STATISTICAL ANALYSIS OF DRUG ABUSE TREATMENT RESEARCH

药物滥用治疗研究的统计分析

• 批准号: 6238003
• 负责人: Kevin L. Delucchi
• 金额: $ 32.48万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-09-01 至 1998-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6238003
• 关键词: computer simulation; data collection methodology /evaluation; drug abuse therapy; human data; longitudinal human study; statistics /biometry

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.

两组纵向比较目前是最多的 药物滥用结果研究中临床试验的常见设计。 到 分析此类研究的数据，有几种统计程序 可以理解其理论特性的可用。 不是这样 众所周知是在分析真实时应使用以下哪种方法 在药物滥用中经常遇到的约束下收集的数据 研究。 这样的限制包括高损耗率，受限制 样本量和不同的评估与评估相关性。 这 是一个复杂的问题，统计理论和 研究经常超过数据收集和 分析。 拟议的研究将通过研究五个统计来解决这一问题 使用模拟数据的方法，然后使用真实的方法确认这些结果 数据。 研究1、2和3将比较 分析两组纵向设计的大小和功率， 将坡度的单个最小二乘估计作为摘要 四个替代程序的统计数据：重复测量分析 方差，随机回归建模，验证前变化和归一化 曲线下的区域。 研究1将研究四个的影响 控制有效样本量可用于分析的因素： 1）初始样本量； 2）最小观测数量 分析所需的主题； 3）流失率和； 4）形状 损耗曲线。 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 小组平均值在每个评估点的分布。 我们将有所不同：1） 效应大小； 2）存在或不存在相互作用； 3）数量 评估和； 4）评估与评估相互关系的水平。 第三项研究将研究存在的程度 非随机丢失的数据偏向获得的概率。 有效性 然后，将在研究4中建立仿真结果 从 对药物滥用的实际随机研究。 结论 模拟将用于预测每种方法的行为 分析数据集特性的功能。 来自 这四项研究将改善纵向数据的分析 所有领域的临床试验，尤其是药物滥用治疗研究。