Jones B, Teather D, Wang J, Lewis J A
Department of Medical Statistics, Faculty of Computing Sciences and Engineering, De Montfort University, Gateway, Leicester, U.K.
Stat Med. 1998;17(15-16):1767-77; discussion 1799-800. doi: 10.1002/(sici)1097-0258(19980815/30)17:15/16<1767::aid-sim978>3.0.co;2-h.
When a clinical trial is conducted at more than one centre it is likely that the true treatment effect will not be identical at each centre. In other words there will be some degree of treatment-by-centre interaction. A number of alternative approaches for dealing with this have been suggested in the literature. These include frequentist approaches with a fixed or random effects model for the observed data and Bayesian approaches. In the fixed effects model, there are two common competing estimators of the treatment difference, based on weighted or unweighted estimates from individual centres. Which one of these should be used is the subject of some controversy and we do not intend to take a particular methodological position in this paper. Our intention is to provide some insight into the relative merits of the indicated range of possible estimators of the treatment effect. For the fixed effects model, we also look at the merits of using a preliminary test for interaction assuming a 10 per cent significance level for the test. In order to make comparisons we have simulated a 'typical' trial which compares an active drug with a placebo in the treatment of hypertension, using systolic blood pressure as the primary variable. As well as allowing the treatment effect to vary between centres, we have concentrated on the particular case where one centre is out of line with the others in terms of its true treatment difference. The various estimators that result from the different approaches are compared in terms of mean squared error and power to reject the null hypothesis of no treatment difference. Overall, the approach that uses the fixed effects weighted estimator of overall treatment difference is recommended as one that has much to offer.
当在多个中心进行临床试验时，各中心的真实治疗效果可能并不相同。换句话说，会存在一定程度的治疗与中心之间的交互作用。文献中已提出了多种应对此问题的替代方法。这些方法包括对观测数据采用固定效应模型或随机效应模型的频率学派方法以及贝叶斯方法。在固定效应模型中，基于各个中心的加权或未加权估计，有两种常见的相互竞争的治疗差异估计量。应使用其中哪一个存在一些争议，并且我们在本文中不打算采取特定的方法论立场。我们的目的是对治疗效果的一系列可能估计量的相对优点提供一些见解。对于固定效应模型，我们还探讨了在假设检验显著性水平为10%的情况下使用交互作用初步检验的优点。为了进行比较，我们模拟了一项“典型”试验，该试验在高血压治疗中比较一种活性药物与一种安慰剂，将收缩压作为主要变量。除了允许治疗效果在各中心之间有所不同外，我们还专注于一种特殊情况，即一个中心在其真实治疗差异方面与其他中心不一致。根据均方误差和拒绝无治疗差异原假设的功效，对不同方法产生的各种估计量进行了比较。总体而言，建议使用总体治疗差异的固定效应加权估计量的方法，因为它有很多优点。
