Modeling Dropout From Adverse Event Data: Impact of Dosing Regimens Across Pregabalin Trials in the Treatment of Generalized Anxiety Disorder

• Published in: Journal of clinical pharmacology 2011-05, Vol.51 (5), p.706-718
• Main Authors: Lalovic, Bojan, Hutmacher, Matt, Frame, Bill, Miller, Raymond
• Format: Article
• Language: English
• Subjects: adverse events models; Anti-Anxiety Agents - administration & dosage; Anti-Anxiety Agents - adverse effects; Anti-Anxiety Agents - pharmacokinetics; Anxiety; Anxiety Disorders - drug therapy; Chi-Square Distribution; Clinical Trials as Topic - statistics & numerical data; Computer Simulation; discrete-time survival models; Dizziness - chemically induced; dose titration; Dose-Response Relationship, Drug; Dropout; Drug Administration Schedule; Female; gamma-Aminobutyric Acid - administration & dosage; gamma-Aminobutyric Acid - adverse effects; gamma-Aminobutyric Acid - analogs & derivatives; gamma-Aminobutyric Acid - pharmacokinetics; generalized anxiety disorder; Humans; Kaplan-Meier Estimate; Likelihood Functions; longitudinal data analysis; Male; Markov Chains; Models, Statistical; Patient Dropouts; Pregabalin; Regression Analysis; Risk Assessment; Risk Factors; Severity of Illness Index; Time Factors; Treatment Outcome; Worry
• ISSN: 0091-2700; 1552-4604
• DOI: 10.1177/0091270010370973

Dizziness represents a major determinant of dropout in the treatment of generalized anxiety disorder with pregabalin. Titration (dose escalation) regimens based on clinical judgment were implemented to mitigate this adverse event and reduce patient dropout across clinical trials. Dropout is an important treatment failure endpoint, which can be analyzed using time‐to‐event models that incorporate daily dosing or other time‐varying information. A parametric discrete‐time dropout model with daily dizziness severity score as a covariate afforded a systematic, model‐based assessment of titration dosing strategies, with model predictions evaluated against corresponding nonparametric estimates. A Gompertz hazard function adequately described the decreasing dropout hazard over time for individuals with severe or moderate dizziness and a lower, constant hazard for individuals reporting no dizziness or mild dizziness. Predictive performance of the model was adequate based on external validation with an independent trial and other goodness‐of‐fit criteria. Prospective simulations highlight the utility of this approach in reducing dropout based on examination of untested titration scenarios for future generalized anxiety disorder or other trials.