Summary Despite the use of standardized protocols in, multicentre, randomised clinical trials (RCTs), outcome may vary between centres. Such heterogeneity may alter the interpretation and reporting of the treatment effect. Below, we propose a general frailty modelling approach for investigating, inter alia, putative treatment-by-centre interactions in time-to-event data in multi-centre clinical trials. A correlated random effects model is used to model the baseline risk and the treatment effect across centres. It may be based on shared, individual or correlated random-effects. For inference we develop the hierarchical-likelihood (or h-likelihood) approach which facilitates computation of prediction intervals for the random effects with proper precision. We illustrate our methods using disease-free time-to-event data on bladder cancer patients participating in an European Organization for Research and Treatment of Cancer (EORTC) trial, and a simulation study. We also demonstrate model selection using h-likelihood criteria.

No abstract

5In a prospective single-center longitudinal randomized controlled trial 116 patients were 6 allocated to the sub-vastus approach, and 115 to the medial parapatellar approach. At one 7 week follow-up, compared to baseline, range of motion, Knee Society (KS) global, KS 8 knee, and KS pain scores were significantly better in the sub-vastus group. At the one year 9 follow-up WOMAC global and pain scores, SF36 physical function and role-physical 10 scores, and EuroQol utility and pain score were significantly better in the sub-vastus 11 group. The ease of exposure in the sub-vastus approach was significantly worse. There 12 was no significant difference in length of stay or analgesia intake. The sub-vastus approach 13 to total knee arthroplasty was more effective than a medial parapatellar approach at both 14 one week and fifty-two weeks post-operatively, but surgeons reported a less easy exposure 15 in the sub-vastus group. 16[ISRCTN44544446] 17 18

Different definitions of 'significant' ST elevation led to marked variations in sensitivity and specificity for diagnosis of acute myocardial infarction. Multiple QRST features in addition to ST elevation only marginally improved overall classification.

Various frailty models have been developed and are now widely used for analysing multivariate survival data. It is therefore important to develop an information criterion for model selection. However, in frailty models there are several alternative ways of forming a criterion and the particular criterion chosen may not be uniformly best. In this paper, we study an Akaike information criterion (AIC) on selecting a frailty structure from a set of (possibly) non-nested frailty models. We propose two new AIC criteria, based on a conditional likelihood and an extended restricted likelihood (ERL) given by Lee and Nelder (J. R. Statist. Soc. B 1996; 58:619-678). We compare their performance using well-known practical examples and demonstrate that the two criteria may yield rather different results. A simulation study shows that the AIC based on the ERL is recommended, when attention is focussed on selecting the frailty structure rather than the fixed effects.