权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Predicting individual responses to treatment for alcohol use disorder.

预测个体对酒精使用障碍治疗的反应。

基本信息

批准号：
10659811
负责人：
M LEE VAN HORN
金额：
$ 61.1万
依托单位：
UNIVERSITY OF NEW MEXICO
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-04-20 至 2028-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10659811
关键词：
Alcohol consumption Algorithms Area Behavior Therapy Behavioral Biological Markers Caring Characteristics Client Clinical Clinical Data Clinical Trials Clinical Trials Cooperative Group Communication Complex Data Disease Ensure Goals Heavy Drinking Heterogeneity Individual Individual Differences Intervention Literature Methods Naltrexone Outcome Patients Pharmaceutical Preparations Pharmacological Treatment Prediction of Response to Therapy Prevention program Provider Psychosocial Factor Public Health Publishing Randomized, Controlled Trials Reaction Relapse Research Research Personnel Sampling Selection for Treatments Specific qualifier value Symptoms Telephone Testing Treatment Effectiveness Treatment Efficacy Treatment outcome Validation Work acamprosate alcohol abuse therapy alcohol intervention alcohol response alcohol use disorder behavior test clinical decision-making clinical practice design experience improved improved outcome individual response individualized medicine machine learning algorithm mindfulness intervention novel strategies personalized approach personalized medicine pharmacologic predicting response prediction algorithm predictive modeling randomized trial randomized, clinical trials relapse prevention response simulation theories topiramate treatment effect treatment response usability

项目摘要

Project summary: Treatment of alcohol use disorder (AUD) is characterized by common relapse, heterogeneity in findings, and many diverse interventions which show modest efficacy but fail to out perform each other. Research aiming to explain the existing heterogeneity has found many significant moderators of treatment effects but few of these have effect sizes large enough to indicate that they should be used in clinical practice for targeting treatments. New personalized medicine methods which use machine learning algorithms to create predictions of responses to AUD treatment which take into account multiple predictors show early promise. This research This research uses data from 11 randomized clinical trials, 6 of behavioral relapse prevention programs and 5 of pharmacological interventions to reduce heavy drinking, to develop and cross validate individual predictions of treatment effects on heavy drinking. We will also test the significance of individual differences for each intervention and provide predictive intervals for individuals describing their expected response to different interventions. The study also aims to test new approaches for combining data across multiple trials and for improving precision of predictions in order to make the use of the predicted individual treatment effects (PITEs) framework more useful in clinical practice. At the end of this study there will be published algorithms for comparing predictions of treatment effects for new individuals across multiple treatments, predictive intervals for those effects, and an assessment of internal and, where possible, external validation of those predictions. The work emphasizes replicability of results through cross-validation (which will itself be tested with simulations), a priori specification of predictive methods and covariates, and use of an expert panel to make theory and literature informed decisions. This research is designed to make personalized medicine for treatment of AUD usable in clinical practice through its integration of theory, clinical experience brought by the clinical advisory board, and clear communication of results to a clinical audience.

项目总结: