共病导致的治疗效果改变：120项随机对照试验的个体参与者数据荟萃分析
2023/06/25
摘要
背景：患有合并症的人在临床试验中的代表性不足。缺乏通过共病改变治疗效果的经验估计，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）来估计共病对治疗效果的影响
方法和发现：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年之间注册，并招募了≥300人。纳入的试验是多中心和国际性的。对于每个指标条件，我们分析了所纳入的试验中最常报告的结果。我们进行了两阶段的IPD荟萃分析，以估计合并症对治疗效果的修正。首先，对于每个试验，我们对合并症和治疗组之间的相互作用进行建模，并对年龄和性别进行调整。其次，对于每个指数条件下的每个治疗，我们对每个试验中的合并症-治疗的交互项进行了荟萃分析。我们以3种方式估计了合并症的影响：(i)合并症的数量（除指数条件外）；(ii)每种指数条件下6种最常见的合并症的存在与否；以及(iii)使用基础条件的连续标记（如估计肾小球滤过率（eGFR））。治疗效果按照结果类型的通常尺度进行建模（数字结果为绝对尺度，二元结果为相对尺度）。试验的平均年龄从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）到100%（良性前列腺肥大试验）。有3种或更多合并症的参与者的百分比从2.3%（过敏性鼻炎试验）到57%（系统性红斑狼疮试验）。我们没有发现任何证据表明合并症会改变治疗效果，对于合并症的3个衡量标准中的任何一个。在20种情况下，结果变量是连续的（如糖尿病的糖化血红蛋白变化），在3种情况下，结果是离散的（如偏头痛的头痛次数）。尽管所有的试验都是无效的，但在某些情况下，对治疗效果修正的估计是比较精确的（例如，钠-葡萄糖协同转运2（SGLT2）抑制剂治疗2型糖尿病-合并症计数的交互项为0.004，95%CI为-0.01至0.02），而对其他试验的可信区间则很宽（例如，皮质类固醇治疗哮喘-交互项为-0.22，95%CI为-1.07至0.54）。主要的局限性是，这些试验的设计和动力都不是为了评估合并症对治疗效果的影响，而且相对来说，有3种以上合并症的试验参与者很少。
结论：治疗效果改良的评估很少考虑合并症。我们的研究结果表明，对于包括在该分析中的试验，没有经验证据表明合并症会改变治疗效果。证据综合中使用的标准假设是，不同亚组的疗效是恒定的，尽管这经常受到批评。我们的研究结果表明，对于中度合并症，这一假设是合理的。因此，试验疗效结果可以与自然史和竞争风险的数据相结合，以评估在合并症背景下治疗的可能总体益处
Treatment effect modification due to comorbidity: Individual participant data meta-analyses of 120 randomised controlled trials
Hanlon, P., Butterly, E. W., Shah, A. S., Hannigan, L. J., Lewsey, J., Mair, F. S., Kent, D. M., Guthrie, B., Wild, S. H., Welton, N. J., Dias, S., & McAllister, D. A.
Abstract
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.
CONCLUSION: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.
背景：患有合并症的人在临床试验中的代表性不足。缺乏通过共病改变治疗效果的经验估计，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）来估计共病对治疗效果的影响
方法和发现：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年之间注册，并招募了≥300人。纳入的试验是多中心和国际性的。对于每个指标条件，我们分析了所纳入的试验中最常报告的结果。我们进行了两阶段的IPD荟萃分析，以估计合并症对治疗效果的修正。首先，对于每个试验，我们对合并症和治疗组之间的相互作用进行建模，并对年龄和性别进行调整。其次，对于每个指数条件下的每个治疗，我们对每个试验中的合并症-治疗的交互项进行了荟萃分析。我们以3种方式估计了合并症的影响：(i)合并症的数量（除指数条件外）；(ii)每种指数条件下6种最常见的合并症的存在与否；以及(iii)使用基础条件的连续标记（如估计肾小球滤过率（eGFR））。治疗效果按照结果类型的通常尺度进行建模（数字结果为绝对尺度，二元结果为相对尺度）。试验的平均年龄从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）到100%（良性前列腺肥大试验）。有3种或更多合并症的参与者的百分比从2.3%（过敏性鼻炎试验）到57%（系统性红斑狼疮试验）。我们没有发现任何证据表明合并症会改变治疗效果，对于合并症的3个衡量标准中的任何一个。在20种情况下，结果变量是连续的（如糖尿病的糖化血红蛋白变化），在3种情况下，结果是离散的（如偏头痛的头痛次数）。尽管所有的试验都是无效的，但在某些情况下，对治疗效果修正的估计是比较精确的（例如，钠-葡萄糖协同转运2（SGLT2）抑制剂治疗2型糖尿病-合并症计数的交互项为0.004，95%CI为-0.01至0.02），而对其他试验的可信区间则很宽（例如，皮质类固醇治疗哮喘-交互项为-0.22，95%CI为-1.07至0.54）。主要的局限性是，这些试验的设计和动力都不是为了评估合并症对治疗效果的影响，而且相对来说，有3种以上合并症的试验参与者很少。
结论：治疗效果改良的评估很少考虑合并症。我们的研究结果表明，对于包括在该分析中的试验，没有经验证据表明合并症会改变治疗效果。证据综合中使用的标准假设是，不同亚组的疗效是恒定的，尽管这经常受到批评。我们的研究结果表明，对于中度合并症，这一假设是合理的。因此，试验疗效结果可以与自然史和竞争风险的数据相结合，以评估在合并症背景下治疗的可能总体益处
(中日友好医院呼吸与危重症医学科 沈焜路 摘译 林江涛 审校)
(PLoS Med. 2023 Jun 6. DOI: 10.1371/journal.pmed.1004176)
(PLoS Med. 2023 Jun 6. DOI: 10.1371/journal.pmed.1004176)
Treatment effect modification due to comorbidity: Individual participant data meta-analyses of 120 randomised controlled trials
Hanlon, P., Butterly, E. W., Shah, A. S., Hannigan, L. J., Lewsey, J., Mair, F. S., Kent, D. M., Guthrie, B., Wild, S. H., Welton, N. J., Dias, S., & McAllister, D. A.
Abstract
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.
CONCLUSION: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.
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