W. Ling, S. Shoptaw, D. Wesson, R. Rawson, Margaret A. Compton, Klett Cj
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In order to optimize the likelihood of both detecting individual episodes of problem drug use and correctly inferring drug abstinence based on urine toxicology results, guidelines have been suggested for collection procedures and timing for collection of urine specimens (Blaine et al. 1994; Cone and Dickerson 1992; Jain 1992). However, the difficult task of aggregating urine toxicology results remains, whether when interpreting the response of a single patient to a specific treatment or when evaluating a treatment’s effectiveness based on a group of patients’ responses in a clinical trial. Difficulties in aggregating urine toxicology results include, but certainly would not be limited to, such problems as the frequency and sensitivity of toxicology screens, early termination of some patients from treatment (or, conversely, the continued participation of some patients who respond poorly to treatment), and problems of analyzing a data matrix that contains a large number of missing datapoints. This chapter reviews the objective indices of treatment response that have traditionally been used and suggests three composite methods for evaluating these data: the Treatment Effectiveness Score (TES), the Joint Probability score (JP), and the Clinical Stabilization Score (CSS).","PeriodicalId":76229,"journal":{"name":"NIDA research monograph","volume":"175 1","pages":"208-20"},"PeriodicalIF":0.0000,"publicationDate":"1997-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"52","resultStr":"{\"title\":\"Treatment effectiveness score as an outcome measure in clinical trials.\",\"authors\":\"W. Ling, S. Shoptaw, D. Wesson, R. Rawson, Margaret A. Compton, Klett Cj\",\"doi\":\"10.1037/e495552006-011\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A variety of measures are used for evaluating patients’ responses to substance abuse treatments. 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引用次数: 52
摘要
各种措施被用来评估病人对药物滥用治疗的反应。这些范围从物理测量(如尿液、呼吸、头发或血液样本),药物使用的自我报告(如成瘾严重程度指数(ASI)或时间线追踪),心理或生理功能的自我报告(如症状清单或渴望或情绪评级),以及附带报告。近期药物使用的物理指标,如尿液毒理学筛查,因其客观性而优于自我报告或附带报告,以评估患者对药物滥用治疗的反应。为了优化发现问题药物使用的个体事件和根据尿液毒理学结果正确推断药物戒断的可能性,已经提出了收集尿液标本的程序和时间指南(Blaine等人,1994;Cone and Dickerson 1992;耆那教的1992)。然而,汇总尿液毒理学结果的艰巨任务仍然存在,无论是在解释单个患者对特定治疗的反应时,还是在临床试验中基于一组患者的反应来评估治疗的有效性时。汇总尿液毒理学结果的困难包括,但肯定不限于,毒理学筛查的频率和敏感性,一些患者早期终止治疗(或者相反,一些对治疗反应不佳的患者继续参与治疗),以及分析包含大量缺失数据点的数据矩阵的问题。本章回顾了传统上使用的治疗反应的客观指标,并提出了评估这些数据的三种综合方法:治疗效果评分(TES)、联合概率评分(JP)和临床稳定评分(CSS)。
Treatment effectiveness score as an outcome measure in clinical trials.
A variety of measures are used for evaluating patients’ responses to substance abuse treatments. These range from physical measures (such as samples of urine, breath, hair, or blood), self-reports of drug use (such as the Addiction Severity Index (ASI) or the Time Line Follow-Back), self-reports of psychological or physiological functioning (such as symptom checklists or craving or mood ratings), and collateral reports. Physical indices of recent drug use, such as urine toxicology screens, are preferable to self-report or collateral reports for evaluating patients’ responses to drug abuse treatments because of their objectivity. In order to optimize the likelihood of both detecting individual episodes of problem drug use and correctly inferring drug abstinence based on urine toxicology results, guidelines have been suggested for collection procedures and timing for collection of urine specimens (Blaine et al. 1994; Cone and Dickerson 1992; Jain 1992). However, the difficult task of aggregating urine toxicology results remains, whether when interpreting the response of a single patient to a specific treatment or when evaluating a treatment’s effectiveness based on a group of patients’ responses in a clinical trial. Difficulties in aggregating urine toxicology results include, but certainly would not be limited to, such problems as the frequency and sensitivity of toxicology screens, early termination of some patients from treatment (or, conversely, the continued participation of some patients who respond poorly to treatment), and problems of analyzing a data matrix that contains a large number of missing datapoints. This chapter reviews the objective indices of treatment response that have traditionally been used and suggests three composite methods for evaluating these data: the Treatment Effectiveness Score (TES), the Joint Probability score (JP), and the Clinical Stabilization Score (CSS).