An empirical Bayes formulation of cohort models in cancer epidemiology.
An empirical Bayes formulation of cohort models in cancer epidemiology.
复制标题
癌症流行病学队列模型的经验贝叶斯公式。
DOI：
10.1002/sim.4780100807
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发表时间：
1991
影响因子：
2
通讯作者：
Desouza,CM
中科院分区：
文献类型：
--
作者：
Desouza,CM
本文关注三个地理区域几个年龄、性别组和时间段的恶性皮肤黑色素瘤的发病率，采用队列分析方法并采用两阶段随机效应模型。第一阶段假设固定年龄性别群体的疾病发病率的区域内变化呈泊松分布，其平均值与高危人群成正比。第二阶段，在调整年龄和性别后，需要假设真实发病率对数的区域间地理变化具有通过最大似然法估计的参数的先验分布。调整年龄效应后，我们使用经验贝叶斯方法估计每个性别的随机地理特定队列效应，并将结果与通常的乘法泊松模型进行比较，该模型假设每个性别都有固定的地理特定队列效应。这种比较表明，此处提出的方法提供了对特定地理群组效应的更稳定的估计，此外，随机效应模型更充分地描述了这些数据。
This paper concerns the incidence rates of malignant skin melanoma for several age‐sex groups and time periods in three geographic regions, uses a method of cohort analysis and employs a two‐stage random effects model. The first stage entails the assumption that the within‐region variation in the frequency of disease incidence for a fixed age‐sex‐cohort group has a Poisson distribution with mean proportional to the population at risk. The second stage, after adjusting for age and sex, entails the assumption that the between‐region geographic variation in the logarithm of the true incidence rate has a prior distribution with parameters estimated by the method of maximum likelihood. After adjusting for age effects, we estimate random geographic‐specific cohort effects for each sex with use of an empirical Bayes method and compare the results with the usual multiplicative Poisson model that assumes fixed geographic‐specific cohort effects for each sex. This comparison shows that the method presented here provides more stable estimates of geographic‐specific cohort effects, and in addition the random effects model describes these data more adequately.