Division of Epidemiology & Biostatistics, School of Public Health, University of California, Berkeley, CA; Department of Epidemiology, University of North Carolina, Chapel Hill, NC.
Center for Health and Community, University of California, San Francisco, CA.
Ann Epidemiol. 2022 Jan;65:109-115. doi: 10.1016/j.annepidem.2021.06.019. Epub 2021 Jul 1.
Population-based surveys are possible sources from which to draw representative control data for case-control studies. However, these surveys involve complex sampling that could lead to biased estimates of measures of association if not properly accounted for in analyses. Approaches to incorporating complex-sampled controls in density-sampled case-control designs have not been examined.
We used a simulation study to evaluate the performance of different approaches to estimating incidence density ratios (IDR) from case-control studies with controls drawn from complex survey data using risk-set sampling. In simulated population data, we applied four survey sampling approaches, with varying survey sizes, and assessed the performance of four analysis methods for incorporating survey-based controls.
Estimates of the IDR were unbiased for methods that conducted risk-set sampling with probability of selection proportional to survey weights. Estimates of the IDR were biased when sampling weights were not incorporated, or only included in regression modeling. The unbiased analysis methods performed comparably and produced estimates with variance comparable to biased methods. Variance increased and confidence interval coverage decreased as survey size decreased.
Unbiased estimates are obtainable in risk-set sampled case-control studies using controls drawn from complex survey data when weights are properly incorporated.
基于人群的调查可以作为病例对照研究的代表性对照数据的来源。然而，如果在分析中没有正确考虑到这些调查中涉及的复杂抽样，那么这些调查可能会导致关联度量的估计值存在偏差。目前尚未研究如何将复杂抽样的对照纳入密度抽样病例对照设计中。
我们使用模拟研究来评估不同方法在使用风险集抽样从复杂调查数据中抽取对照的病例对照研究中估计发病率密度比 (IDR) 的性能。在模拟人群数据中，我们应用了四种具有不同调查规模的调查抽样方法，并评估了四种用于纳入基于调查的对照的分析方法的性能。
对于采用与调查权重成比例的选择概率进行风险集抽样的方法，IDR 的估计值是无偏的。当未纳入抽样权重或仅纳入回归建模时，IDR 的估计值存在偏差。无偏分析方法的性能相当，产生的估计值与有偏方法的方差相当。随着调查规模的减小，方差增加，置信区间覆盖范围减小。
当正确纳入权重时，可以从复杂调查数据中抽取对照的风险集抽样病例对照研究中获得无偏估计值。
