Causal inference in a clinical trial: a comparative example.
Recently there has been much interest in methods for analyzing clinical trials of treatments that are subject to noncompliance. In this paper I study a small, simple dataset from a clinical trial of immunosuppressive therapy in the treatment of multiple sclerosis. I apply and compare a range of methods: the as-randomized (intention-to-treat) analysis, the as-treated analysis, estimates based on a nonignorable selection model, and Rubin's causal model. The results differ substantially even in this small dataset that exhibits modest noncompliance. For this reason, data analysts should be clear about which parameters are of greatest importance in the analysis of a clinical trial.
Heitjan
D F
DF
Division of Biostatistics, Joseph L. Mailman School of Public Health, Columbia University, New York, New York 10032, USA. dfh5@columbia.edu
D001379:Azathioprine / Q000008:administration & dosage* / Q000009:adverse effects; D015982:Bias; D004305:Dose-Response Relationship, Drug; D004334:Drug Administration Schedule; D004359:Drug Therapy, Combination; D006801:Humans; D007166:Immunosuppressive Agents / Q000008:administration & dosage* / Q000009:adverse effects; D008775:Methylprednisolone / Q000008:administration & dosage* / Q000009:adverse effects; D015233:Models, Statistical; D009103:Multiple Sclerosis / Q000188:drug therapy*; D010352:Patient Dropouts / Q000706:statistics & numerical data; D016312:Treatment Refusal / Q000706:statistics & numerical data*
D007166:Immunosuppressive Agents; D001379:Azathioprine; D008775:Methylprednisolone