Search PubMed⌕ Search

Biomedical subjects

Isabella Butcher

Publications and source records attributed to Isabella Butcher.

2 recordsLinked to original sources

Adjustment for strong predictors of outcome in traumatic brain injury trials: 25% reduction in sample size requirements in the IMPACT study.

The aim of this study was to quantify the potential reduction in sample size that can be achieved by adjustment for predictors of outcome in traumatic brain injury (TBI) trials. We used individual patient data from seven therapeutic phase III randomized clinical trials (RCTs; n = 6166) in moderate or severe TBI, and three TBI surveys (n = 2238). The primary outcome was the dichotomized Glasgow Outcome Scale at 6 months (favorable/unfavorable). Baseline predictors of outcome considered were age, motor score, pupillary reactivity, computed tomography (CT) classification, traumatic subarachnoid hemorrhage, hypoxia, hypotension, glycemia, and hemoglobin. We calculated the potential sample size reduction obtained by adjustment of a hypothetical treatment effect for one to seven predictors with logistic regression models. The distribution of predictors was more heterogeneous in surveys than in trials. Adjustment of the treatment effect for the strongest predictors (age, motor score, and pupillary reactivity) yielded a reduction in sample size of 16-23% in RCTs and 28-35% in surveys. Adjustment for seven predictors yielded a reduction of about 25% in most studies: 20-28% in RCTs and 32-39% in surveys. A major reduction in sample size can be obtained with covariate adjustment in TBI trials. Covariate adjustment for strong predictors should be incorporated in the analysis of future TBI trials.

Age Factors↗

Randomization inference for balanced cluster-randomized trials.

This paper discusses the choice of randomization tests for inferences from cluster-randomized trials that have been designed to ensure a balanced allocation of clusters to treatments. Methods for covariate-adjusted randomization tests are reviewed and their application to balanced cluster-randomized trials discussed. Two cluster-randomized trials with balanced designs are used to illustrate the choices that can be made in selecting a randomization test, and methods for obtaining confidence intervals for treatment effects are illustrated. The balance imposed by the randomization in these trials makes adjustment for covariates less beneficial than for an unbalanced design. However, the adjusted analyses do not appear generally to have worse properties than the unadjusted ones, and may provide protection against any imbalance that has not been controlled for in the design. The only case when adjustment for covariates may result in worse precision is when a large number of cluster-level covariates are included in the analysis. An expression is provided that allows the size of this effect to be calculated for any given set of cluster-level covariates.

Cluster Analysis↗