PDF(Pubmed)
Abstract：
There is insufficient investigation of multiple imputation for systematically missing discrete variables in individual participant data meta-analysis (IPDMA) with a small number of included studies. Therefore, this study aims to evaluate the performance of three multiple imputation strategies - fully conditional specification (FCS), multivariate normal (MVN), conditional quantile imputation (CQI) - on systematically missing data on gait speed in the Swedish National Study on Aging and Care (SNAC).
In total, 1 000 IPDMA were simulated with four prospective cohort studies based on the characteristics of the SNAC. The three multiple imputation strategies were analysed with a two-stage common-effect multivariable logistic model targeting the effect of three levels of gait speed (100% missing in one study) on 5-years mortality with common odds ratios set to OR1 = 0.55 (0.8-1.2 vs ≤0.8 m/s), and OR2 = 0.29 (>1.2 vs ≤0.8 m/s).
The average combined estimate for the mortality odds ratio OR1 (relative bias %) were 0.58 (8.2%), 0.58 (7.5%), and 0.55 (0.7%) for the FCS, MVN, and CQI, respectively. The average combined estimate for the mortality odds ratio OR2 (relative bias %) were 0.30 (2.5%), 0.33 (10.0%), and 0.29 (0.9%) for the FCS, MVN, and CQI respectively.
In our simulations of an IPDMA based on the SNAC where gait speed data was systematically missing in one study, all three imputation methods performed relatively well. The smallest bias was found for the CQI approach.
In total, 1 000 IPDMA were simulated with four prospective cohort studies based on the characteristics of the SNAC. The three multiple imputation strategies were analysed with a two-stage common-effect multivariable logistic model targeting the effect of three levels of gait speed (100% missing in one study) on 5-years mortality with common odds ratios set to OR1 = 0.55 (0.8-1.2 vs ≤0.8 m/s), and OR2 = 0.29 (>1.2 vs ≤0.8 m/s).
The average combined estimate for the mortality odds ratio OR1 (relative bias %) were 0.58 (8.2%), 0.58 (7.5%), and 0.55 (0.7%) for the FCS, MVN, and CQI, respectively. The average combined estimate for the mortality odds ratio OR2 (relative bias %) were 0.30 (2.5%), 0.33 (10.0%), and 0.29 (0.9%) for the FCS, MVN, and CQI respectively.
In our simulations of an IPDMA based on the SNAC where gait speed data was systematically missing in one study, all three imputation methods performed relatively well. The smallest bias was found for the CQI approach.
摘要：
背景：在少数纳入研究的个体参与者数据荟萃分析（IPDMA）中，对系统性缺失离散变量的多重插补研究不足。因此,本研究旨在评估三种多重插补策略的性能-完全条件规范(FCS)，多元正态(MVN)，条件分位数插补（CQI）-关于瑞典国家衰老与护理研究（SNAC）中步态速度的系统性缺失数据。
方法：总共，根据SNAC的特点，用四项前瞻性队列研究模拟了1000IPDMA。使用两阶段共同效应多变量逻辑模型分析了三种多重填补策略，该模型针对三个水平的步态速度（一项研究中缺失100％）对5年死亡率的影响，共同比值比设置为OR1=0.55（0.8-1.2vs≤0.8m/s），OR2=0.29(>1.2vs≤0.8m/s)。
结果：死亡率比值比OR1（相对偏倚％）的平均综合估计值为0.58（8.2％），0.58（7.5%），FCS为0.55（0.7%），MVN,和CQI，分别。死亡率比值比OR2(相对偏差%)的平均综合估计值为0.30(2.5%),0.33（10.0%），FCS为0.29（0.9%），MVN,和CQI分别。
结论：在我们对基于SNAC的IPDMA的模拟中，其中一项研究系统地丢失了步态速度数据，这三种插补方法表现相对较好。对于CQI方法发现最小的偏差。
方法：总共，根据SNAC的特点，用四项前瞻性队列研究模拟了1000IPDMA。使用两阶段共同效应多变量逻辑模型分析了三种多重填补策略，该模型针对三个水平的步态速度（一项研究中缺失100％）对5年死亡率的影响，共同比值比设置为OR1=0.55（0.8-1.2vs≤0.8m/s），OR2=0.29(>1.2vs≤0.8m/s)。
结果：死亡率比值比OR1（相对偏倚％）的平均综合估计值为0.58（8.2％），0.58（7.5%），FCS为0.55（0.7%），MVN,和CQI，分别。死亡率比值比OR2(相对偏差%)的平均综合估计值为0.30(2.5%),0.33（10.0%），FCS为0.29（0.9%），MVN,和CQI分别。
结论：在我们对基于SNAC的IPDMA的模拟中，其中一项研究系统地丢失了步态速度数据，这三种插补方法表现相对较好。对于CQI方法发现最小的偏差。
