Evidence map›Paper›PMID 27822739›Full record

ArticleNeurocritical care2017

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

Brenno Cabella, Joseph Donnelly, Danilo Cardim, Xiuyun Liu, Manuel Cabeleira, Peter Smielewski, Christina Haubrich, Peter J A Hutchinson, Dong-Joo Kim, Marek Czosnyka

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Neurocritical care, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Observational
  4. Article
  5. Article
  6. Article
  7. Article
  8. Observational
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Brenno CabellaDivision of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK. cabellab@gmail.com.ORCID http://orcid.org/0000-0003-2214-645X
Joseph DonnellyDivision of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
Danilo CardimDivision of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
Xiuyun LiuDivision of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
Manuel CabeleiraDivision of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
Peter SmielewskiDivision of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
Christina HaubrichDivision of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
Peter J A HutchinsonDivision of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
Dong-Joo KimDivision of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
Marek CzosnykaDivision of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.

Funding

Department of Health 12/35/57Department of Health NIHR-RP-R3-12-013Medical Research Council G0600986Medical Research Council G0601025
6 · The paper itself

Abstract

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.

Indexed as

Data Interpretation, StatisticalSample SizeArterial PressureBrain Injuries, TraumaticHemodynamic MonitoringHumansIntracranial PressureOutcome Assessment, Health CareRetrospective StudiesAutoregulationIntracranial pressureOutcome predictionStatistical inferenceTraumatic brain injury

Identifiers

PMID27822739
PMCPMC5524874

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.