Dataset for: Proportional hazards regression of survival-sacrifice data with cause-of-death information in animal carcinogenicity studies
datasetposted on 01.08.2019 by Lu Mao
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
Rodent survival-sacrifice experiments are routinely conducted to assess the tumor-inducing potential of a certain exposure or drug. Because most tumors under study are impalpable, animals are examined at death for evidence of tumor formation. In some studies, the cause of death is ascertained by a pathologist to account for possible correlation between tumor development and death. Existing methods for survival-sacrifice data with cause-of-death information have been restricted to multi-group testing or one-sample estimation of tumor onset distribution and thus do not provide a natural way to quantify treatment effect or dose-response relationship. In this paper, we propose semiparametric regression methods under the popular proportional hazards model for both tumor onset and tumor-caused death. For inference, we develop a maximum pseudo-likelihood estimation procedure using a modified iterative convex minorant algorithm, which is guaranteed to converge to the unique maximizer of the objective function. Simulation studies under different tumor rates show that the new methods provide valid inference on the covariate-outcome relationship and outperform alternative approaches. A real study investigating the effects of benzidine dihydrochloride on liver tumor in mice is analyzed as an illustration.