Mean Response Models of Repeated Measurements in Presence of Varying Effectiveness Onset

Ying Chen and Su-Chun Cheng
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Ying Chen: Division of Biostatistics, School of Public Health, University of California, Berkeley
Su-Chun Cheng: Department of Epidemiology & Biostatistics, University of California, San Francisco

No 1148, U.C. Berkeley Division of Biostatistics Working Paper Series from Berkeley Electronic Press

Abstract: Repeated measurements are often collected over time to evaluate treatment efficacy in clinical trials. Most of the statistical models of the repeated measurements have been focusing on their mean response as function of time. These models usually assume that the treatment has persistent effect of constant additivity or multiplicity on the mean response functions throughout the observation period of time. In reality, however, such assumption may be confounded by the potential existence of the so-called effectiveness action onset, although they are often unobserved or difficult to obtain. Instead of including nonparametric time-varying coefficients in the mean response models, we propose and study some semiparametric mean response models to accommodate such effectiveness times. Our methodologies will be demonstrated by a real randomised clinical trial data.

Keywords: Action onset; clinical trials; longitudinal outcomes; mean response model; semiparametric analysis; time-varying coefficient (search for similar items in EconPapers)
Date: 2004-07-11
Note: oai:bepress.com:ucbbiostat-1148
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