance, simple estimates are derived for a set of causal parameters. The example is analyzed in detail, and more general applicability and extensions of the method are discussed.