A comparison of various estimators of a treatment difference for a multi-centre clinical trial

Abstract

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.

Comparing methods to estimate treatment effects on a continuous outcome in multicentre randomized controlled trials: A simulation study

“…To date, only a few studies have been carried out to compare the performance of statistical models in analyzing multicentre RCTs using Monte Carlo simulation [6,7,14], whereas many studies assessed the impact of ICC in cluster randomization trials. Moerbeek et al [6] compared the simple linear regression model, fixed-effects regression and fixed-effects centre-level analysis with equal centre size.…”

“…Performance of the GEE approach and centre-level methods were not investigated in that work. Jones et al [14] compared the fixed-effects and random-effects regression models to a two-step Frequentist procedure as well as a Bayesian model, in the presence of treatment by centre interaction, and recommended fixed-effects weighted method for future analysis of multicentre trials. The investigation was further expanded to assessing correlated survival outcomes from large multicentre cancer trials.…”

Wilcoxon–Mann–Whitney Test: Stratify or Not?

“…The decision on whether to adjust for strata or not in linear models has been researched extensively (Fleiss, 1986;Ganju and Mehrotra, 2003;Gould, 1998;Jones et al, 1998;Schwemer, 2000;Searle, 1987). Generally, including stratum effects depends on the trade-off between the reduction in mean squared errors (MSE) and the loss of degrees of freedom.…”

Sample Size Requirements and Study Duration for Testing Main Effects and Interactions in Completely Randomized Factorial Designs When Time to Event is the Outcome

“…Pothoff, Peterson, and George 1 describe various procedures for testing the interaction of two treatments by multiple centers in the 2× b factorial setting. The general issue of testing treatment by center interactions has been discussed by many authors, including Kallen 8 , Snapinn 9 , Senn 10 , Jones 11 et al, Gould 12 , and Gallo 13 . In a more general factorial setting, McAlister et al 14 discuss examining interaction effects by calculating interaction ratios.…”