Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA.
Department of Epidemiology and Biostatistics, Peking University Health Science Center, Beijing, China.
Res Synth Methods. 2020 Sep;11(5):594-616. doi: 10.1002/jrsm.1406. Epub 2020 May 8.
Meta-analyses of clinical trials typically focus on one outcome at a time. However, treatment decision-making depends on an overall assessment of outcomes balancing benefit in various domains and potential risks. This calls for meta-analysis methods for combined outcomes that encompass information from different domains. When individual patient data (IPD) are available from all studies, combined outcomes can be calculated for each individual and standard meta-analysis methods would apply. However, IPD are usually difficult to obtain. We propose a method to estimate the overall treatment effect for combined outcomes based on first reconstructing pseudo IPD from available summary statistics and then pooling estimates from multiple reconstructed datasets. We focus on combined outcomes constructed from two continuous original outcomes. The reconstruction step requires the specification of the joint distribution of these two original outcomes, including the correlation which is often unknown. For outcomes that are combined in a linear fashion, misspecifications of this correlation affect efficiency, but not consistency, of the resulting treatment effect estimator. For other combined outcomes, an accurate estimate of the correlation is necessary to ensure the consistency of treatment effect estimates. To this end, we propose several ways to estimate this correlation under different data availability scenarios. We evaluate the performance of the proposed methods through simulation studies and apply these to two examples: (a) a meta-analysis of dipeptidyl peptidase-4 inhibitors vs control on treating type 2 diabetes; and (b) a meta-analysis of positive airway pressure therapy vs control on lowering blood pressure among patients with obstructive sleep apnea.
临床试验的荟萃分析通常一次关注一个结局。然而，治疗决策取决于对各领域结局获益和潜在风险的综合评估。这就需要有针对包含不同领域信息的联合结局的荟萃分析方法。当所有研究都可获得个体患者数据（IPD）时，可以为每个个体计算联合结局，并应用标准荟萃分析方法。然而，IPD 通常难以获得。我们提出了一种方法，基于从可用汇总统计数据中重建伪 IPD，并从多个重建数据集汇总估计值，从而对联合结局进行估计。我们关注由两个连续原始结局构建的联合结局。重建步骤需要指定这两个原始结局的联合分布，包括通常未知的相关性。对于以线性方式组合的结局，如果对这种相关性的指定有误，将会影响治疗效果估计值的效率，但不会影响其一致性。对于其他联合结局，需要准确估计相关性，以确保治疗效果估计值的一致性。为此，我们在不同的数据可用性情况下提出了几种估计这种相关性的方法。我们通过模拟研究来评估所提出方法的性能，并将其应用于两个示例：（a）二肽基肽酶-4 抑制剂与对照组治疗 2 型糖尿病的荟萃分析；（b）正压通气疗法与对照组治疗阻塞性睡眠呼吸暂停患者血压的荟萃分析。
