An Association Between ICP-Derived Data and Outcome in TBI Patients: The Role of Sample Size.
BACKGROUND
Many 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.


METHODS
Data 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.


RESULTS
In 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.


CONCLUSIONS
Low 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.
Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK. cabellab@gmail.com.
Brenno
http://orcid.org/0000-0003-2214-645X
B
Cabella
Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
Joseph
J
Donnelly
Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
Danilo
D
Cardim
Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
Xiuyun
X
Liu
Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
Manuel
M
Cabeleira
Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
Peter
P
Smielewski
Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
Christina
C
Haubrich
Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
Peter J A
PJA
Hutchinson
Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.|Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea.
Dong-Joo
DJ
Kim
Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
Marek
M
Czosnyka
D062186:Arterial Pressure / Q000502:physiology; D000070642:Brain Injuries, Traumatic / Q000503:physiopathology* / Q000628:therapy*; D003627:Data Interpretation, Statistical; D000074743:Hemodynamic Monitoring / Q000706:statistics & numerical data*; D006801:Humans; D007427:Intracranial Pressure / Q000502:physiology*; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data*; D012189:Retrospective Studies; D018401:Sample Size
Autoregulation; Intracranial pressure; Outcome prediction; Statistical inference; Traumatic brain injury