http://repub.eur.nl/res/aut/28348/ List of Publications en http://repub.eur.nl/static-eur/img/logo.png http://repub.eur.nl http://repub.eur.nl/res/pub/25183/ 2011-05-25T00:00:00Z Background: Logistic random effects models are a popular tool to analyze multilevel also called hierarchical data with a binary or ordinal outcome. Here, we aim to compare different statistical software implementations of these models. Methods. We used individual patient data from 8509 patients in 231 centers with moderate and severe Traumatic Brain Injury (TBI) enrolled in eight Randomized Controlled Trials (RCTs) and three observational studies. We fitted logistic random effects regression models with the 5-point Glasgow Outcome Scale (GOS) as outcome, both dichotomized as well as ordinal, with center and/or trial as random effects, and as covariates age, motor score, pupil reactivity or trial. We then compared the implementations of frequentist and Bayesian methods to estimate the fixed and random effects. Frequentist approaches included R (lme4), Stata (GLLAMM), SAS (GLIMMIX and NLMIXED), MLwiN ([R]IGLS) and MIXOR, Bayesian approaches included WinBUGS, MLwiN (MCMC), R package MCMCglmm and SAS experimental procedure MCMC. Three data sets (the full data set and two sub-datasets) were analysed using basically two logistic random effects models with either one random effect for the center or two random effects for center and trial. For the ordinal outcome in the full data set also a proportional odds model with a random center effect was fitted. Results: The packages gave similar parameter estimates for both the fixed and random effects and for the binary (and ordinal) models for the main study and when based on a relatively large number of level-1 (patient level) data compared to the number of level-2 (hospital level) data. However, when based on relatively sparse data set, i.e. when the numbers of level-1 and level-2 data units were about the same, the frequentist and Bayesian approaches showed somewhat different results. The software implementations differ considerably in flexibility, computation time, and usability. There are also differences in the availability of additional tools for model evaluation, such as diagnostic plots. The experimental SAS (version 9.2) procedure MCMC appeared to be inefficient. Conclusions: On relatively large data sets, the different software implementations of logistic random effects regression models produced similar results. Thus, for a large data set there seems to be no explicit preference (of course if there is no preference from a philosophical point of view) for either a frequentist or Bayesian approach (if based on vague priors). The choice for a particular implementation may largely depend on the desired flexibility, and the usability of the package. For small data sets the random effects variances are difficult to estimate. In the frequentist approaches the MLE of this variance was often estimated zero with a standard error that is either zero or could not be determined, while for Bayesian methods the estimates could depend on the chosen "non-informative" prior of the variance parameter. The starting value for the variance parameter may be also critical for the convergence of the Markov chain. http://repub.eur.nl/res/pub/23057/ 2011-03-01T00:00:00Z Background: Differences between centers in patient outcome after traumatic brain injury are of importance for multicenter studies and have seldom been studied. Objective: To quantify the differences in centers enrolling patients in randomized clinical trials (RCTs) and surveys. Methods: We analyzed individual patient data from 9578 patients with moderate and severe traumatic brain injury enrolled in 10 RCTs and 3 observational studies. We used random-effects logistic regression models to estimate the between-center differences in unfavorable outcome (dead, vegetative state, or severe disability measured with the Glasgow Outcome Scale) at 6 months adjusted for differences in patient characteristics. We calculated the difference in odds of unfavorable outcome between the centers at the higher end vs those at the lower end of the outcome distribution. We analyzed the total database, Europe and the United States separately, and 4 larger RCTs. Results: The 9578 patients were enrolled at 265 centers, and 4629 (48%) had an unfavorable outcome. After adjustment for patient characteristics, there was a 3.3-fold difference in the odds of unfavorable outcome between the centers at the lower end of the outcome distribution (2.5th percentile) vs those at the higher end of the outcome distribution (97.5th percentile; P < .001). In the 4 larger RCTs, the differences between centers were similar. However, differences were smaller between centers in the United States (2.4-fold) than between centers in Europe (3.8-fold). Conclusion: Outcome after traumatic brain injury differs substantially between centers, particularly in Europe. Further research is needed to study explanations for these differences to suggest where quality of care might be improved.