• Brenno Cabella, Joseph Donnelly, Danilo Cardim, Xiuyun Liu, Manuel Cabeleira, Peter Smielewski, Christina Haubrich, HutchinsonPeter J APJADivision of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK., Dong-Joo Kim, and Marek Czosnyka.
• Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK. cabellab@gmail.com.
• Neurocrit Care. 2017 Aug 1; 27 (1): 103107103-107.

BackgroundMany demographic and physiological variables have been associated with TBI outcomes. However, with small sample sizes, making spurious inferences is possible. This paper explores the effect of sample sizes on statistical relationships between patient variables (both physiological and demographic) and outcome.MethodsData from head-injured patients with monitored arterial blood pressure, intracranial pressure (ICP) and outcome assessed at 6 months were included in this retrospective analysis. A univariate logistic regression analysis was performed to obtain the odds ratio for unfavorable outcome. Three different dichotomizations between favorable and unfavorable outcomes were considered. A bootstrap method was implemented to estimate the minimum sample sizes needed to obtain reliable association between physiological and demographic variables with outcome.ResultsIn a univariate analysis with dichotomized outcome, samples sizes should be generally larger than 100 for reproducible results. Pressure reactivity index, ICP, and ICP slow waves offered the strongest relationship with outcome. Relatively small sample sizes may overestimate effect sizes or even produce conflicting results.ConclusionLow power tests, generally achieved with small sample sizes, may produce misleading conclusions, especially when they are based only on p values and the dichotomized criteria of rejecting/not-rejecting the null hypothesis. We recommend reporting confidence intervals and effect sizes in a more complete and contextualized data analysis.

21 keywords...

hide…