Background: Mendelian randomization (MR) provides unconfounded estimates. MR is open to selection bias when the underlying sample is selected on surviving to recruitment on the genetically instrumented exposure and competing risk of the outcome. Few methods to address this bias exist.

Methods: We show that this selection bias can sometimes be addressed by adjusting for common causes of survival and outcome. We use multivariable MR to obtain a corrected MR estimate for statins on stroke. Statins affect survival, and stroke typically occurs later in life than ischemic heart disease (IHD), making estimates for stroke open to bias from competing risk.

Results: In univariable MR in the UK Biobank, genetically instrumented statins did not protect against stroke [odds ratio (OR) 1.33, 95% confidence interval (CI) 0.80–2.20] but did in multivariable MR (OR 0.81, 95% CI 0.68–0.98) adjusted for major causes of survival and stroke [blood pressure, body mass index (BMI), and smoking initiation] with a multivariable Q-statistic indicating absence of selection bias. However, the MR estimate for statins on stroke using MEGASTROKE remained positive and the Q statistic indicated pleiotropy.

Conclusion: MR studies of harmful exposures on late-onset diseases with shared etiology need to be conceptualized within a mechanistic understanding so as to identify any potential bias due to survival to recruitment on both genetically instrumented exposure and competing risk of the outcome, which may then be investigated using multivariable MR or estimated analytically and results interpreted accordingly.

背景：孟德尔随机化 (MR) 提供无混杂的估计。当根据基因仪器暴露和结果的竞争风险来选择生存到招募的基础样本时，MR 会出现选择偏差。解决这种偏见的方法很少。

方法：我们表明，有时可以通过调整生存和结果的常见原因来解决这种选择偏差。我们使用多变量 MR 来获得他汀类药物治疗中风的校正 MR 估计值。他汀类药物会影响生存，而中风通常比缺血性心脏病 (IHD) 发生得晚，因此对中风的估计可能会因竞争风险而产生偏差。

结果：在英国生物银行的单变量 MR 中，基因检测他汀类药物不能预防中风 [比值比 (OR) 1.33，95% 置信区间 (CI) 0.80–2.20]，但在多变量 MR 中却可以预防中风（OR 0.81，95% CI 0.68–0.98） ）根据生存和中风的主要原因[血压、体重指数（BMI）和开始吸烟]进行调整，并使用多变量 Q 统计表明不存在选择偏差。然而，使用 MEGASTROKE 对他汀类药物治疗中风的 MR 估计仍然为正，并且 Q 统计表明多效性。

结论：对具有共同病因的迟发性疾病的有害暴露的 MR 研究需要在机械理解的范围内进行概念化，以便识别由于基因仪器暴露和结果竞争风险的招募生存而导致的任何潜在偏差，然后可以对其进行调查使用多变量 MR 或分析估计并相应地解释结果。