Nonparametric Prevalence and Mortality Estimators for Animal Experiments with Incomplete Cause-of-Death Data
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 ↓
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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
- 研究问题
- 部分患病动物的死因归属不确定时，如何估计年龄别患病率和死亡率，并纳入处死动物提供的疾病观测信息？
- 方法与思路
- 作者扩展非参数最大似然方法，将患病率在时间区间内设为常数，用观测信息矩阵估计方差，并以 EM 处理部分约束。
- 材料陈述的贡献
- 摘要称方法允许疾病是否致死存在不确定性，无须设定疾病致死程度，并可纳入处死动物数据及患病率单调性约束。
- 阅读关注点
- 应核对无需处死数据时的代表性假设、死亡率与患病率的定义，以及区间划分、死因分类和 EM 算法的具体处理。
- 阅读边界
- 仅据版本未注明的 OpenAlex 索引摘要；非参数方法仍含代表性要求和分段稳定化设定，不能理解为完全无假设或推断刊本变化。
原文摘要
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 animals dying with the disease. The proposed analysis makes no assumptions about the degree of disease lethality. Data from sacrificed animals can be incorporated easily, although sacrifices are not necessary if a certain representativeness assumption holds. No restrictions are imposed on the distribution of survival times, but the prevalence function is held constant over time intervals for stabilization purposes. Variances are estimated by inverting the observed information matrix. An EM algorithm simplifies the analysis when the prevalence function is constrained to be monotone or when cause of death is classified into ordered categories, according to how likely it was that the disease was responsible for death. The proposed methods are illustrated with some data on nonrenal vascular disease in female RFM mice.