Estimating a population cumulative incidence under calendar time trends
Estimating a population cumulative incidence under calendar time trends
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DOI：
10.1186/s12874-016-0280-6
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发表时间：
2017-01-11
影响因子：
4
通讯作者：
Parner, Erik T.
中科院分区：
文献类型：
--
作者：
Hansen, Stefan N.;Overgaard, Morten;Parner, Erik T.
背景：疾病或精神障碍的风险通常通过特定年龄的累积发病率来衡量。累积发病率估计通常是在队列研究中得出的，该研究在日历时间内招募个体，并根据特定日期进行随访结束。通常的做法是将 Kaplan-Meier 或 Aalen-Johansen 估计量应用于总样本，并报告估计的累积发病率曲线或仅报告曲线上的单个点作为疾病风险的描述。方法：我们认为，每当感兴趣的疾病或病症受到日历时间趋势的影响时，总样本 Kaplan-Meier 和 Aalen-Johansen 估计量并不能提供对目标人群中一般风险的有用估计。我们提出了此类分析的一些替代方案。结果：我们展示了如果比例是合理的假设，则如何使用比例风险模型来推断疾病风险估计。如果不合理，我们反而主张对疾病风险的更有用的描述在于由进入时间或可能只是所有阶层的后续估计结束时给出的各阶层的特定年龄累积发病率曲线。最后，我们认为，这些随访结束估计的加权平均值可能是研究期间疾病风险的有用汇总衡量标准。结论：疾病风险的时间趋势将使总样本估计量在交错进入和行政审查的观察性研究中不太有用。基于比例风险的分析或分层分析可能是更好的选择。
Background: The risk of a disease or psychiatric disorder is frequently measured by the age-specific cumulative incidence. Cumulative incidence estimates are often derived in cohort studies with individuals recruited over calendar time and with the end of follow-up governed by a specific date. It is common practice to apply the Kaplan-Meier or Aalen-Johansen estimator to the total sample and report either the estimated cumulative incidence curve or just a single point on the curve as a description of the disease risk.Methods: We argue that, whenever the disease or disorder of interest is influenced by calendar time trends, the total sample Kaplan-Meier and Aalen-Johansen estimators do not provide useful estimates of the general risk in the target population. We present some alternatives to this type of analysis.Results: We show how a proportional hazards model may be used to extrapolate disease risk estimates if proportionality is a reasonable assumption. If not reasonable, we instead advocate that a more useful description of the disease risk lies in the age-specific cumulative incidence curves across strata given by time of entry or perhaps just the end of follow-up estimates across all strata. Finally, we argue that a weighted average of these end of follow-up estimates may be a useful summary measure of the disease risk within the study period.Conclusions: Time trends in a disease risk will render total sample estimators less useful in observational studies with staggered entry and administrative censoring. An analysis based on proportional hazards or a stratified analysis may be better alternatives.