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

Abstract

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.

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“…Compliance was acceptable based on the number of capsules in returned bottles and self reporting [median number of capsules remaining per visit was 0 for both placebo and drug with maximum 1 capsule]. As expected, mild drowsiness was the most common side‐effect reported 39 . Euphoria was reported in one patient.…”

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“…Models generally do not predict boundary values well and removing all zero values is not appropriate because the zero in this example is an important response. Consequently, “standard modeling approaches” often result in models with upwardly biased predictions, and the estimated random‐effects distributions may exhibit severe departures from normality 53 . This situation occurs when response to drug results in a maximum response for protracted periods.…”

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“…In general, models generally do not predict zero values well, and removing all zero values is not appropriate. Consequently, if the “standard modeling” approach is used, the model predictions will be often upwardly biased and the estimated random effects distributions may exhibit severe departures from normality 1 …”

“…Consequently, if the "standard modeling" approach is used, the model predictions will be often upwardly biased and the estimated random effects distributions may exhibit severe departures from normality. 1 There are published reports in which the authors have used a 2-part model for adverse event data that combined logistic regression with ordinal mixed-effects regression. In these examples, the subjects were classified into 2 groups according to whether a nonzero adverse event was recorded or the subject experienced no adverse events.…”