OBJECTIVES: Standard survival models are often used in medication persistence analysis. These methods implicitly assume that all patients will experience the event (medication discontinuation), which may bias the estimation of persistence if long-term medication persistent patients’ rate is expected in the population. We aimed to introduce mixture cure model framework in medication persistence analysis to describe the characteristics of long-term and short-term persistent patients and demonstrate its application using a real-world data analysis.

METHODS: A cohort of new users of statins was used to demonstrate the differences between the standard survival model and the mixture cure model in medication persistence analysis. The mixture cure model estimated effects of variables, reported as odds ratios (OR) associated with likelihood of being long-term persistent and effects of variables, reported as hazard ratios (HR) associated with time to discontinue medication among short-term persistent patients.

RESULTS: Long-term persistent rate was estimated as 17% for statin users aged between 45 and 55 vs. 10% for age less than 45 vs. 4% for age greater than 55 via the mixture cure model. The hazard ratios of covariates estimated by the standard survival model (HR = 1.41, 95% CI = [1.35, 1.48]) were higher than those estimated by the mixture cure model (HR = 1.32, 95% CI = [1.25, 1.39]) when comparing patients with age greater than 55 to those between 45 and 55.

CONCLUSIONS: Compared with standard survival modeling, a mixture cure model can improve the estimation of medication persistence when long-term persistent patients are expected in the population.