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237 Studies of epilepsy surgery outcomes are statistically underpowered.

Published online by Cambridge University Press:  19 April 2022

Adam Dickey
Affiliation:
Emory University
Robert T. Krafty
Affiliation:
Department of Biostatistics and Bioinformatics, Emory University
Nigel P. Pedersen
Affiliation:
Division of Epilepsy, Emory Brain Health Center
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Abstract

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OBJECTIVES/GOALS: Low statistical power is a problem is many fields. We performed a systematic review to determine the median statistical power of studies of epilepsy surgery outcomes. METHODS/STUDY POPULATION: We performed a PubMed search for studies reporting epilepsy surgery outcomes for the years 1980-2000, focusing on studies using stereo-electroencephalography (SEEG). We extracted patient count data for comparisons of surgical outcome between groups, based on a prognostic factor. We defined a clinically meaningful difference the surgical outcome for MRI positive (66.9%) compared to MRI negative (45.5%) in the largest study in the series. The statistical power of a Chi-square test was computed as the percentage of simulated runs (10,000 repetitions) assuming this difference with a p-value less than 0.05. RESULTS/ANTICIPATED RESULTS: Based on 69 studies, the median sample size was 38 patients, and the median statistical power was 24%. This implies at least a 17% (0.5/[0.24+0.05)) chance a study with a significant result in false, assuming 1:1 pre-test odds. A 'typical’ SEEG study with 33 patients and 2:1 allocation had a median significant odds ratio of 6.5, which over-estimates the true odds ratio of 2.4. DISCUSSION/SIGNIFICANCE: Studies of epilepsy surgery outcomes using SEEG are statistically underpowered. This means true effects will be missed, the chance a study with a significant result is false will be inflated, and significant effects found will be over-estimated. Studies of surgery outcome need better statistical rigor if they are to reliably guide treatment.

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This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
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© The Author(s), 2022. The Association for Clinical and Translational Science