Estimation and inference for the causal effect of receiving treatment on a multinomial outcome: an alternative approach.
Recently, Cheng (2009, Biometrics 65, 96-103) proposed a model for the causal effect of receiving treatment when there is all-or-none compliance in one randomization group, with maximum likelihood estimation based on convex programming. We discuss an alternative approach that involves a model for all-or-none compliance in two randomization groups and estimation via a perfect fit or an expectation-maximization algorithm for count data. We believe this approach is easier to implement, which would facilitate the reproduction of calculations.
Baker
Stuart G
SG
Biometry Research Group, Division of Cancer Prevention, National Cancer Institute, EPN 3131, 6130 Executive Blvd MSC 7354, Bethesda, Maryland 20892-7354, USA. sb16i@nih.gov
D000368:Aged; D000928:Antidepressive Agents / Q000627:therapeutic use; D015984:Causality; D003863:Depression / Q000188:drug therapy; D006801:Humans; D016013:Likelihood Functions; D010349:Patient Compliance; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D016896:Treatment Outcome
D000928:Antidepressive Agents