Siegel C, Laska E, Meisner M
Statistical Sciences and Epidemiology Division, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA.
Control Clin Trials. 1996 Oct;17(5):387-406. doi: 10.1016/s0197-2456(95)00259-6.
A statistical framework is presented for examining cost and effect data on competing interventions obtained from an RCT or from an observational study. Parameters of the join distribution of costs and effects or a regression function linking costs and effects are used to define cost-effectiveness (c-e) measures. Several new c-e measures are proposed that utilize the linkage between costs and effects on the patient level. These measures reflect perspectives that are different from those of the commonly used measures, such as the ratio of expected cost to expected effect, and they can lead to different relative rankings of the interventions. The cost-effectiveness of interventions are assessed statistically in a two stage procedure that first eliminates clearly inferior interventions. Members of the remaining admissible set are then rank ordered according to a c-e preference measure. Statistical techniques, particularly in the multivariate normal case, are given for several commonly used c-e measures. These techniques provide methods for obtaining confidence intervals, for testing the hypothesis of admissibility and for the equality of interventions, and for ranking interventions. The ideas are illustrated for a hypothetical clinical trial of antipsychotic agents for community-based persons with mental illness.
本文提出了一个统计框架，用于检验从随机对照试验（RCT）或观察性研究中获得的相互竞争干预措施的成本和效果数据。成本和效果的联合分布参数或连接成本与效果的回归函数用于定义成本效益（c-e）度量。提出了几种新的c-e度量，这些度量利用了患者层面上成本与效果之间的联系。这些度量所反映的观点不同于常用度量，例如预期成本与预期效果之比，它们可能导致对干预措施的不同相对排名。干预措施的成本效益通过一个两阶段程序进行统计评估，该程序首先排除明显较差的干预措施。然后，根据c-e偏好度量对剩余可接受集合的成员进行排序。针对几种常用的c-e度量，给出了统计技术，特别是在多元正态情况下的技术。这些技术提供了获取置信区间、检验可接受性假设和干预措施相等性假设以及对干预措施进行排名的方法。通过一项针对社区精神疾病患者的抗精神病药物假设临床试验对这些想法进行了说明。
