Confounding by comorbidities is of concern in many epidemiological studies. To take this into account a common strategy is to calculate each participant’s Charlson Comorbidity Index (CCI) and use this for adjustment in regression analyses. Various CCI adjustment strategies are possible, and it is unclear, which is preferable. In this simulation study, we compared common adjustment strategies in Cox regression analyses to determine to which degree they mitigate confounding and conservative bias caused by missing adjustment for independent predictors. We found that adjustment for each comorbidity as separate dichotomous covariate is the preferable adjustment strategy in samples of sufficient size as this mitigates both bias sources to the largest degree. If this is impractical in smaller studies adjustment for CCI split into multiple categories is preferable. In conclusion, the choice of CCI adjustment strategy impacts mitigation of bias in this simulation study, and suboptimal adjustment strategies can cause an observable bias, although of quite limited magnitude of only a few percent in this simulation example. Researcher should be careful when deciding on the adjustment strategies applied to ensure that the desired mitigation of bias sources is achieved.

许多流行病学研究都关注合并症的混淆。考虑到这一点，一个常见的策略是计算每个参与者的查尔森合并症指数 (CCI)，并将其用于回归分析中的调整。各种CCI调整策略都是可能的，目前还不清楚，哪个更可取。在此模拟研究中，我们比较了 Cox 回归分析中的常见调整策略，以确定它们在多大程度上减轻了因缺少独立预测变量调整而导致的混杂和保守偏差。我们发现，在足够大的样本中，将每种合并症作为单独的二分协变量进行调整是优选的调整策略，因为这可以最大程度地减轻两种偏差来源。如果这在较小的研究中不切实际，则最好将 CCI 分为多个类别进行调整。总之，在本模拟研究中，CCI 调整策略的选择会影响偏差的缓解，次优调整策略可能会导致可观察到的偏差，尽管在本模拟示例中其幅度非常有限，只有几个百分点。研究人员在决定采用何种调整策略以确保实现预期的偏差源缓解时应谨慎。