A semiparametric estimate of treatment effects with censored data.
A semiparametric estimate of an average regression effect with right-censored failure time data has recently been proposed under the Cox-type model where the regression effect beta(t) is allowed to vary with time. In this article, we derive a simple algebraic relationship between this average regression effect and a measurement of group differences in k-sample transformation models when the random error belongs to the G(rho) family of Harrington and Fleming (1982, Biometrika 69, 553-566), the latter being equivalent to the conditional regression effect in a gamma frailty model. The models considered here are suitable for the attenuating hazard ratios that often arise in practice. The results reveal an interesting connection among the above three classes of models as alternatives to the proportional hazards assumption and add to our understanding of the behavior of the partial likelihood estimate under nonproportional hazards. The algebraic relationship provides a simple estimator under the transformation model. We develop a variance estimator based on the empirical influence function that is much easier to compute than the previously suggested resampling methods. When there is truncation in the right tail of the failure times, we propose a method of bias correction to improve the coverage properties of the confidence intervals. The estimate, its estimated variance, and the bias correction term can all be calculated with minor modifications to standard software for proportional hazards regression.
Xu
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Department of Biostatistics, Harvard School of Public Health and Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA. rxu@jimmy.harvard.edu
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D001699:Biometry; D016001:Confidence Intervals; D006801:Humans; D016013:Likelihood Functions; D008175:Lung Neoplasms / Q000401:mortality; D008228:Lymphoma, Non-Hodgkin / Q000401:mortality; D015233:Models, Statistical; D011379:Prognosis; D016016:Proportional Hazards Models; D012044:Regression Analysis; D016019:Survival Analysis