Quantifying inconsistency in one-stage individual participant data meta-analyses of treatment-covariate interactions: a simulation study
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单阶段个体参与者数据元分析中治疗与协变量交互作用的不一致性度量
- 研究问题
- 在亚组人数不等或协变量连续时，如何计算单阶段个体数据元分析中治疗交互作用的 I²，并与两阶段模型比较？
- 方法与思路
- 作者扩展 I² 估计公式，在十四种模拟情景中改变试验间异质性、试验数量及每项试验人数，比较等价模型的偏差与精度。
- 材料陈述的贡献
- 摘要称，两类模型的 I² 偏差与精度相近，平均差为负一至零个百分点，但小样本模拟中差异可达十九点四个百分点。
- 阅读关注点
- 应核对单阶段与两阶段模型的等价条件、连续协变量的尺度处理，以及小样本下 I² 差异与不确定性如何报告。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要提供部分模拟结果，但未说明完整设计及公式，不能推广至任意元分析。
原文摘要
It is recommended that measures of between-study effect heterogeneity be reported when conducting individual-participant data meta-analyses (IPD-MA). Methods exist to quantify inconsistency between trials via I^2 (the percentage of variation in the treatment effect due to between-study heterogeneity) when conducting two-stage IPD-MA, and when conducting one-stage IPD-MA with approximately equal numbers of treatment and control group participants. We extend formulae to estimate I^2 when investigating treatment-covariate interactions with unequal numbers of participants across subgroups and/or continuous covariates. A simulation study was conducted to assess the agreement in values of I^2 between those derived from two-stage models using traditional methods and those derived from equivalent one-stage models. Fourteen scenarios differed by the magnitude of between-trial heterogeneity, the number of trials, and the average number of participants in each trial. Bias and precision of I^2 were similar between the one- and two-stage models. The mean difference in I^2 between equivalent models ranged between -1.0 and 0.0 percentage points across scenarios. However, disparities were larger in simulated datasets with smaller samples sizes with up to 19.4 percentage points difference between models. Thus, the estimates of I^2 derived from these extended methods can be interpreted similarly to those from existing formulae for two-stage models.