When does the use of individual patient data in network meta-analysis make a difference? A simulation study.
When does the use of individual patient data in network meta-analysis make a difference? A simulation study.
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DOI：
10.1186/s12874-020-01198-2
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发表时间：
2021-01-13
影响因子：
4
通讯作者：
Bansback N
中科院分区：
文献类型：
--
作者：
Kanters S;Karim ME;Thorlund K;Anis A;Bansback N
个体患者数据（IPD）在网络荟萃分析（NMA）中的使用正在迅速增长。本研究旨在通过模拟确定，相对于仅使用AgD，结合IPD和汇总数据（AgD）时，选择因素对NMA估计的有效性和精确度的影响。通过模拟比较了三种分析策略：1）无调整的AgD NMA（AgD-NMA）; 2）具有元回归的AgD NMA（AgD-NMA-MR）;和3）具有元回归的IPD-AgD NMA（IPD-NMA）。我们比较了108种参数排列：网络节点数量（3、5或10）; IPD通知的治疗比较比例（低、中或高）;同等规模试验（2组，每组200例患者）或更大的IPD试验（每组500例患者）;稀疏或人口稠密的网络;以及效应修改类型（无、治疗比较恒定或可交换）。对于每种参数组合，通过200次模拟生成数据，每次模拟均使用正态分布的线性回归。为了评估模型性能和估计有效性，收集了治疗效应和协变量估计值的均方误差（MSE）和偏倚。标准误差（SE）和方差分析用于比较估计精度。总体而言，IPD-NMA在有效性和精确度方面表现最好。在108个场景中，有88个场景的IPD-NMA的中位MSE较低（其他结果相似）。平均而言，IPD-NMA中位MSE是使用AgD-NMA-MR的中位MSE的0.54倍。同样，IPD-NMA治疗效果估计值的SE是AgD-NMA-MR SE大小的1/5。使用IPD-NMA的上级有效性和精确度的大小在不同场景中变化，并且与IPD的量相关。在小型或稀疏网络中使用IPD始终会提高有效性和精度;然而，在大型/密集网络中，如果包含的IPD太少，IPD的影响往往可以忽略不计。类似的结果也适用于元回归系数估计。我们的模拟研究表明，在NMA中使用IPD将大大提高在大多数NMA IPD数据场景中估计治疗效果和回归系数的有效性和精度。然而，当使用可忽略的IPD时，IPD可能不会为大型和密集治疗网络的NMA增加有意义的有效性和精度。在线版本包含补充材料，可通过10.1186/s12874-020-01198-2获得。
The use of individual patient data (IPD) in network meta-analyses (NMA) is rapidly growing. This study aimed to determine, through simulations, the impact of select factors on the validity and precision of NMA estimates when combining IPD and aggregate data (AgD) relative to using AgD only. Three analysis strategies were compared via simulations: 1) AgD NMA without adjustments (AgD-NMA); 2) AgD NMA with meta-regression (AgD-NMA-MR); and 3) IPD-AgD NMA with meta-regression (IPD-NMA). We compared 108 parameter permutations: number of network nodes (3, 5 or 10); proportion of treatment comparisons informed by IPD (low, medium or high); equal size trials (2-armed with 200 patients per arm) or larger IPD trials (500 patients per arm); sparse or well-populated networks; and type of effect-modification (none, constant across treatment comparisons, or exchangeable). Data were generated over 200 simulations for each combination of parameters, each using linear regression with Normal distributions. To assess model performance and estimate validity, the mean squared error (MSE) and bias of treatment-effect and covariate estimates were collected. Standard errors (SE) and percentiles were used to compare estimate precision. Overall, IPD-NMA performed best in terms of validity and precision. The median MSE was lower in the IPD-NMA in 88 of 108 scenarios (similar results otherwise). On average, the IPD-NMA median MSE was 0.54 times the median using AgD-NMA-MR. Similarly, the SEs of the IPD-NMA treatment-effect estimates were 1/5 the size of AgD-NMA-MR SEs. The magnitude of superior validity and precision of using IPD-NMA varied across scenarios and was associated with the amount of IPD. Using IPD in small or sparse networks consistently led to improved validity and precision; however, in large/dense networks IPD tended to have negligible impact if too few IPD were included. Similar results also apply to the meta-regression coefficient estimates. Our simulation study suggests that the use of IPD in NMA will considerably improve the validity and precision of estimates of treatment effect and regression coefficients in the most NMA IPD data-scenarios. However, IPD may not add meaningful validity and precision to NMAs of large and dense treatment networks when negligible IPD are used. The online version contains supplementary material available at 10.1186/s12874-020-01198-2.
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