Robust Transition Models for the Analysis of Longitudinal Drinking Outcomes
用于分析纵向饮酒结果的稳健转变模型
基本信息
- 批准号:8787586
- 负责人:
- 金额:$ 6.28万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2011
- 资助国家:美国
- 起止时间:2011-09-10 至 2014-07-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
DESCRIPTION (provided by applicant): Identifying effective treatments for alcoholism and predictors of transitional or pattern drinking are important goals of the NIAAA. In clinical studies, the derivation of appropriate drinking outcomes is often subject to debate. Both primary drinking and secondary non drinking outcomes, such as mood and quality of life, are important outcomes via which to assess treatment effects. However, commonly used summaries of self-reported drinking do not provide information about the effect of treatment and time dependent comorbidities on daily drinking behavior. To appropriately model the evolution of drinking and non drinking outcomes in response to these variables, statistical methods for densely measured longitudinal responses should be developed for use in this setting. They should handle outcomes that are measured using various metrics and that are dependent on multiple time varying factors. They should also mitigate the effect of measurement error inherent in self-report, as drinking summaries are typically reported using a calendar based method of recall. Finally, methods should lend themselves to simultaneous joint modeling of drinking and secondary nondrinking outcomes. Current statistical methods do not address all of this under a unified framework. Goals: Using a Bayesian paradigm, the proposed study will develop robust statistical methods addressing all of the above challenges for assessing treatment effectiveness on drinking and non drinking behavior, and for assessing the relevance of time dependent covariates on the evolution of drinking. Software will be developed and disseminated freely. Subjects: The statistical methods will be evaluated on two datasets, the Combined Pharmacotherapies and Behavioral Interventions for Alcohol Dependence (COMBINE) clinical trial (n=1383) and a prospective comorbidity study (n=663) assessing major depressive disorder on the course of alcohol and other substance dependence. Available data and study design: Daily or weekly responses such as drinking, other substance use, mood, depressive symptoms, craving, stress, and quality of life were collected and will be evaluated as outcomes. Treatment status, demographics, baseline measures, and prior psychiatric/health disorders will serve as baseline variables, and relevant medical status, adverse events, and onset of psychiatric disorders such as major depressive disorder that were measured throughout the studies will serve as predictors in the models. Both studies have an over representation of minority participants and include women in high proportions. Significance: The new statistical methods will provide alcohol researchers with a rich description of the behavioral evolution of primary drinking and secondary nondrinking outcomes in response to treatment and time dependent comorbidities throughout the course of these studies.
描述(由申请人提供):确定酒精中毒的有效治疗方法和过渡或模式饮酒的预测因素是NIAAA的重要目标。在临床研究中,适当饮酒结果的推导经常受到争论。主要饮酒和次要非饮酒结果,如情绪和生活质量,都是评估治疗效果的重要结果。然而,常用的自我报告饮酒总结并没有提供关于治疗和时间依赖性合并症对日常饮酒行为影响的信息。为了适当地模拟饮酒和非饮酒结果的演变,以应对这些变量,密集测量的纵向响应的统计方法,应在这种情况下使用。他们应该处理使用各种指标衡量的结果,这些结果取决于多个随时间变化的因素。他们还应该减轻自我报告中固有的测量误差的影响,因为饮酒总结通常是使用基于日历的回忆方法报告的。最后,方法应适用于饮酒和二次非饮酒结果的同时联合建模。目前的统计方法没有在一个统一的框架下处理所有这些问题。目标:使用贝叶斯范式,拟议的研究将开发强大的统计方法,解决所有上述挑战,以评估饮酒和非饮酒行为的治疗效果,并评估饮酒演变的时间依赖性协变量的相关性。软件将免费开发和传播。主题:将在两个数据集上评价统计方法,酒精依赖药物治疗和行为干预联合临床试验(联合收割机)(n=1383)和一项前瞻性合并症研究(n=663),评估酒精和其他物质依赖过程中的重度抑郁症。可用数据和研究设计:收集每日或每周的反应,如饮酒、其他物质使用、情绪、抑郁症状、渴望、压力和生活质量,并将其作为结果进行评估。治疗状态、人口统计学、基线测量和既往精神/健康疾病将作为基线变量,相关医学状态、不良事件和整个研究期间测量的精神疾病(如重度抑郁症)发作将作为模型中的预测因子。这两项研究都有少数民族参与者的代表性,包括妇女的比例很高。重要性:新的统计方法将为酒精研究人员提供丰富的描述,主要饮酒和次要非饮酒结果的行为演变,以应对整个研究过程中的治疗和时间依赖性合并症。
项目成果
期刊论文数量(7)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
A randomized, double-blind, placebo-controlled clinical trial of acamprosate in alcohol-dependent individuals with bipolar disorder: a preliminary report.
阿坎酸在酒精依赖型双相情感障碍患者中的随机、双盲、安慰剂对照临床试验:初步报告。
- DOI:10.1111/j.1399-5618.2011.00973.x
- 发表时间:2012
- 期刊:
- 影响因子:5.4
- 作者:Tolliver,BryanK;Desantis,StaciaM;Brown,DelisaG;Prisciandaro,JamesJ;Brady,KathleenT
- 通讯作者:Brady,KathleenT
Analyzing Propensity Matched Zero-Inflated Count Outcomes in Observational Studies.
- DOI:10.1080/02664763.2013.834296
- 发表时间:2014-01-01
- 期刊:
- 影响因子:1.5
- 作者:Desantis SM;Lazaridis C;Ji S;Spinale FG
- 通讯作者:Spinale FG
Zero-inflated count models for longitudinal measurements with heterogeneous random effects.
- DOI:10.1177/0962280215588224
- 发表时间:2017-08
- 期刊:
- 影响因子:2.3
- 作者:Zhu H;Luo S;DeSantis SM
- 通讯作者:DeSantis SM
Bayesian mixed treatment comparisons meta-analysis for correlated outcomes subject to reporting bias.
- DOI:10.1111/rssc.12220
- 发表时间:2018-01
- 期刊:
- 影响因子:0
- 作者:Liu Y;DeSantis SM;Chen Y
- 通讯作者:Chen Y
Simultaneous modeling of the impact of treatments on alcohol consumption and quality of life in the COMBINE study: a coupled hidden Markov analysis.
COMBINE 研究中治疗对饮酒量和生活质量影响的同步建模:耦合隐马尔可夫分析。
- DOI:10.1111/j.1530-0277.2012.01823.x
- 发表时间:2012
- 期刊:
- 影响因子:0
- 作者:Prisciandaro,JamesJ;DeSantis,StaciaM;Bandyopadhyay,Dipankar
- 通讯作者:Bandyopadhyay,Dipankar
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Dipankar Bandyopadhyay其他文献
Dipankar Bandyopadhyay的其他文献
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A pragmatic risk index evaluating the elderly with comorbidity for oral health event times
评估患有合并症的老年人口腔健康事件时间的实用风险指数
- 批准号:
10593634 - 财政年份:2022
- 资助金额:
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Sex/Gender influences on periodontal disease and diabetes: A population science approach, with software
性别/性别对牙周病和糖尿病的影响:人口科学方法与软件
- 批准号:
10531704 - 财政年份:2022
- 资助金额:
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Spatiotemporal models for periodontal disease monitoring and recall frequencies
牙周病监测和召回频率的时空模型
- 批准号:
8983525 - 财政年份:2015
- 资助金额:
$ 6.28万 - 项目类别:
Spatiotemporal models for periodontal disease monitoring and recall frequencies
牙周病监测和召回频率的时空模型
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9321599 - 财政年份:2015
- 资助金额:
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Exploring tooth survival using Bayesian spatial models
使用贝叶斯空间模型探索牙齿存活率
- 批准号:
8699584 - 财政年份:2014
- 资助金额:
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Exploring tooth survival using Bayesian spatial models
使用贝叶斯空间模型探索牙齿存活率
- 批准号:
8827320 - 财政年份:2014
- 资助金额:
$ 6.28万 - 项目类别:
Exploring tooth survival using Bayesian spatial models
使用贝叶斯空间模型探索牙齿存活率
- 批准号:
9195676 - 财政年份:2014
- 资助金额:
$ 6.28万 - 项目类别:
Robust spatial models for clustered periodontal data
牙周聚类数据的稳健空间模型
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8319854 - 财政年份:2011
- 资助金额:
$ 6.28万 - 项目类别:
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