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There is increasing need to identify biomarkers (BMKs) responding early to drug treatment to help decision making during clinical development. One of the statistical metrics often involved in screening such BMKs from a single study is the assessment of correlation between a candidate BMK and a primary clinical endpoint. In this chapter, some drawbacks in relying on simple regression models for such an investigation will be criticized first, followed by a real example to demonstrate the danger of relying on static data to assess such a correlation. A theoretical justification will then be given to promote the idea of pursuing treatment-mediated correlation patterns. The rest of this paper will then be focused on how to estimate correlation under this preferred metric from data with parallel-group design and time-to-event (T2E) being the primary clinical endpoint. A jointly modeling framework of T2E and longitudinally measured BMK will then be introduced, with explanation in details how to parameterize the joint model and interpret some key parameters. By comparing the performances of three different models, the results from the analysis of an AIDS trial will be presented to demonstrate the benefit of joint modeling of T2E and BMK, followed by some brief discussions.
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- Assessment of Treatment-Mediated Correlation Between a Clinical Endpoint and a Biomarker
Peter H. Hu
- Springer New York