Selecting Therapeutic Strategies Based on Efficacy and Death in Multicourse Clinical Trials

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Abstract
Therapy of rapidly fatal diseases often requires multiple courses of treatment. In each course, the treatment may achieve the desired clinical goal (“response”), the patient may survive without response (“failure”), or the patient may die. When treatment fails in a given course, it is common medical practice to switch to a different treatment for the next course. Most statistical approaches to such settings simply ignore the multicourse structure. They characterize patient outcome as a single binary variable, combine death and failure, and identify only one treatment for each patient. Such approaches waste important information. We provide a statistical framework, including a family of generalized logistic regression models and an approximate Bayesian method, that incorporates historical data while accommodating multiple treatment courses, a trinary outcome in each course, and patient prognostic covariates. The framework serves as a basis for data analysis, treatment evaluation, and clinical trial design....

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