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Abstract：
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.
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.
摘要：
以人口为基础的调查是可能的来源，从中得出有代表性的控制数据，用于病例对照研究。然而,这些调查涉及复杂的抽样，如果在分析中没有正确说明，可能会导致对关联度量的估计有偏差。尚未研究将复杂采样控制纳入密度采样病例控制设计的方法。
我们使用模拟研究来评估从病例对照研究中估计发病率密度比（IDR）的不同方法的性能，并使用风险集抽样从复杂的调查数据中提取对照。在模拟人口数据中，我们采用了四种调查抽样方法，随着测量大小的变化，并评估了纳入基于调查的控制的四种分析方法的性能。
对于进行风险集抽样的方法，IDR的估计是无偏的，选择概率与调查权重成正比。当没有纳入抽样权重时，IDR的估计是有偏差的，或仅包含在回归建模中。无偏分析方法进行比较，并产生方差与有偏方法相当的估计。随着调查规模的减小，方差增加，置信区间覆盖率降低。
在风险集抽样病例对照研究中，使用从复杂调查数据中提取的对照，当权重适当合并时，可以获得无偏估计。
我们使用模拟研究来评估从病例对照研究中估计发病率密度比（IDR）的不同方法的性能，并使用风险集抽样从复杂的调查数据中提取对照。在模拟人口数据中，我们采用了四种调查抽样方法，随着测量大小的变化，并评估了纳入基于调查的控制的四种分析方法的性能。
对于进行风险集抽样的方法，IDR的估计是无偏的，选择概率与调查权重成正比。当没有纳入抽样权重时，IDR的估计是有偏差的，或仅包含在回归建模中。无偏分析方法进行比较，并产生方差与有偏方法相当的估计。随着调查规模的减小，方差增加，置信区间覆盖率降低。
在风险集抽样病例对照研究中，使用从复杂调查数据中提取的对照，当权重适当合并时，可以获得无偏估计。
