Innovative Analytic Methods of Person-Centered Data and Adaptive Designs for Alco

以人为中心的数据创新分析方法和 Alco 的自适应设计

• 批准号: 7828734
• 负责人: Bankole A Johnson
• 金额: $ 33.7万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7828734
• 关键词: Abstinence; Accounting; Address; Adverse event; Alcohol consumption; Alcohol dependence; Applications Grants; Area; Back; Characteristics; Clinical Research; Clinical Trials; Computer software; Data; Data Analyses; Data Set; Dose; Dropout; Drug Formulations; Ethics; HIV; Heavy Drinking; Knowledge; Life; Light; Malignant Neoplasms; Methods; Modeling; Motivation; Outcome; Patients; Pattern; Placebos; Records; Research; Research Personnel; Sampling; Scheme; Series; Site; Solutions; Statistical Methods; Statistical Models; Substance Addiction; Time; TimeLine; Treatment Efficacy; Upper arm; Work; alcohol abuse therapy; analytical method; base; comparative effectiveness; compare effectiveness; cost; cost effective; design; drinking; effectiveness research; flexibility; follow-up; improved; innovation; interest; person centered; processing speed; programs; psychosocial; public health relevance; response; topiramate; treatment effect; trend; trial comparing

DESCRIPTION (provided by applicant): This application addresses broad Challenge Area (05): Comparative Effectiveness Research and specific Challenge Topic: 05-AA-102 Adaptive Designs and Person-Centered Data Analysis for Alcohol Treatment Research. As the challenge topic implies, statistical analyses using variable-centered approaches (e.g., comparison of means) are insufficient in many clinical studies of alcohol dependence. For example, statistical assumptions (e.g., normality) are routinely violated. Also, such methods can not adequately account for variability in drinking outcome. Similarly, simple trials comparing a treatment and a placebo often do not answer questions of particular import to clinicians, who have to make a series of decisions in the same patient based upon response to initial and subsequent treatment. Recently, there has been substantially increased interest in - and research on - the use of various pharmacological agents as promising adjuncts to psychosocial treatment to reduce alcohol consumption. Many of these studies collected person-centered drinking data using retrospective method, e.g., the timeline follow-back method to recall and record the daily drinking outcome for the past week. These daily drinking records were then summarized and analyzed. For example, Johnson et al. (2003) condensed the daily drinking records in the treatment assessment periods, while Johnson et al. (2007) condensed them in the weekly format. However, such condensed outcomes are not as informative as the original daily drinking record. Also, normality is often assumed for these outcomes, which could be violated. Third, the trajectory of the drinking outcome can not be fully captured in these analyses. In this grant proposal we will develop new statistical methods to analyze person-centered data for alcohol treatment research. First, we will use the original daily drinking level as the response variable, thus our method is more efficient than those using the condensed outcomes. Second, we tackle the non-normality of the daily drinking outcome by two- part models (2PM) to separately describe the odds of daily drinking being zero and the actual number of drinks in a drinking day. We also propose new methods to tackle the skewness and possible heteroscedasticity in the positive daily drinking level. Third, we are interested in the trajectory of the drinking outcome over time to better capture differences in outcomes. We will use both parametric and semiparametric methods (e.g., splines) to describe such temporal patterns. In many simple trials, we compare two arms, often a treatment arm and a control arm, to determine the efficacy of the intervention. However, such studies are insufficient when we are interested in determining the optimal dose from a range of doses. For example, Johnson et al. (2003, 2007) used a dose escalation scheme (from 25 mg to 300 mg) in the topiramate trials. In these proof of concept trials, they established the overall topiramate treatment effect at improving drinking outcomes. However, the topiramate effect at different dose levels remains to be ascertained so that we can identify the best dose which has the satisfactory efficacy while minimizing the rate of adverse events. Adaptive designs can offer a potential solution. The motivation behind adaptive designs is to bring together the statistical advantages of a sequential design with the ethical imperative of treating as many patients as possible at a dose judged to be the best, in the light of prior knowledge and the current accumulated data. In this context, the continual reassessment method (CRM) opened up the field to the use of working statistical models which have some optimal characteristics (O'Quigley, Pepe and Fisher, 1990). New methods to find the most successful dose (MSD) will be proposed to identify the optimal dose in a cost-effective way. PUBLIC HEALTH RELEVANCE (provided by applicant): In this grant proposal we will propose and apply several innovative models to analyze the person-centered data for alcohol treatment research. We will also introduce new adaptive designs to identify optimal dose in a cost-effective way. We expect that the completion of this study will speed the process of comparing effectiveness of different treatments in alcohol dependence studies.

描述（由申请人提供）：此申请涉及广泛的挑战领域（05）：比较有效性研究和特定挑战主题：05-AA-102自适应设计和以人为中心的酒精治疗研究分析。正如挑战主题所暗示的那样，在许多酒精依赖性临床研究中，使用以可变为中心的方法（例如，平均值比较）进行统计分析不足。例如，统计假设（例如，正态性）通常会违反。同样，这种方法无法充分说明饮酒结果的可变性。同样，比较治疗和安慰剂的简单试验通常不会回答特定进口的问题，他们必须根据对初始和随后的治疗的反应在同一患者中做出一系列决定。最近，人们对使用各种药理剂作为有望减少饮酒量的有前途的辅助剂的兴趣大大增加了。这些研究中的许多研究使用回顾性方法收集了以人为中心的饮酒数据，例如，时间表跟随方法来回忆和记录过去一周的每日饮酒结果。然后对这些日常饮酒记录进行了总结和分析。例如，Johnson等。 （2003年）在治疗评估期间凝结了每日饮酒记录，而Johnson等人。 （2007年）以每周​​格式凝结了它们。但是，这种凝结的结果并不像原始的每日饮酒记录那样提供信息。同样，对于这些结果，通常会假定正态性，这可能会违反。第三，在这些分析中无法完全捕获饮酒结果的轨迹。在这项赠款建议中，我们将开发新的统计方法来分析以人为中心的酒精治疗研究的数据。首先，我们将使用原始的每日饮酒水平作为响应变量，因此我们的方法比使用凝结结果的方法更有效。其次，我们通过两部分模型（下午2点）来解决日常饮酒结果的非正常性，以分别描述每天饮酒的几率为零和饮酒日的实际饮料数量。我们还提出了新的方法来应对日常饮酒水平积极的偏度和可能的异质性。第三，我们对随着时间的推移饮酒结果的轨迹感兴趣，以更好地捕捉结果的差异。我们将同时使用参数和半参数方法（例如花键）来描述这种时间模式。在许多简单的试验中，我们比较了两个手臂，通常是一个治疗臂和一个对照组来确定干预的功效。但是，当我们有兴趣确定一系列剂量的最佳剂量时，此类研究不足。例如，Johnson等。 （2003，2007）在托吡酯试验中使用了剂量升级方案（从25 mg到300 mg）。在这些概念试验证明中，他们确立了改善饮酒结果的整体托吡酯治疗效果。但是，在不同剂量水平上的托吡酯效应仍有待确定，因此我们可以确定具有令人满意的功效的最佳剂量，同时最大程度地减少不良事件的速度。自适应设计可以提供潜在的解决方案。自适应设计背后的动机是将顺序设计的统计优势汇总在一起，鉴于先前的知识和当前的累积数据，以判断为最好的剂量的尽可能多的患者的道德要求。在这种情况下，持续的重新评估方法（CRM）为使用具有一些最佳特征的工作统计模型打开了领域（O'Quigley，Pepe和Fisher，1990）。将提出最成功剂量（MSD）的新方法，以以具有成本效益的方式识别最佳剂量。 公共卫生相关性（由申请人提供）：在本赠款提案中，我们将提出并采用几种创新模型来分析以人为本的数据进行酒精治疗研究。我们还将引入新的自适应设计，以一种经济有效的方式识别最佳剂量。我们预计这项研究的完成将加快比较酒精依赖研究中不同治疗方法的有效性的过程。