Format

Send to

Choose Destination
Epilepsia. 2017 May;58(5):835-844. doi: 10.1111/epi.13727. Epub 2017 Mar 30.

A big data approach to the development of mixed-effects models for seizure count data.

Author information

1
Clinical Epilepsy Section, NINDS, NIH, Bethesda, Maryland, U.S.A.
2
Department of Biomedical Engineering, Duke University, Durham, North Carolina, U.S.A.
3
Department of Statistics, Rice University, Houston, Texas, U.S.A.
4
Baylor College of Medicine, Houston, Texas, U.S.A.
5
Alexandria, Virginia, U.S.A.
6
University of California Los Angeles Medical Center, Los Angeles, California, U.S.A.

Abstract

OBJECTIVE:

Our objective was to develop a generalized linear mixed model for predicting seizure count that is useful in the design and analysis of clinical trials. This model also may benefit the design and interpretation of seizure-recording paradigms. Most existing seizure count models do not include children, and there is currently no consensus regarding the most suitable model that can be applied to children and adults. Therefore, an additional objective was to develop a model that accounts for both adult and pediatric epilepsy.

METHODS:

Using data from SeizureTracker.com, a patient-reported seizure diary tool with >1.2 million recorded seizures across 8 years, we evaluated the appropriateness of Poisson, negative binomial, zero-inflated negative binomial, and modified negative binomial models for seizure count data based on minimization of the Bayesian information criterion. Generalized linear mixed-effects models were used to account for demographic and etiologic covariates and for autocorrelation structure. Holdout cross-validation was used to evaluate predictive accuracy in simulating seizure frequencies.

RESULTS:

For both adults and children, we found that a negative binomial model with autocorrelation over 1 day was optimal. Using holdout cross-validation, the proposed model was found to provide accurate simulation of seizure counts for patients with up to four seizures per day.

SIGNIFICANCE:

The optimal model can be used to generate more realistic simulated patient data with very few input parameters. The availability of a parsimonious, realistic virtual patient model can be of great utility in simulations of phase II/III clinical trials, epilepsy monitoring units, outpatient biosensors, and mobile Health (mHealth) applications.

KEYWORDS:

Clinical trial simulation; Epilepsy; Generalized linear mixed-effects modeling

PMID:
28369781
PMCID:
PMC5429882
DOI:
10.1111/epi.13727
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Wiley Icon for PubMed Central
Loading ...
Support Center