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

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 neg

SUBMITTER: Tharayil JJ 

PROVIDER: S-EPMC5429882 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature