Entsuah Anthony Richard
Merck & Co., Inc. USA
Title: A mixture model using likelihood-based and Bayesian approaches for identifying responders and non-responders in longitudinal clinical trials
Biography
Biography: Entsuah Anthony Richard
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. 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 to 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 as shown to be significantly higher in the treated group compared to the control group suggesting the treatment paroxetine is effective.
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