Mun Eun-Young, de la Torre Jimmy, Atkins David C, White Helene R, Ray Anne E, Kim Su-Young, Jiao Yang, Clarke Nickeisha, Huo Yan, Larimer Mary E, Huh David
Center of Alcohol Studies.
Department of Psychiatry and Behavioral Sciences, The University of Washington.
Psychol Addict Behav. 2015 Mar;29(1):34-48. doi: 10.1037/adb0000047. Epub 2014 Dec 29.
This article provides an overview of a study that synthesizes multiple, independently collected alcohol intervention studies for college students into a single, multisite longitudinal data set. This research embraced innovative analytic strategies (i.e., integrative data analysis or meta-analysis using individual participant-level data), with the overall goal of answering research questions that are difficult to address in individual studies such as moderation analysis, while providing a built-in replication for the reported efficacy of brief motivational interventions for college students. Data were pooled across 24 intervention studies, of which 21 included a comparison or control condition and all included one or more treatment conditions. This yielded a sample of 12,630 participants (42% men; 58% first-year or incoming students). The majority of the sample identified as White (74%), with 12% Asian, 7% Hispanic, 2% Black, and 5% other/mixed ethnic groups. Participants were assessed 2 or more times from baseline up to 12 months, with varying assessment schedules across studies. This article describes how we combined individual participant-level data from multiple studies, and discusses the steps taken to develop commensurate measures across studies via harmonization and newly developed Markov chain Monte Carlo (MCMC) algorithms for 2-parameter logistic item response theory models and a generalized partial credit model. This innovative approach has intriguing promises, but significant barriers exist. To lower the barriers, there is a need to increase overlap in measures and timing of follow-up assessments across studies, better define treatment and control groups, and improve transparency and documentation in future single intervention studies.
本文概述了一项研究，该研究将多项独立收集的针对大学生的酒精干预研究整合为一个单一的多地点纵向数据集。这项研究采用了创新的分析策略（即使用个体参与者层面数据的综合数据分析或元分析），总体目标是回答个体研究中难以解决的研究问题，如调节分析，同时为所报告的针对大学生的简短动机干预的疗效提供内置的复制验证。数据来自24项干预研究，其中21项包括比较或对照条件，所有研究都包括一个或多个治疗条件。这产生了一个由12,630名参与者组成的样本（42%为男性；58%为一年级或新生）。样本中的大多数人认定为白人（74%），12%为亚洲人，7%为西班牙裔，2%为黑人，5%为其他/混合种族群体。参与者从基线到12个月接受了2次或更多次评估，不同研究的评估时间表各不相同。本文描述了我们如何合并来自多项研究的个体参与者层面数据，并讨论了通过协调以及针对双参数逻辑斯蒂项目反应理论模型和广义部分计分模型新开发的马尔可夫链蒙特卡罗（MCMC）算法，在各项研究中制定相应测量方法所采取的步骤。这种创新方法有诱人的前景，但也存在重大障碍。为了降低这些障碍，需要增加各项研究在测量方法和随访评估时间上的重叠，更好地定义治疗组和对照组，并提高未来单一干预研究的透明度和记录水平。
