Hedeker D, Mermelstein R J
Prevention Research Center, University of Illinois at Chicago 60612-7260, USA.
Addiction. 1996 Dec;91 Suppl:S211-29.
This article describes and illustrates use of random-effects regression models (RRM) in relapse research. RRM are useful in longitudinal analysis of relapse data since they allow for the presence of missing data, time-varying or invariant covariates, and subjects measured at different timepoints. Thus, RRM can deal with "unbalanced" longitudinal relapse data, where a sample of subjects are not all measured at each and every timepoint. Also, recent work has extended RRM to handle dichotomous and ordinal outcomes, which are common in relapse research. Two examples are presented from a smoking cessation study to illustrate analysis using RRM. The first illustrates use of a random-effects ordinal logistic regression model, examining longitudinal changes in smoking status, treating status as an ordinal outcome. The second example focuses on changes in motivation scores prior to and following a first relapse to smoking. This latter example illustrates how RRM can be used to examine predictors and consequences of relapse, where relapse can occur at any study timepoint.
本文描述并举例说明了随机效应回归模型（RRM）在复发研究中的应用。RRM在复发数据的纵向分析中很有用，因为它们允许存在缺失数据、随时间变化或不变的协变量，以及在不同时间点测量的受试者。因此，RRM可以处理“不平衡”的纵向复发数据，即并非所有受试者样本都在每个时间点进行测量。此外，最近的研究工作扩展了RRM以处理二分法和有序结果，这在复发研究中很常见。本文给出了一项戒烟研究的两个例子，以说明使用RRM进行分析的情况。第一个例子说明了随机效应有序逻辑回归模型的应用，将治疗状态作为有序结果来检查吸烟状态的纵向变化。第二个例子关注首次复吸前后动机得分的变化。后一个例子说明了如何使用RRM来检查复发的预测因素和后果，其中复发可能发生在任何研究时间点。
