Sugar Catherine A, James Gareth M, Lenert Leslie A, Rosenheck Robert A
Marshall School of Business, University of Southern California, Los Angeles, California 90089-0809, USA.
Med Care. 2004 Feb;42(2):183-96. doi: 10.1097/01.mlr.0000108748.13206.ba.
The objective of this study was to demonstrate a multivariate health state approach to analyzing complex disease data that allows projection of long-term outcomes using clustering, Markov modeling, and preference weights.
We studied patients hospitalized 30 to 364 days with refractory schizophrenia at 15 Veterans Affairs medical centers.
We conducted a randomized clinical trial comparing clozapine, an atypical antipsychotic, and haloperidol, a conventional antipsychotic.
Health status instruments measuring disease-related symptoms and drug side effects were administered in face-to-face interviews at baseline, 6 weeks, and quarterly follow-up intervals for 1 year. Cost data were derived from Veterans Affairs records supplemented by interviews. K-means clustering was used to identify a small number of health states for each instrument. Markov modeling was used to estimate long-term outcomes.
Multivariate models with 7 and 6 states, respectively, were required to describe patterns of psychiatric symptoms and side effects (movement disorders). Clozapine increased the proportion of clients in states characterized by mild psychiatric symptoms and decreased the proportion with severe positive symptoms but showed no long-term benefit for negative symptoms. Clozapine dramatically increased the proportion of patients with no movement side effects and decreased incidences of mild akathisia. Effects on extrapyramidal symptoms and tardive dyskinesia were far less pronounced and slower to develop. Markov modeling confirms the consistency of these findings.
Analyzing complex disease data using multivariate health state models allows a richer understanding of trial effects and projection of long-term outcomes. Although clozapine generates substantially fewer side effects than haloperidol, its impact on psychiatric aspects of schizophrenia is less robust and primarily involves positive symptoms.
本研究的目的是展示一种多变量健康状态方法，用于分析复杂疾病数据，该方法允许使用聚类、马尔可夫模型和偏好权重来预测长期结果。
我们研究了在15家退伍军人事务医疗中心住院30至364天的难治性精神分裂症患者。
我们进行了一项随机临床试验，比较非典型抗精神病药物氯氮平和传统抗精神病药物氟哌啶醇。
在基线、6周以及每年的季度随访期间，通过面对面访谈使用健康状况工具测量与疾病相关的症状和药物副作用。成本数据来自退伍军人事务记录，并通过访谈进行补充。使用K均值聚类为每种工具识别少量健康状态。使用马尔可夫模型估计长期结果。
分别需要具有7个和6个状态的多变量模型来描述精神症状和副作用（运动障碍）的模式。氯氮平增加了以轻度精神症状为特征状态的患者比例，降低了严重阳性症状患者的比例，但对阴性症状没有长期益处。氯氮平显著增加了无运动副作用患者的比例，并降低了轻度静坐不能的发生率。对锥体外系症状和迟发性运动障碍的影响远不那么明显，且发展较慢。马尔可夫模型证实了这些发现的一致性。
使用多变量健康状态模型分析复杂疾病数据可以更深入地理解试验效果并预测长期结果。尽管氯氮平产生的副作用比氟哌啶醇少得多，但其对精神分裂症精神方面的影响较弱，主要涉及阳性症状。
