STATISTICAL ANALYSIS OF DRUG ABUSE TREATMENT RESEARCH

两组的纵向比较是目前最 药物滥用结果研究中临床试验的常见设计。 到 分析这样的研究数据，有几个统计程序 它的理论性质是已知的。 什么不是这样 在分析真实的时， 在药物滥用经常遇到的限制下收集的数据 问题研究 这些制约因素包括高自然减员率， 样本量和不同的评估与评估相关性。 这 是一个复杂的问题，更复杂的是，统计理论和 研究的速度经常超过数据收集的实际速度， 分析. 拟议的研究将通过调查五个统计数据来解决这一问题。 方法使用模拟数据，然后用真实的 数据 研究1、2和3将比较 两组纵向设计分析的尺寸和功效， 对比斜率的单个最小二乘估计值作为总结 四种替代方法的统计： 方差、随机回归建模、前后变化和标准化 曲线下面积 研究1将研究四种药物的作用 控制可用于分析的有效样本量的因素： 1)初始样本量; 2）每个样本的最小观测数 分析所需的主题; 3）磨损率; 4）形状 的磨损曲线。 在研究2中，我们将研究四个因素 与治疗效果的大小和类型相关， 每个评估点的组平均值分布。 我们会有所不同：1） 效应量; 2）相互作用的存在或不存在; 3） 评估; 4）评估水平与评估相关性。 第三项研究将审查在多大程度上存在的 非随机缺失数据使获得的概率有偏差。 有效性 然后将在研究4中通过应用建立模拟结果 五种统计方法分析数据集， 药物滥用的随机研究 的结论 模拟将用于预测每种方法的行为， 分析数据集的特征函数。 的结果 这四项研究将改善对纵向数据的分析， 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.