|
Abstract:
|
In clinical trials, measurements or endpoints in various domains are assessed and usually combined to evaluate the totality of the treatment effect of a specific treatment. This strategy is common in neurological disease area where many patient performance assessments have been developed. The most common and simplest way to combine multiple measures is through constructing a composite endpoint when all outcome measures are binary or can be dichotomized, and an event occurrence is defined as if any of the component outcome measures achieves an event. Alternatively, the overall evaluation can be achieved analytically by analyzing each outcome measure separately, e.g., through multivariate regression. In literature, there is no systematic evaluation of these various approaches and performance comparisons. In this project, we first propose two general frameworks to combine multiple measures, a composite endpoint approach and a model-based approach. Statistical properties of the approaches are then evaluated using disability improvement in multiple sclerosis as an example. We finally illustrate our methodology through simulations and an application to a motivating clinical trial data.
|
