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Estimation and inference for the causal effect of receiving treatment on a multinomial outcome: an alternative approach.

ABSTRACT: 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.

SUBMITTER: Baker SG

PROVIDER: S-EPMC3030650 | biostudies-literature | 2011 Mar

REPOSITORIES: biostudies-literature

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Estimation and inference for the causal effect of receiving treatment on a multinomial outcome: an alternative approach.

Baker Stuart G SG

Biometrics 20110301 1

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 ...[more]

PMID: 20560933

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