{"title":"The role of a quadratic term in estimating the average treatment effect from longitudinal randomized controlled trials with missing data.","authors":"Manshu Yang, Lijuan Wang, Scott E Maxwell","doi":"10.1037/met0000709","DOIUrl":null,"url":null,"abstract":"<p><p>Longitudinal randomized controlled trials (RCTs) have been commonly used in psychological studies to evaluate the effectiveness of treatment or intervention strategies. Outcomes in longitudinal RCTs may follow either straight-line or curvilinear change trajectories over time, and missing data are almost inevitable in such trials. The current study aims to investigate (a) whether the estimate of average treatment effect (ATE) would be biased if a straight-line growth (SLG) model is fit to longitudinal RCT data with quadratic growth and missing completely at random (MCAR) or missing at random (MAR) data, and (b) whether adding a quadratic term to an SLG model would improve the ATE estimation and inference. Four models were compared via a simulation study, including the SLG model, the quadratic growth model with arm-invariant and fixed quadratic effect (QG-AIF), the quadratic growth model with arm-specific and fixed quadratic effects (QG-ASF), and the quadratic growth model with arm-specific and random quadratic effects (QG-ASR). Results suggest that fitting an SLG model to quadratic growth data often yielded severe biases in ATE estimates, even if data were MCAR or MAR. Given four or more waves of longitudinal data, the QG-ASR model outperformed the other methods; for three-wave data, the QG-ASR model was not applicable and the QG-ASF model performed well. Applications of different models are also illustrated using an empirical data example. (PsycInfo Database Record (c) 2024 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.6000,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Psychological methods","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1037/met0000709","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
摘要
纵向随机对照试验(RCT)通常用于心理研究,以评估治疗或干预策略的有效性。纵向随机对照试验的结果随着时间的推移可能呈现直线或曲线变化轨迹,而数据缺失在这类试验中几乎是不可避免的。本研究旨在探讨:(a) 如果将直线增长(SLG)模型拟合到具有二次增长和完全随机缺失(MCAR)或随机缺失(MAR)数据的纵向 RCT 数据中,平均治疗效果(ATE)的估计值是否会出现偏差;(b) 在 SLG 模型中加入二次项是否会改善 ATE 的估计和推断。通过模拟研究对四种模型进行了比较,包括 SLG 模型、具有臂不变量和固定二次方效应的二次方生长模型(QG-AIF)、具有臂特异性和固定二次方效应的二次方生长模型(QG-ASF)以及具有臂特异性和随机二次方效应的二次方生长模型(QG-ASR)。结果表明,即使数据是 MCAR 或 MAR,将 SLG 模型拟合到二次方生长数据中往往会导致 ATE 估计值出现严重偏差。对于四波或更多波的纵向数据,QG-ASR 模型的表现优于其他方法;对于三波数据,QG-ASR 模型不适用,而 QG-ASF 模型表现良好。此外,还通过一个经验数据实例说明了不同模型的应用。(PsycInfo Database Record (c) 2024 APA, 版权所有)。
The role of a quadratic term in estimating the average treatment effect from longitudinal randomized controlled trials with missing data.
Longitudinal randomized controlled trials (RCTs) have been commonly used in psychological studies to evaluate the effectiveness of treatment or intervention strategies. Outcomes in longitudinal RCTs may follow either straight-line or curvilinear change trajectories over time, and missing data are almost inevitable in such trials. The current study aims to investigate (a) whether the estimate of average treatment effect (ATE) would be biased if a straight-line growth (SLG) model is fit to longitudinal RCT data with quadratic growth and missing completely at random (MCAR) or missing at random (MAR) data, and (b) whether adding a quadratic term to an SLG model would improve the ATE estimation and inference. Four models were compared via a simulation study, including the SLG model, the quadratic growth model with arm-invariant and fixed quadratic effect (QG-AIF), the quadratic growth model with arm-specific and fixed quadratic effects (QG-ASF), and the quadratic growth model with arm-specific and random quadratic effects (QG-ASR). Results suggest that fitting an SLG model to quadratic growth data often yielded severe biases in ATE estimates, even if data were MCAR or MAR. Given four or more waves of longitudinal data, the QG-ASR model outperformed the other methods; for three-wave data, the QG-ASR model was not applicable and the QG-ASF model performed well. Applications of different models are also illustrated using an empirical data example. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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.