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动物致瘤性数据的简单参数分析AI 解读

The usual onset/death model for tumorigenicity data can be characterized by the tumor incidence rate and the conditional death rates for tumor-free and tumor-bearing animals. If the tumor is unobservable in live animals, however, these rates are difficult to estimate without cause-of-death data or numerous sacrifices. This article proposes simple but flexible parametric models for another set of functions that can be estimated directly from survival/sacrifice data. These…

部分删失数据的半马尔可夫模型札记AI 解读

A recharacterization of the semi-Markov model described by Lagakos, Sommer & Zelen (1978) permits the nonparametric maximum likelihood estimators to be expressed in terms of familiar and easily computable quantities, such as event-specific hazard estimators and Kaplan & Meier (1958) survival estimators. In addition to simplifying the calculation and clarifying the interpretation of the previously derived estimators, this reformulation also facilitates the estimation of…

死因数据不完整时动物实验患病率与死亡率的非参数估计AI 解读

Age-specific prevalence and mortality estimators are important descriptors of disease development and the subsequent effects of a disease on longevity. Nonparametric maximum likelihood estimators for the prevalence and mortality functions are available for the case in which incidental and fatal occurrences of a disease can always be distinguished. This article generalizes these methods to allow the role of a disease in causing death to be uncertain for a subset of the…

部分删失数据的半马尔可夫模型札记AI 解读

A recharacterization of the semi-Markov model described by Lagakos, Sommer & Zelen (1978) permits the nonparametric maximum likelihood estimators to be expressed in terms of familiar and easily computable quantities, such as event-specific hazard estimators and Kaplan & Meier (1958) survival estimators. In addition to simplifying the calculation and clarifying the interpretation of the previously derived estimators, this reformulation also facilitates the estimation of…