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Statistical Inference for Two-Sample and Regression Models with Heterogeneity Effect: A Collected-Sample Perspective

In: Probability, Statistics and Modelling in Public Health

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  • Hong-Dar Isaac Wu

    (China Medical University, School of Public Health)

Abstract

Summary Heterogeneity effect is an important issue in the analysis of clinical trials, survival data, and epidemiological cohort studies. This article reviews the works of inference for heterogeneity effect from a series of works by Hsieh who used the empirical process approach, and relevant works by Bagdonavičius, Nikulin, and coworkers. This includes two-sample models and Cox-type relative risk regression models. Heterogeneity property over the covariate space as well as non-constancy property are discussed for several models. In survival analysis, the log-relative risk as a function of time and of the covariates are plotted to present the heterogeneity property of Hsieh’s and Bagdonavicius and Nikulin’s hazards regression models.

Suggested Citation

  • Hong-Dar Isaac Wu, 2006. "Statistical Inference for Two-Sample and Regression Models with Heterogeneity Effect: A Collected-Sample Perspective," Springer Books, in: Mikhail Nikulin & Daniel Commenges & Catherine Huber (ed.), Probability, Statistics and Modelling in Public Health, pages 452-465, Springer.
    • Handle: RePEc:spr:sprchp:978-0-387-26023-5_31
    DOI: 10.1007/0-387-26023-4_31
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