处理部分聚类随机对照试验中缺失的数据。

IF 7.8 1区 心理学 Q1 PSYCHOLOGY, MULTIDISCIPLINARY
Psychological methods Pub Date : 2025-10-01 Epub Date: 2023-11-06 DOI:10.1037/met0000612
Manshu Yang, Darrell J Gaskin
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引用次数: 0

摘要

部分聚类设计广泛应用于心理学研究,尤其是在检查预防或干预策略有效性的随机对照试验中。在部分集群试验中,为了提供干预,将一个或多个研究组的个体分为干预组,而其他组(如等待名单对照组)的个体则不集群。在部分聚集性试验中,数据缺失几乎是不可避免的,这可能对得出有效的研究结论构成重大挑战。本文的重点是处理部分聚类研究中随机数据的辅助变量相关缺失。通过模拟研究比较了五种方法,包括使用联合建模的同时多重插补(MI-JM-SIM)、使用联合建模(MI-JM-AS)的手臂特定多重插补、使用实质性模型兼容序列建模的手臂特定多元插补(MI-SMC-AS)、使用非形成性先验的序列完全贝叶斯估计(SFB-NON),以及使用弱信息先验的顺序完全贝叶斯估计(SFB-WEAK)。结果表明,当具有缺失值的变量仅涉及固定效应时,MI-JM-AS方法优于其他方法,而如果不完全变量具有随机效应,则MI-SMC-AS方法更可取。并以实证数据为例说明了不同方法的应用。(PsycInfo数据库记录(c)2023 APA,保留所有权利)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Handling missing data in partially clustered randomized controlled trials.

Partially clustered designs are widely used in psychological research, especially in randomized controlled trials that examine the effectiveness of prevention or intervention strategies. In a partially clustered trial, individuals are clustered into intervention groups in one or more study arms, for the purpose of intervention delivery, whereas individuals in other arms (e.g., the waitlist control arm) are unclustered. Missing data are almost inevitable in partially clustered trials and could pose a major challenge in drawing valid research conclusions. This article focuses on handling auxiliary-variable-dependent missing at random data in partially clustered studies. Five methods were compared via a simulation study, including simultaneous multiple imputation using joint modeling (MI-JM-SIM), arm-specific multiple imputation using joint modeling (MI-JM-AS), arm-specific multiple imputation using substantive-model-compatible sequential modeling (MI-SMC-AS), sequential fully Bayesian estimation using noninformative priors (SFB-NON), and sequential fully Bayesian estimation using weakly informative priors (SFB-WEAK). The results suggest that the MI-JM-AS method outperformed other methods when the variables with missing values only involved fixed effects, whereas the MI-SMC-AS method was preferred if the incomplete variables featured random effects. Applications of different methods are also illustrated using an empirical data example. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

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来源期刊
Psychological methods
Psychological methods PSYCHOLOGY, MULTIDISCIPLINARY-
CiteScore
13.10
自引率
7.10%
发文量
159
期刊介绍: Psychological Methods is devoted to the development and dissemination of methods for collecting, analyzing, understanding, and interpreting psychological data. Its purpose is the dissemination of innovations in research design, measurement, methodology, and quantitative and qualitative analysis to the psychological community; its further purpose is to promote effective communication about related substantive and methodological issues. The audience is expected to be diverse and to include those who develop new procedures, those who are responsible for undergraduate and graduate training in design, measurement, and statistics, as well as those who employ those procedures in research.
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