Joint modeling of dizziness, drowsiness, and dropout associated with pregabalin and placebo treatment of generalized anxiety disorder

• Published in: Journal of pharmacokinetics and pharmacodynamics 2009-12, Vol.36 (6), p.565-584
• Main Authors: Frame, Bill, Miller, Raymond, Hutmacher, Matthew M.
• Format: Article
• Language: English
• Subjects: Anti-Anxiety Agents - administration & dosage; Anti-Anxiety Agents - adverse effects; Anxiety; Anxiety Disorders - drug therapy; Biochemistry; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Computer Simulation; Creatinine - blood; Dizziness - chemically induced; Dose-Response Relationship, Drug; Double-Blind Method; gamma-Aminobutyric Acid - administration & dosage; gamma-Aminobutyric Acid - adverse effects; gamma-Aminobutyric Acid - analogs & derivatives; Humans; Kaplan-Meier Estimate; Markov Chains; Models, Biological; Models, Statistical; Multicenter Studies as Topic; Patient Dropouts; Pharmacology/Toxicology; Pharmacy; Placebo Effect; Pregabalin; Randomized Controlled Trials as Topic; Severity of Illness Index; Sleep Stages - drug effects; Time Factors; Treatment Outcome; Veterinary Medicine/Veterinary Science
• ISSN: 1567-567X; 1573-8744
• DOI: 10.1007/s10928-009-9137-5

Dizziness and drowsiness are cited as being predictors of dropout from clinical trials for the medicine pregabalin. These adverse events are typically recorded daily on a four point ordinal scale (0 = none, 1 = mild, 2 = moderate, 3 = severe), with most subjects never reporting either adverse event. We modeled the dizziness, drowsiness, and dropout associated with pregabalin use in generalized anxiety disorder using piecewise Weibull distributions for the time to first non-zero dizziness or drowsiness score, after which the dizziness or drowsiness was modeled with ordinal regression with a Markovian element. Dropout was modeled with a Weibull distribution. Platykurtosis was encountered in the estimated random effects distributions for the ordinal regression models and was addressed with dynamic John–Draper transformations. The only identified predictor for the time to first non-zero dizziness or drowsiness score was daily titrated dose. Predictors for dropout included creatinine clearance and maximum daily adverse event score. Tolerance to adverse events over time was modeled by including a non-stationary component for the dizziness ordinal Markov regression while the piecewise Weibull distributions allowed a change point in the median time to first non-zero dizziness or drowsiness score.