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Author Notes:

Adam Dickey, Woodruff Memorial Research Building, Room 6209, 101 Woodruff Circle, 30322 Atlanta, GA, USA. Email: adam.s.dickey@emory.edu

ASD and NPP contributed to the conception and design of the study. ASD, RTK, and NPP contributed to the drafting of the text. ASD performed the statistical analysis and prepared the figures and tables.

The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. We also thank Scott Millis of Wayne State University for help finding relevant references. A version of this manuscript was posted as a preprint to https://www.medrxiv.org/content/10.1101/2021.10.01.21264435. MATLAB code which can be used to reproduce the analyses and figures described here is posted at https://github.com/AdamSDickey/Ordinal_Regression.

NPP 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. ASD and RTK have no conflicts of interest to disclose. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.


Research Funding:

NPP is supported by the Woodruff Foundation, CURE Epilepsy, and NIH grants K08 NS105929, R01 NS088748, and R21 NS122011.

ASD is supported by the National Center for Advancing Translational Sciences of the NIH under award numbers UL1 TR002378 and KL2 TR002381.

RTK is supported by R01 GM113243.


  • Science & Technology
  • Life Sciences & Biomedicine
  • Clinical Neurology
  • Neurosciences
  • Neurosciences & Neurology
  • Engel outcome
  • epilepsy surgery
  • logistic regression
  • seizure freedom
  • statistical power

Ordinal regression increases statistical power to predict epilepsy surgical outcomes


Journal Title:



Volume 7, Number 2


, Pages 344-349

Type of Work:

Article | Final Publisher PDF


Studies of epilepsy surgery outcomes are often small and thus underpowered to reach statistically valid conclusions. We hypothesized that ordinal logistic regression would have greater statistical power than binary logistic regression when analyzing epilepsy surgery outcomes. We reviewed 10 manuscripts included in a recent meta-analysis which found that mesial temporal sclerosis (MTS) predicted better surgical outcomes after a stereotactic laser amygdalohippocampectomy (SLAH). We extracted data from 239 patients from eight studies that reported four discrete Engel surgical outcomes after SLAH, stratified by the presence or absence of MTS. The rate of freedom from disabling seizures (Engel I) was 64.3% (110/171) for patients with MTS compared to 44.1% (30/68) without MTS. The statistical power to detect MTS as a predictor for better surgical outcome after a SLAH was 29% using ordinal regression, which was significantly more than the 13% power using binary logistic regression (paired t-test, P <.001). Only 120 patients are needed for this example to achieve 80% power to detect MTS as a predictor using ordinal regression, compared to 210 patients that are needed to achieve 80% power using binary logistic regression. Ordinal regression should be considered when analyzing ordinal outcomes (such as Engel surgical outcomes), especially for datasets with small sample sizes.

Copyright information:

© 2022 The Authors. Epilepsia Open published by Wiley Periodicals LLC on behalf of International League Against Epilepsy

This is an Open Access work distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/rdf).
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