Comparing single and multiple imputation strategies for harmonizing substance use data across HIV-related cohort studies.
Comparing single and multiple imputation strategies for harmonizing substance use data across HIV-related cohort studies.
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DOI：
10.1186/s12874-022-01554-4
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发表时间：
2022-04-03
影响因子：
4
通讯作者：
Gorbach P
中科院分区：
文献类型：
--
作者：
Javanbakht M;Lin J;Ragsdale A;Kim S;Siminski S;Gorbach P
虽然有评估物质使用的标准化措施，但大多数研究使用这些措施的变体，因此难以协调研究之间的数据。本研究的目的是评价不同策略的性能，以填补缺失的物质使用数据，这可能是数据协调程序的一部分。我们使用了2014年8月至2019年6月期间从528名参与者中收集的自我报告的药物使用数据，这些参与者在药物使用和艾滋病毒的队列研究中进行了2，389次研究访问。我们选择了低（海洛因），中（甲基苯丙胺）和高（大麻）流行药物，并将每种物质的10-50%设置为缺失。数据截断模拟了不同测量的协调导致的缺失。我们进行了Monte Carlo模拟，以评估单重和多重插补（MI）方法的比较性能，使用相对平均偏差，均方根误差（RMSE）和每个插补估计值的95%置信区间的覆盖概率。不进行估算（即，列表删除），对药物使用的估计有偏差，特别是对海洛因等低流行率结果。例如，即使有10%的数据缺失，完整的病例分析也将海洛因的流行率低估了33%。MI，即使只有5个插补产生的偏倚最小的估计，但是，对于高患病率的结果，如大麻低至中度缺失，性能的单一插补策略改善。例如，在大麻的情况下，缺失率为10%，进行回归的单次插补以及多重插补，导致最小偏倚（相对平均偏倚分别为0.06%和0.07%）和相当的性能（RMSE = 0.0102，覆盖率分别为95.8%和96.2%）。我们的结果插补缺失的物质使用数据的数据协调，结果表明，MI提供了最好的性能在一系列的条件。此外，物质使用数据的单一插补在结局的患病率高且缺失率低的情况下进行。这些研究结果提供了一个实际应用的评价几种插补策略，并有助于解决缺失数据的问题时，从个别研究的数据相结合。
Although standardized measures to assess substance use are available, most studies use variations of these measures making it challenging to harmonize data across studies. The aim of this study was to evaluate the performance of different strategies to impute missing substance use data that may result as part of data harmonization procedures. We used self-reported substance use data collected between August 2014 and June 2019 from 528 participants with 2,389 study visits in a cohort study of substance use and HIV. We selected a low (heroin), medium (methamphetamine), and high (cannabis) prevalence drug and set 10–50% of each substance to missing. The data amputation mimicked missingness that results from harmonization of disparate measures. We conducted Monte Carlo simulations to evaluate the comparative performance of single and multiple imputation (MI) methods using the relative mean bias, root mean square error (RMSE), and coverage probability of the 95% confidence interval for each imputed estimate. Without imputation (i.e., listwise deletion), estimates of substance use were biased, especially for low prevalence outcomes such as heroin. For instance, even when 10% of data were missing, the complete case analysis underestimated the prevalence of heroin by 33%. MI, even with as few as five imputations produced the least biased estimates, however, for a high prevalence outcome such as cannabis with low to moderate missingness, performance of single imputation strategies improved. For instance, in the case of cannabis, with 10% missingness, single imputation with regression performed just as well as multiple imputation resulting in minimal bias (relative mean bias of 0.06% and 0.07% respectively) and comparable performance (RMSE = 0.0102 for both and coverage of 95.8% and 96.2% respectively). Our results from imputation of missing substance use data resulting from data harmonization indicate that MI provided the best performance across a range of conditions. Additionally, single imputation for substance use data performed comparably under scenarios where the prevalence of the outcome was high and missingness was low. These findings provide a practical application for the evaluation of several imputation strategies and helps to address missing data problem when combining data from individual studies.
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影响因子：
3
作者：
Justice, Amy C.;Dombrowski, Elizabeth;Bryant, Kendall
通讯作者：
Bryant, Kendall
影响因子：
2
作者：
Song, JW;Belin, TR
通讯作者：
Belin, TR
DOI：
10.1007/s11524-018-0309-8
发表时间：
2019-06-01
影响因子：
6.6
作者：
Javanbakht, Marjan;Ragsdale, Amy;Gorbach, Pamina M.
通讯作者：
Gorbach, Pamina M.
影响因子：
7.7
作者：
Rourke, Sean B.;Gardner, Sandra;Rachlis, Anita
通讯作者：
Rachlis, Anita
影响因子：
1.7
作者：
Kipke, Michele D.;Kubicek, Katrina;Belzer, Marvin
通讯作者：
Belzer, Marvin