Modeling longitudinal daily seizure frequency data from pregabalin add-on treatment.

The purpose of this study was to describe longitudinal daily seizure count data with respect to the effects of time and pregabalin add-on therapy. Models were developed in a stepwise manner: base model, time effect model, and time and drug effect (final) model, using a negative binomial distribution with Markovian features. Mean daily seizure count (lambda) was estimated to be 0.385 (relative standard error [RSE] 3.09%) and was further increased depending on the seizure count on the previous day. An overdispersion parameter (OVDP), representing extra-Poisson variation, was estimated to be 0.330 (RSE 11.7%). Interindividual variances on lambda, and OVDP were 84.7% and 210%, respectively. Over time, lambda tended to increase exponentially with a rate constant of 0.272 year(-1) (RSE 26.8%). A mixture model was applied to classify responders/nonresponders to pregabalin treatment. Within the responders, lambda decreased exponentially with respect to dose with a constant of 0.00108 mg(-1) (RSE 11.9%). The estimated responder rate was 66% (RSE 27.6%). Simulation-based diagnostics showed the model reasonably reproduced the characteristics of observed data. Highly variable daily seizure frequency was successfully characterized incorporating baseline characteristics, time effect, and the effect of pregabalin with classification of responders/nonresponders, all of which are necessary to adequately assess the efficacy of antiepileptic drugs.