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首页> 外文期刊>Statistics in medicine >Assessing the consistency assumption by exploring treatment by covariate interactions in mixed treatment comparison meta-analysis: Individual patient-level covariates versus aggregate trial-level covariates
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Assessing the consistency assumption by exploring treatment by covariate interactions in mixed treatment comparison meta-analysis: Individual patient-level covariates versus aggregate trial-level covariates

机译：通过在混合治疗比较荟萃分析中通过协变量相互作用探索治疗来评估一致性假设：个体患者水平协变量与总体试验水平协变量

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Mixed treatment comparison (MTC) meta-analysis allows several treatments to be compared in a single analysis while utilising direct and indirect evidence. Treatment by covariate interactions can be included in MTC models to explore how the covariate modifies the treatment effects. If interactions exist, the assumptions underlying MTCs may be invalidated. For conventional pair-wise meta-analysis, important benefits regarding the investigation of such interactions, gained from using individual patient data (IPD) rather than aggregate data (AD), have been described. We aim to compare IPD MTC models including patient-level covariates with AD MTC models including study-level covariates. IPD and AD random-effects MTC models for dichotomous outcomes are specified. Three assumptions are made regarding the interactions (i.e. independent, exchangeable and common interactions). The models are applied to a dataset to compare four drugs for treating malaria (i.e. amodiaquine-artesunate, dihydroartemisinin-piperaquine (DHAPQ), artemether-lumefantrine and chlorproguanil-dapsone plus artesunate) using the outcome unadjusted treatment success at day 28. The treatment effects and regression coefficients for interactions from the IPD models were more precise than those from AD models. Using IPD, assuming independent or exchangeable interactions, the regression coefficient for chlorproguanil-dapsone plus artesunate versus DHAPQ was statistically significant and assuming common interactions, the common coefficient was significant; whereas using AD, no coefficients were significant. Using IPD, DHAPQ was the best drug; whereas using AD, the best drug varied. Using AD models, there was no evidence that the consistency assumption was invalid; whereas, the assumption was questionable based on the IPD models. The AD analyses were misleading.
机译：混合治疗比较（MTC）荟萃分析允许在利用直接和间接证据的情况下在一次分析中比较几种治疗。 MTC模型中可以包括通过协变量交互作用进行的治疗，以探讨协变量如何改变治疗效果。如果存在相互作用，则MTC的基本假设可能会失效。对于常规的逐对荟萃分析，已经描述了有关研究此类相互作用的重要益处，这些益处是通过使用单个患者数据（IPD）而非聚合数据（AD）获得的。我们旨在比较包括患者水平协变量的IPD MTC模型与包括研究水平协变量的AD MTC模型。指定了用于二分结果的IPD和AD随机效应MTC模型。对于交互作用（即独立，可交换和共同的交互作用）做出了三个假设。将模型应用于数据集，以比较在第28天使用未调整的治疗成功率的结果来比较四种用于治疗疟疾的药物（即阿莫地喹-青蒿琥酯，二氢青蒿素-哌喹（DHAPQ），青蒿素-卢美替林和氯丙胍-氨苯砜加青蒿琥酯）。与IP模型相比，IPD模型的交互作用的回归系数更为精确。使用IPD，假设相互作用是独立的或可交换的，氯丙胍-氨苯砜加青蒿琥酯对DHAPQ的回归系数具有统计学意义，假定共同相互作用，则共同系数显着。而使用AD时，没有显着系数。使用IPD，DHAPQ是最好的药物。而使用AD则最好的药物有所不同。使用AD模型，没有证据表明一致性假设是无效的。然而，基于IPD模型，该假设值得怀疑。 AD分析具有误导性。

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