title: A mixture model using likelihood-based and Bayesian approaches for identifying responders and non-responders in longitudinal clinical trials.
abstract: A longitudinal mixture model for classifying patients into responders and non-responders is established using both likelihood-based and Bayesian approaches. The model takes into consideration responders in the control group. Therefore, it is especially useful in situations where the placebo response is strong, or in equivalence trials where the drug in development is compared with a standard treatment. Under our model, a treatment shows evidence of being effective if it increases the proportion of responders or increases the response rate among responders in the treated group compared with the control group. Therefore, the model has flexibility to accommodate different situations. The proposed method is illustrated using simulation and a depression clinical trial dataset for the likelihood-based approach, and the same depression clinical trial dataset for the Bayesian approach. The likelihood-based and Bayesian approaches generated consistent results for the depression trial data. In both the placebo group and the treated group, patients are classified into two components with distinct response rate. The proportion of responders is shown to be significantly higher in the treated group compared with the control group, suggesting the treatment paroxetine is effective.
authors.affiliation: Department of Biostatistics, University of Pennsylvania School of Medicine, Philadelphia, PA, USA.
authors.forename: Jiwei
authors.initials: J
authors.lastname: He
authors.forename: Richard
authors.initials: R
authors.lastname: Entsuah
mesh_terms: D001499:Bayes Theorem; D017322:Clinical Trials, Phase II as Topic / Q000706:statistics & numerical data*; D006801:Humans; D016013:Likelihood Functions; D008137:Longitudinal Studies; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D016896:Treatment Outcome
keywords: Gibbs sampler; expectation maximization; longitudinal clinical trial; mixed effects mixture model