BACKGROUND People with comorbidities are underrepresented in clinical trials. Empirical estimates of treatment effect modification by comorbidity are lacking, leading to uncertainty in treatment recommendations. We aimed to produce estimates of treatment effect modification by comorbidity using individual participant data (IPD). METHODS AND FINDINGS We obtained IPD for 120 industry-sponsored phase 3/4 trials across 22 index conditions (n = 128,331). Trials had to be registered between 1990 and 2017 and have recruited ≥300 people. Included trials were multicentre and international. For each index condition, we analysed the outcome most frequently reported in the included trials. We performed a two-stage IPD meta-analysis to estimate modification of treatment effect by comorbidity. First, for each trial, we modelled the interaction between comorbidity and treatment arm adjusted for age and sex. Second, for each treatment within each index condition, we meta-analysed the comorbidity-treatment interaction terms from each trial. We estimated the effect of comorbidity measured in 3 ways: (i) the number of comorbidities (in addition to the index condition); (ii) presence or absence of the 6 commonest comorbid diseases for each index condition; and (iii) using continuous markers of underlying conditions (e.g., estimated glomerular filtration rate (eGFR)). Treatment effects were modelled on the usual scale for the type of outcome (absolute scale for numerical outcomes, relative scale for binary outcomes). Mean age in the trials ranged from 37.1 (allergic rhinitis trials) to 73.0 (dementia trials) and percentage of male participants range from 4.4% (osteoporosis trials) to 100% (benign prostatic hypertrophy trials). The percentage of participants with 3 or more comorbidities ranged from 2.3% (allergic rhinitis trials) to 57% (systemic lupus erythematosus trials). We found no evidence of modification of treatment efficacy by comorbidity, for any of the 3 measures of comorbidity. This was the case for 20 conditions for which the outcome variable was continuous (e.g., change in glycosylated haemoglobin in diabetes) and for 3 conditions in which the outcomes were discrete events (e.g., number of headaches in migraine). Although all were null, estimates of treatment effect modification were more precise in some cases (e.g., sodium-glucose co-transporter-2 (SGLT2) inhibitors for type 2 diabetes-interaction term for comorbidity count 0.004, 95% CI -0.01 to 0.02) while for others credible intervals were wide (e.g., corticosteroids for asthma-interaction term -0.22, 95% CI -1.07 to 0.54). The main limitation is that these trials were not designed or powered to assess variation in treatment effect by comorbidity, and relatively few trial participants had >3 comorbidities. CONCLUSIONS Assessments of treatment effect modification rarely consider comorbidity. Our findings demonstrate that for trials included in this analysis, there was no empirical evidence of treatment effect modification by comorbidity. The standard assumption used in evidence syntheses is that efficacy is constant across subgroups, although this is often criticised. Our findings suggest that for modest levels of comorbidities, this assumption is reasonable. Thus, trial efficacy findings can be combined with data on natural history and competing risks to assess the likely overall benefit of treatments in the context of comorbidity.

中文翻译：

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治疗效果因合并症而改变：120 项随机对照试验的个体参与者数据荟萃分析。

背景 患有合并症的人在临床试验中的代表性不足。缺乏对合并症治疗效果改变的经验估计，导致治疗建议的不确定性。我们的目的是使用个体参与者数据 (IPD) 来估计合并症对治疗效果的影响。方法和结果 我们获得了 120 项行业赞助的 3/4 期试验的 IPD，涉及 22 个指标条件 (n = 128,331)。试验必须在 1990 年至 2017 年期间注册，并且招募了≥300 人。纳入的试验是多中心和国际性的。对于每个指标条件，我们分析了纳入试验中最常报告的结果。我们进行了两阶段 IPD 荟萃分析，以估计合并症对治疗效果的影响。首先，对于每个试验，我们模拟了根据年龄和性别调整的合并症和治疗组之间的相互作用。其次，对于每个指标条件下的每种治疗，我们对来自每个试验的合并症-治疗相互作用项进行了荟萃分析。我们通过 3 种方式评估了合并症的影响：(i) 合并症的数量（除指数条件外）；(ii) 每种指标条件下是否存在 6 种最常见的合并症；(iii) 使用潜在条件的连续标记（例如，估计的肾小球滤过率 (eGFR)）。治疗效果根据结果类型的通常量表（数字结果的绝对量表，二元结果的相对量表）建模。试验中的平均年龄从 37.1 岁（过敏性鼻炎试验）到 73.0 岁（痴呆症试验），男性参与者的百分比从 4. 4%（骨质疏松症试验）至 100%（良性前列腺肥大试验）。患有 3 种或更多合并症的参与者比例从 2.3%（过敏性鼻炎试验）到 57%（系统性红斑狼疮试验）不等。对于 3 种合并症测量中的任何一种，我们没有发现合并症会改变治疗效果的证据。20 种结果变量是连续的（例如，糖尿病中糖化血红蛋白的变化）和 3 种结果是离散事件（例如，偏头痛的头痛次数）的情况就是这种情况。尽管所有结果均无效，但在某些情况下对治疗效果修改的估计更为精确（例如，钠-葡萄糖协同转运蛋白 2 (SGLT2) 抑制剂用于 2 型糖尿病-合并症计数的相互作用项 0.004，95% CI -0.01 至 0 . 02) 而对于其他人来说，可信区间很宽（例如，皮质类固醇用于哮喘相互作用项 -0.22，95% CI -1.07 至 0.54）。主要局限性在于，这些试验的设计或功效并非旨在评估合并症对治疗效果的影响，并且相对较少的试验参与者有 >3 种合并症。结论 治疗效果调整的评估很少考虑合并症。我们的研究结果表明，对于包含在该分析中的试验，没有合并症改变治疗效果的经验证据。证据综合中使用的标准假设是疗效在各个亚组中保持不变，尽管这经常受到批评。我们的研究结果表明，对于适度水平的合并症，这种假设是合理的。因此，