
Summary: We consider a class of semi-parametric transformation models, under which an unknown transformation of the survival time is linearly related to the covariates with various completely specified error distributions. This class of regression models includes the proportional hazards and proportional odds models. Inference procedures derived from a class of generalised estimating equations are proposed to examine the covariate effects with censored observations. Numerical studies are conducted to investigate the properties of our proposals for practical sample sizes. These transformation models, coupled with the new simple inference procedures, provide many useful alternatives to the Cox regression model in survival analysis.
Asymptotic distribution theory in statistics, martingale, generalised estimating equations, proportional odds models, proportional hazards, Applications of statistics to biology and medical sciences; meta analysis, survival analysis, censored observations, semi-parametric transformation models, completely specified error distributions, U-statistics, covariate effects, Nonparametric estimation
Asymptotic distribution theory in statistics, martingale, generalised estimating equations, proportional odds models, proportional hazards, Applications of statistics to biology and medical sciences; meta analysis, survival analysis, censored observations, semi-parametric transformation models, completely specified error distributions, U-statistics, covariate effects, Nonparametric estimation
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