http://repub.eur.nl/res/aut/14973/ List of Publications en http://repub.eur.nl/static-eur/img/logo.png http://repub.eur.nl http://repub.eur.nl/res/pub/33929/ 2011-12-09T00:00:00Z Objective: We aimed to determine to what extent covariate adjustment could affect power in a randomized controlled trial (RCT) of a heterogeneous population with traumatic brain injury (TBI). Study Design and Setting: We analyzed 14-day mortality in 9,497 participants in the Corticosteroid Randomization After Significant Head Injury (CRASH) RCT of corticosteroid vs. placebo. Adjustment was made using logistic regression for baseline covariates of two validated risk models derived from external data (International Mission on Prognosis and Analysis of Clinical Trials in Traumatic Brain Injury [IMPACT]) and from the CRASH data. The relative sample size (RESS) measure, defined as the ratio of the sample size required by an adjusted analysis to attain the same power as the unadjusted reference analysis, was used to assess the impact of adjustment. Results: Corticosteroid was associated with higher mortality compared with placebo (odds ratio = 1.25, 95% confidence interval = 1.13-1.39). RESS of 0.79 and 0.73 were obtained by adjustment using the IMPACT and CRASH models, respectively, which, for example, implies an increase from 80% to 88% and 91% power, respectively. Conclusion: Moderate gains in power may be obtained using covariate adjustment from logistic regression in heterogeneous conditions such as TBI. Although analyses of RCTs might consider covariate adjustment to improve power, we caution against this approach in the planning of RCTs. http://repub.eur.nl/res/pub/34578/ 2011-08-25T00:00:00Z Background: In Traumatic Brain Injury (TBI), large between-centre differences in outcome exist and many clinicians believe that such differences influence estimation of the treatment effect in randomized controlled trial (RCTs). The aim of this study was to assess the influence of between-centre differences in outcome on the estimated treatment effect in a large RCT in TBI.Methods: We used data from the MRC CRASH trial on the efficacy of corticosteroid infusion in patients with TBI. We analyzed the effect of the treatment on 14 day mortality with fixed effect logistic regression. Next we used random effects logistic regression with a random intercept to estimate the treatment effect taking into account between-centre differences in outcome. Between-centre differences in outcome were expressed with a 95% range of odds ratios (OR) for centres compared to the average, based on the variance of the random effects (tau2). A random effects logistic regression model with random slopes was used to allow the treatment effect to vary by centre. The variation in treatment effect between the centres was expressed in a 95% range of the estimated treatment ORs.Results: In 9978 patients from 237 centres, 14-day mortality was 19.5%. Mortality was higher in the treatment group (OR = 1.22, p = 0.00010). Using a random effects model showed large between-centre differences in outcome (95% range of centre effects: 0.27- 3.71), but did not substantially change the estimated treatment effect (OR = 1.24, p = 0.00003). There was limited, although statistically significant, between-centre variation in the treatment effect (OR = 1.22, 95% treatment OR range: 1.17-1.26).Conclusion: Large between-centre differences in outcome do not necessarily affect the estimated treatment effect in RCTs, in contrast to current beliefs in the clinical area of TBI. andcopy; 2011 Lingsma et al; licensee BioMed Central Ltd. http://repub.eur.nl/res/pub/34307/ 2011-05-17T00:00:00Z Introduction: In clinical trials, ordinal outcome measures are often dichotomized into two categories. In traumatic brain injury (TBI) the 5-point Glasgow outcome scale (GOS) is collapsed into unfavourable versus favourable outcome. Simulation studies have shown that exploiting the ordinal nature of the GOS increases chances of detecting treatment effects. The objective of this study is to quantify the benefits of ordinal analysis in the real-life situation of a large TBI trial.Methods: We used data from the CRASH trial that investigated the efficacy of corticosteroids in TBI patients (n = 9,554). We applied two techniques for ordinal analysis: proportional odds analysis and the sliding dichotomy approach, where the GOS is dichotomized at different cut-offs according to baseline prognostic risk. These approaches were compared to dichotomous analysis. The information density in each analysis was indicated by a Wald statistic. All analyses were adjusted for baseline characteristics.Results: Dichotomous analysis of the six-month GOS showed a non-significant treatment effect (OR = 1.09, 95% CI 0.98 to 1.21, P = 0.096). Ordinal analysis with proportional odds regression or sliding dichotomy showed highly statistically significant treatment effects (OR 1.15, 95% CI 1.06 to 1.25, P = 0.0007 and 1.19, 95% CI 1.08 to 1.30, P = 0.0002), with 2.05-fold and 2.56-fold higher information density compared to the dichotomous approach respectively.Conclusions: Analysis of the CRASH trial data confirmed that ordinal analysis of outcome substantially increases statistical power. We expect these results to hold for other fields of critical care medicine that use ordinal outcome measures and recommend that future trials adopt ordinal analyses. This will permit detection of smaller treatment effects. http://repub.eur.nl/res/pub/12936/ 2008-08-07T00:00:00Z BACKGROUND: Traumatic brain injury (TBI) is a leading cause of death and disability. A reliable prediction of outcome on admission is of great clinical relevance. We aimed to develop prognostic models with readily available traditional and novel predictors. METHODS & FINDINGS: Prospectively collected individual patient data were analyzed from 11 studies. We considered predictors available at admission in logistic regression models to predict mortality and unfavorable outcome according to the Glasgow Outcome Scale at 6 months after injury. Prognostic models were developed in 8509 patients with severe or moderate TBI, with cross-validation by omitting each of the 11 studies in turn. External validation was on 6681 patients from the recent MRC CRASH trial. We found that the strongest predictors were age, motor score, pupillary reactivity and CT characteristics including the presence of traumatic subarachnoid hemorrhage. A prognostic model that combined age, motor score, and pupillary reactivity had an area under the receiver operating characteristic curve (AUC) between 0.66 and 0.84 at cross-validation. This performance could be improved (AUC increase approximately 0.05) by considering CT characteristics, secondary insults (hypotension, hypoxia), and laboratory parameters (glucose and hemoglobin). External validation confirmed the adequate discriminative ability (AUC 0.80). Outcomes were systematically worse than predicted, but less so in 1588 patients from high income countries in the CRASH trial. CONCLUSIONS: Prognostic models using baseline characteristics provide adequate discrimination between patients with good and poor 6 month outcomes after TBI, especially if CT and laboratory findings are considered in addition to traditional predictors. The model predictions may support clinical practice and research, including the design and analysis of randomised controlled trials.