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Bayesian Estimation Improves Prediction of Outcomes after Epilepsy Surgery.

Bayesian Estimation Improves Prediction of Outcomes after Epilepsy Surgery.

Authors :
Dickey AS
Reddy V
Pedersen NP
Krafty RT
Source :
MedRxiv : the preprint server for health sciences [medRxiv] 2024 Jun 22. Date of Electronic Publication: 2024 Jun 22.
Publication Year :
2024

Abstract

Low power is a problem in many fields, as underpowered studies that find a statistically significant result will exaggerate the magnitude of the observed effect size. We quantified the statistical power and magnitude error of studies of epilepsy surgery outcomes. The median power across all studies was 14%. Studies with a median sample size or less (n<=56) and a statistically significant result exaggerated the true effect size by a factor of 5.4 (median odds ratio 9.3 vs. median true odds ratio 1.7), while the Bayesian estimate of the odds ratio only exaggerated the true effect size by a factor of 1.6 (2.7 vs. 1.7). We conclude that Bayesian estimation of odds ratio attenuates the exaggeration of significant effect sizes in underpowered studies. This approach could help improve patient counseling about the chance of seizure freedom after epilepsy surgery.<br />Competing Interests: DISCLOSURE OF CONFLICTS OF INTEREST N.P.P. has served as a paid consultant for DIXI Medical USA, who manufactures products used in the workup for epilepsy surgery. The terms of this arrangement have been reviewed and approved by Emory University in accordance with its conflict-of-interest policies. A.S.D, V.R. and R.T.K. have no conflicts of interest to disclose.

Details

Language :
English
Database :
MEDLINE
Journal :
MedRxiv : the preprint server for health sciences
Publication Type :
Academic Journal
Accession number :
38947027
Full Text :
https://doi.org/10.1101/2024.06.21.24309313