Epidemiological researchers often examine associations between risk factors and health outcomes in non-experimental designs. Observed associations may be causal or confounded by unmeasured factors. Sibling and co-twin control studies account for familial confounding by comparing exposure levels among siblings (or twins). If the exposure-outcome association is causal, the siblings should also differ regarding the outcome. However, such studies may sometimes introduce more bias than they alleviate. Measurement error in the exposure may bias results and lead to erroneous conclusions that truly causal exposure-outcome associations are confounded by familial factors. The current study used Monte Carlo simulations to examine bias due to measurement error in sibling control models when the observed exposure-outcome association is truly causal. The results showed that decreasing exposure reliability and increasing sibling-correlations in the exposure led to deflated exposure-outcome associations and inflated associations between the family mean of the exposure and the outcome. The risk of falsely concluding that causal associations were confounded was high in many situations. For example, when exposure reliability was 0.7 and the observed sibling-correlation was r = 0.4, about 30–90% of the samples (n = 2,000) provided results supporting a false conclusion of confounding, depending on how p-values were interpreted as evidence for a family effect on the outcome. The current results have practical importance for epidemiological researchers conducting or reviewing sibling and co-twin control studies and may improve our understanding of observed associations between risk factors and health outcomes. We have developed an app (SibSim) providing simulations of many situations not presented in this paper.

流行病学研究人员经常在非实验设计中检查危险因素与健康结果之间的关联。观察到的关联可能是因果关系，或者被未测量的因素所混淆。兄弟姐妹和同卵双胞胎对照研究通过比较兄弟姐妹（或双胞胎）之间的暴露水平来解释家庭混杂因素。如果暴露与结果之间存在因果关系，那么兄弟姐妹对于结果也应该有不同的看法。然而，此类研究有时可能会引入更多的偏见，而不是缓解的偏见。暴露中的测量误差可能会使结果产生偏差，并导致错误的结论，即真正的因果暴露-结果关联被家庭因素所混淆。当前的研究使用蒙特卡罗模拟来检查当观察到的暴露-结果关联确实存在因果关系时，兄弟控制模型中的测量误差导致的偏差。结果表明，暴露可靠性的降低和暴露中兄弟姐妹相关性的增加导致暴露与结果之间的关联性减弱，以及暴露的家庭均值与结果之间的关联性增大。在许多情况下，错误地得出因果关联被混淆的结论的风险很高。例如，当暴露可靠性为 0.7 并且观察到的兄弟相关性为 r = 0.4 时，大约 30-90% 的样本 (n = 2,000) 提供的结果支持混杂的错误结论，具体取决于 p 值如何解释为家庭对结果影响的证据。目前的结果对于流行病学研究人员进行或审查兄弟姐妹和同卵双胞胎对照研究具有实际意义，并可能提高我们对观察到的风险因素与健康结果之间关联的理解。 我们开发了一个应用程序 (SibSim)，可以模拟本文中未介绍的许多情况。