STATISTICAL ANALYSIS OF DRUG ABUSE TREATMENT RESEARCH

两组、纵向比较是目前最多的 药物滥用结果研究中临床试验的常见设计。至 分析来自这样的研究的数据，有几个统计过程 可用，其理论属性已被理解。什么不是这样的？ 众所周知，在分析真实数据时应该使用这些方法中的哪一种 在药物滥用中经常遇到的限制下收集的数据 学习。这些限制包括高流失率、受限的 样本大小，以及不同的评估对评估的相关性。这 是一个复杂的问题，而且统计理论和 研究经常超过数据收集和实际情况 分析。 拟议的研究将通过调查五项统计数据来解决这一问题 方法使用模拟数据，然后用真实数据验证结果 数据。研究1、研究2和研究3将比较 用于分析两组纵向设计的尺寸和功率， 坡度的单项最小二乘估计对比综述 四种可选程序的统计：重复测量分析 方差、随机回归建模、变更前后和归一化 曲线下面积。研究1将调查四个方面的影响 控制可用于分析的有效样本量的因素： 1)初始样本量；2)每个样本的最小观察数 分析所需的对象；3)流失率；4)形状 自然减员曲线。在研究2中，我们将考察四个因素 与治疗效果的大小和类型相关，如 各评价点的组别平均数分布。我们将有所不同：1) 效果大小；2)存在或不存在交互作用；3) 评估；4)评估水平与评估的相互关系。 第三项研究将考察在多大程度上 非随机缺失数据对所获得的概率有偏差。其有效性 的模拟结果将在研究4中通过应用 数据集分析的五种统计方法 实际的药物滥用随机研究。得出的结论 模拟将被用来预测每种方法的行为 对数据集的功能特性进行了分析。调查结果来自 这四项研究将改进对纵向数据的分析 所有领域的临床试验，特别是药物滥用治疗研究。

The two-group, longitudinal comparison is currently one of the most common designs of clinical trials in drug abuse outcome research. To analyze data from such a study, there are several statistical procedures available whose theoretical properties are understood. What is not so well-known is which of these methods should be used when analyzing real data collected under the constraints often encountered in drug abuse studies. Such constraints include high rates of attrition, restricted sample sizes, and varying assessment-to-assessment correlations. This is a complex question, compounded by the fact that statistical theory and research frequently outpace the realities of data collection and analysis. The proposed research will address this by investigating five statistical methods using simulated data and then confirm those results using real data. Studies 1, 2, and 3 will compare the statistical properties of size and power for the analysis of a two-group longitudinal design, contrasting individual Least-Squares Estimates of Slope as a summary statistic to four alternative procedures: Repeated Measures Analysis of Variance, Random Regression Modeling, Pre-Post Change, and the Normalized Area Under the Curve. Study 1 will investigate the effects of four factors that control the effective sample size available for analysis: 1) initial sample size; 2) the minimum number of observations per subject required for analysis; 3) the rate of attrition and; 4) the shape of the attrition curve. In Study 2, we will examine four factors associated with the size and type of treatment effect as reflected by the distribution of group means at each assessment point. We will vary: 1) effect size; 2) presence or absence of an interaction; 3) the number of assessments and; 4) level of assessment to assessment intercorrelation. The third study will examine the extent to which the presence of nonrandom missing data biases the obtained probabilities. The validity of the simulation results will then be established in Study 4 by applying the five statistical methods to the analysis of data sets drawn from actual randomized studies of drug abuse. Conclusions from the simulations will be used to predict the behavior of each method as a function of the characteristics of the data set analyzed. Findings from these four studies will improve the analysis of data from longitudinal clinical trials in all fields, especially drug abuse treatment research.