The majority of my research on this project was performed in two areas: (1) censored failure-time analysis when some censoring indicators are missing and (2) general analysis of animal carcinogenicity studies. These two areas of research are described in more detail below. Area 1: Failure-time data are typically subject to censoring, such as when a study ends before all participants fail. Additionally, when multiple causes of failure are operating, the time to failure from one cause can be censored by a failure from another cause. In some situations, the censoring indicator is missing for a subset of individuals, such as in a cancer bioassay when the pathologist is not able to determine the role of a tumor in causing death or when some records are incomplete. The analysis of failure-time data typically focuses on hazard functions. Under an accelerated failure-time model, we derived three nonparametric hazard estimators that are appropriate when some failure times are right censored and some censoring indicators are missing. Specifically, we developed a regression surrogate estimator, an imputation estimator, and an inverse probability weighted estimator. All three estimators use kernel smoothing techniques and enjoy certain large-sample properties such as uniform strong consistency and asymptotic normality. A simulation study showed that the proposed hazard estimators also performed well in small samples. We published an article describing this work (see reference 2). Area 2: A three-state stochastic model is often used to analyze carcinogenicity data from long-term rodent bioassays, such as the 2-year studies conducted by the NTP. The most appropriate analysis depends on many factors, such as whether: tumors are observable before death, tumors affect mortality, cause-of-death information is collected, random sacrifices are performed, parametric models are justified, functional constraints are reasonable, or supplemental information (e.g., historical data, additional covariates, expert opinions) is available. An article describing this work is scheduled to appear in December (see reference 1).

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Wang, Qihua; Dinse, Gregg E; Liu, Chunling (2012) Hazard Function Estimation with Cause-of-Death Data Missing at Random. Ann Inst Stat Math 64:415-438
Wang, Qihua; Dinse, Gregg E (2011) Linear regression analysis of survival data with missing censoring indicators. Lifetime Data Anal 17:256-79
Dinse, Gregg E; Peddada, Shyamal D (2011) Comparing tumor rates in current and historical control groups in rodent cancer bioassays. Stat Biopharm Res 3:97-105
Dinse, Gregg E; Peddada, Shyamal D; Harris, Shawn F et al. (2010) Comparison of NTP historical control tumor incidence rates in female Harlan Sprague Dawley and Fischer 344/N Rats. Toxicol Pathol 38:765-75
Song, Xinyuan; Sun, Liuquan; Mu, Xiaoyun et al. (2010) Additive hazards regression with censoring indicators missing at random. Can J Stat 38:333-351