Multiple primary outcomes are sometimes collected and analysed in randomised controlled trials (RCTs), and are used in favour of a single outcome. By collecting multiple primary outcomes, it is possible to fully evaluate the effect that an intervention has for a given disease process. A simple approach to analysing multiple outcomes is to consider each outcome separately, however, this approach does not account for any pairwise correlations between the outcomes. Any cases with missing values must be ignored, unless an additional imputation step is performed. Alternatively, multivariate methods that explicitly model the pairwise correlations between the outcomes may be more efficient when some of the outcomes have missing values. In this paper, we present an overview of relevant methods that can be used to analyse multiple outcome measures in RCTs, including methods based on multivariate multilevel (MM) models. We perform simulation studies to evaluate the bias in the estimates of the intervention effects and the power of detecting true intervention effects observed when using selected methods. Different simulation scenarios were constructed by varying the number of outcomes, the type of outcomes, the degree of correlations between the outcomes and the proportions and mechanisms of missing data. We compare multivariate methods to univariate methods with and without multiple imputation. When there are strong correlations between the outcome measures (ρ > .4), our simulation studies suggest that there are small power gains when using the MM model when compared to analysing the outcome measures separately. In contrast, when there are weak correlations (ρ < .4), the power is reduced when using univariate methods with multiple imputation when compared to analysing the outcome measures separately.

中文翻译：

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使用模拟研究分析随机对照试验中多种结果指标的方法比较


有时会在随机对照试验 (RCT) 中收集和分析多个主要结局，并用于支持单一结局。通过收集多个主要结果，可以全面评估干预措施对特定疾病过程的效果。分析多个结果的一个简单方法是单独考虑每个结果，但是，这种方法没有考虑结果之间的任何成对相关性。任何缺失值的情况都必须被忽略，除非执行额外的插补步骤。或者，当某些结果具有缺失值时，对结果之间的成对相关性进行显式建模的多变量方法可能会更有效。在本文中，我们概述了可用于分析随机对照试验中多种结果指标的相关方法，包括基于多元多水平（MM）模型的方法。我们进行模拟研究，以评估干预效果估计的偏差以及检测使用选定方法时观察到的真实干预效果的能力。通过改变结果的数量、结果的类型、结果之间的相关程度以及缺失数据的比例和机制来构建不同的模拟场景。我们将使用和不使用多重插补的多变量方法与单变量方法进行比较。当结果测量之间存在很强的相关性 (ρ > .4) 时，我们的模拟研究表明，与单独分析结果测量相比，使用 MM 模型时的功效增益较小。相反，当相关性较弱时 (ρ < .4），与单独分析结果测量相比，使用具有多重插补的单变量方法时功效会降低。