Using Historical Controls to Adjust for Covariates in Trend Tests for Binary Data
本文提出一种利用历史对照信息调整协变量效应的趋势检验方法，适用于啮齿动物致癌性研究等二元数据分析，并开发了计算检验统计量的新算法。
Abstract Historical data often play an important role in helping interpret the results of a current study. This article is motivated primarily by one specific application: the analysis of data from rodent carcinogenicity studies. By proposing a suitable informative prior distribution on the relationship between control outcome data and covariates, we derive modified trend test statistics that incorporate historical control information to adjust for covariate effects. Frequentist and fully Bayesian methods are presented, and novel computational techniques are developed to compute the test statistics. Several attractive theoretical and computational properties of the proposed priors are derived. In addition, a semiautomatic elicitation scheme for the priors is developed. Our approach is used to modify a widely used prevalence test for carcinogenicity studies. The proposed methodology is applied to data from a National Toxicology Program carcinogenicity experiment and is shown to provide helpful insight on the results of the analysis. Key Words: Gibbs samplingHistorical dataLikelihood ratio testLogistic regressionPosterior distributionPrior distributionScore test.