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


  • 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)


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.

Suggested Citation

  • Ying Chen & Su-Chun Cheng, 2004. "Mean Response Models of Repeated Measurements in Presence of Varying Effectiveness Onset," U.C. Berkeley Division of Biostatistics Working Paper Series 1148, Berkeley Electronic Press.
  • Handle: RePEc:bep:ucbbio:1148

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    References listed on IDEAS

    1. Chiang C-T. & Rice J. A & Wu C. O, 2001. "Smoothing Spline Estimation for Varying Coefficient Models With Repeatedly Measured Dependent Variables," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 605-619, June.
    2. Lin D Y & Ying Z, 2001. "Semiparametric and Nonparametric Regression Analysis of Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 103-126, March.
    3. Y. Q. Chen, 2002. "Additive hazards models with latent treatment effectiveness lag time," Biometrika, Biometrika Trust, vol. 89(4), pages 917-931, December.
    4. Yatchew, A., 1997. "An elementary estimator of the partial linear model," Economics Letters, Elsevier, vol. 57(2), pages 135-143, December.
    5. J. Sun & L. J. Wei, 2000. "Regression analysis of panel count data with covariate‚Äźdependent observation and censoring times," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(2), pages 293-302.
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