缺失数据和未测量的混杂因素是外部比较研究的主要挑战。这项工作通过两个案例研究和模拟，根据缺失和未测量的混杂因素评估偏倚和其他性能特征。两个案例研究是通过从两个随机对照试验和一个表现出大量缺失的外部真实世界数据源中获取治疗组来构建的。随机对照试验的适应症是多发性骨髓瘤和转移性激素敏感性前列腺癌。总生存期被视为主要终点。通过报告估计的外部比较器与随机对照试验治疗效果，评估了案例研究中缺失数据和未测量混杂因素的影响。基于这两个案例研究，通过改变潜在风险比、样本量、实验组与外部比较器之间的样本量比率、缺失协变量的数量和缺失百分比来进行模拟，以拓宽设置。因此，可以根据这些因素对偏倚和其他性能指标进行量化。对于多发性骨髓瘤外部比较研究，尽管存在缺失和潜在的未测量混杂因素，但结果与随机对照试验一致，而对于转移性激素敏感前列腺癌病例研究缺失数据导致样本量较小，总体上导致结果不确定。此外，对于转移性激素敏感的前列腺癌研究，重要资格标准中数据的缺失导致了进一步的限制。成功地应用模拟来定量了解缺失数据和未测量混杂因素的影响。这项探索性研究通过使用案例研究和模拟量化缺失数据和未测量混杂因素的影响，确认了外部比较器的优势和局限性。特别是，关键资格标准中的缺失数据被视为限制了准确导出外部比较器目标分析群体的能力，而模拟则证明了各种设置下预期的偏差程度。© 2024。作者。

Missing data and unmeasured confounding are key challenges for external comparator studies. This work evaluates bias and other performance characteristics depending on missingness and unmeasured confounding by means of two case studies and simulations.Two case studies were constructed by taking the treatment arms from two randomised controlled trials and an external real-world data source that exhibited substantial missingness. The indications of the randomised controlled trials were multiple myeloma and metastatic hormone-sensitive prostate cancer. Overall survival was taken as the main endpoint. The effects of missing data and unmeasured confounding were assessed for the case studies by reporting estimated external comparator versus randomised controlled trial treatment effects. Based on the two case studies, simulations were performed broadening the settings by varying the underlying hazard ratio, the sample size, the sample size ratio between the experimental arm and the external comparator, the number of missing covariates and the percentage of missingness. Thereby, bias and other performance metrics could be quantified dependent on these factors.For the multiple myeloma external comparator study, results were in line with the randomised controlled trial, despite missingness and potential unmeasured confounding, while for the metastatic hormone-sensitive prostate cancer case study missing data led to a low sample size, leading overall to inconclusive results. Furthermore, for the metastatic hormone-sensitive prostate cancer study, missing data in important eligibility criteria led to further limitations. Simulations were successfully applied to gain a quantitative understanding of the effects of missing data and unmeasured confounding.This exploratory study confirmed external comparator strengths and limitations by quantifying the impact of missing data and unmeasured confounding using case studies and simulations. In particular, missing data in key eligibility criteria were seen to limit the ability to derive the external comparator target analysis population accurately, while simulations demonstrated the magnitude of bias to expect for various settings.© 2024. The Author(s).