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

描述（由申请人提供）：本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酒精治疗研究的自适应设计和以人为本的数据分析。正如挑战主题所暗示的那样，使用以变量为中心的方法进行统计分析（例如，平均值的比较）在许多酒精依赖的临床研究中是不够的。例如，统计假设（例如，正常情况下，通常会被违反。此外，这些方法不能充分说明饮酒结果的变化。同样，比较治疗和安慰剂的简单试验通常不能回答临床医生特别重要的问题，他们必须根据对初始和后续治疗的反应对同一患者做出一系列决定。最近，人们对使用各种药物作为减少酒精消费的心理社会治疗的有希望的替代品的兴趣和研究大大增加。其中许多研究采用回顾性方法收集了以人为中心的饮酒数据，例如，时间轴跟踪方法，以回忆和记录过去一周的每日饮酒结果。然后对这些每日饮酒记录进行总结和分析。例如，约翰逊等人（2003）在治疗评估期间浓缩了每日饮酒记录，而约翰逊等人（2007）则以每周形式浓缩了这些记录。然而，这种浓缩的结果并不像原始的每日饮酒记录那样具有信息性。此外，这些结果通常被假设为正态性，这可能会被违反。第三，饮酒结果的轨迹不能在这些分析中完全捕获。在这项拨款提案中，我们将开发新的统计方法来分析以人为本的数据，用于酒精治疗研究。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浓缩结果的方法更有效。其次，我们通过两部分模型（2PM）来处理每日饮酒结果的非正态性，以分别描述每日饮酒为零的几率和饮酒日的实际饮酒次数。我们还提出了新的方法来解决偏态和可能的异方差在积极的每日饮酒水平。第三，我们对饮酒结果随时间变化的轨迹感兴趣，以更好地捕捉结果的差异。我们将使用参数和半参数方法（例如，样条）来描述这样的时间模式。在许多简单的试验中，我们比较两个组，通常是治疗组和对照组，以确定干预的有效性。然而，当我们有兴趣从一系列剂量中确定最佳剂量时，这种研究是不够的。例如，约翰逊等人（2003，2007）在托吡酯试验中使用了剂量递增方案（从25 mg至300 mg）。在这些概念验证试验中，他们确定了托吡酯在改善饮酒结果方面的总体治疗效果。然而，托吡酯在不同剂量水平下的效果仍有待确定，以便我们可以确定最佳剂量，该剂量具有令人满意的疗效，同时将不良事件发生率降至最低。适应性设计可以提供一个潜在的解决方案。适应性设计背后的动机是将序贯设计的统计学优势与伦理要求结合起来，即根据先验知识和当前积累的数据，以被判定为最佳的剂量治疗尽可能多的患者。在这种情况下，连续重新评估方法（CRM）开辟了使用具有某些最佳特征的工作统计模型的领域（O 'Quigley，Pepe和Fisher，1990年）。将提出寻找最成功剂量（MSD）的新方法，以经济有效的方式确定最佳剂量。 公共卫生相关性（由申请人提供）：在这项资助提案中，我们将提出并应用几个创新模型来分析以人为本的酒精治疗研究数据。我们还将引入新的自适应设计，以具有成本效益的方式确定最佳剂量。我们希望这项研究的完成将加速酒精依赖研究中不同治疗方法的有效性比较过程。

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.