Risk of bias: a simulation study of power to detect study-level moderator effects in meta-analysis
Risk of bias: a simulation study of power to detect study-level moderator effects in meta-analysis
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DOI：
10.1186/2046-4053-2-107
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发表时间：
2013-01-01
影响因子：
3.7
通讯作者：
Shekelle, Paul G.
中科院分区：
文献类型：
--
作者：
Hempel, Susanne;Miles, Jeremy N. V.;Shekelle, Paul G.
背景资料：有理论和经验的理由相信，设计和执行因素与对照试验中的偏倚。统计学显著的调节效应，如试验质量对治疗效应量的影响，在个体荟萃分析中很少检测到，并且来自元流行病学数据集的证据不一致。理论与经验观察脱节的原因尚不清楚。本研究的目的是探索的权力，以检测研究水平的主持人在meta-analys.Methods的影响：我们使用蒙特-卡罗模拟生成的荟萃分析，并研究了试验数量，试验样本量，主持人效应大小，异质性，主持人分布的权力，以检测主持人的影响。模拟提供了一个参考指南，调查人员估计功率时，规划meta-regressions.Results：功率检测主持人效应的荟萃分析，例如，效果大小的研究质量的影响，在很大程度上是由残余异质性的程度存在于数据集（噪音不解释主持人）。只有当剩余异质性较低时，较大的试验样本量才能增加功效。需要大量试验或低残差异质性来检测效应。当调节者的比例不相等时（例如，25%的“高质量"试验，75%的”低质量“试验），在研究的场景中很少达到80%的功效。应用于具有实质异质性的经验性元流行病学数据集（I-2 = 92%，tau（2）= 0.285）估计需要>200次试验才能达到80%的功效，以显示统计学显著性结果，即使是显著的调节效应（0.2），以及具有较不常见特征的试验次数（例如，很少有“高质量”的研究）广泛影响功率。虽然研究特征，如试验质量，可以解释一定比例的异质性在荟萃分析的研究结果，剩余异质性是一个关键因素，在确定当调节变量和效应量之间的关联可以在统计学上检测。检测调节剂效应需要比大多数已发表的研究中所采用的更强大的分析;因此，负面结果不应被视为缺乏效应的证据，除非功效计算显示有足够的能力检测效应，否则研究不是假设证明。
Background: There are both theoretical and empirical reasons to believe that design and execution factors are associated with bias in controlled trials. Statistically significant moderator effects, such as the effect of trial quality on treatment effect sizes, are rarely detected in individual meta-analyses, and evidence from meta-epidemiological datasets is inconsistent. The reasons for the disconnect between theory and empirical observation are unclear. The study objective was to explore the power to detect study level moderator effects in meta-analyses.Methods: We generated meta-analyses using Monte-Carlo simulations and investigated the effect of number of trials, trial sample size, moderator effect size, heterogeneity, and moderator distribution on power to detect moderator effects. The simulations provide a reference guide for investigators to estimate power when planning meta-regressions.Results: The power to detect moderator effects in meta-analyses, for example, effects of study quality on effect sizes, is largely determined by the degree of residual heterogeneity present in the dataset (noise not explained by the moderator). Larger trial sample sizes increase power only when residual heterogeneity is low. A large number of trials or low residual heterogeneity are necessary to detect effects. When the proportion of the moderator is not equal (for example, 25% 'high quality', 75% 'low quality' trials), power of 80% was rarely achieved in investigated scenarios. Application to an empirical meta-epidemiological dataset with substantial heterogeneity (I-2 = 92%, tau(2) = 0.285) estimated >200 trials are needed for a power of 80% to show a statistically significant result, even for a substantial moderator effect (0.2), and the number of trials with the less common feature (for example, few 'high quality' studies) affects power extensively.Conclusions: Although study characteristics, such as trial quality, may explain some proportion of heterogeneity across study results in meta-analyses, residual heterogeneity is a crucial factor in determining when associations between moderator variables and effect sizes can be statistically detected. Detecting moderator effects requires more powerful analyses than are employed in most published investigations; hence negative findings should not be considered evidence of a lack of effect, and investigations are not hypothesis-proving unless power calculations show sufficient ability to detect effects.