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Applying mixture cure survival modeling to medication persistence analysis
Chao Cai1, Bryan L Love1, Ismaeel Yunusa1
1College of Pharmacy, University of South Carolina, Department of Clinical Pharmacy and Outcomes Sciences, Columbia, South Carolina, USA.
Purpose:
Standard survival models are often used in a 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 a mixture cure model in the 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 the 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 medication discontinuation among short-term persistent patients.
Results:
Long-term persistent rate was estimated as 17% for statin users aged between 45 and 55 versus 10% for age less than 45 versus 4% for age greater than 55 via the mixture cure model. The HR 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.
Insights
A mixture cure model improves medication persistence analysis by accounting for long-term persistent patients, offering more accurate estimations than standard survival models. This approach better describes patient characteristics for both short-term and long-term medication adherence.
Area of Science:
- Pharmacoeconomics and Health Outcomes Research
- Biostatistics and Survival Analysis
- Real-World Evidence in Pharmaceutical Research
Background:
- Standard survival models in medication persistence analysis may introduce bias by assuming all patients discontinue medication.
- This assumption is problematic when a segment of the patient population is expected to remain persistent long-term.
- Accurate estimation of medication persistence is crucial for understanding treatment effectiveness and healthcare resource utilization.
Purpose of the Study:
- To introduce and demonstrate the application of a mixture cure model for medication persistence analysis.
- To differentiate between long-term and short-term medication persistent patient characteristics.
- To compare the performance of the mixture cure model against standard survival models using real-world data.
Main Methods:
- Utilized a cohort of new statin users for comparative analysis.
- Applied a mixture cure model to estimate long-term persistent rates and identify factors influencing persistence.
- Employed standard survival models for benchmarking and comparison.
Main Results:
- The mixture cure model estimated a long-term persistent rate of 17% for statin users aged 45-55.
- Hazard ratios (HR) for covariates were lower with the mixture cure model (HR=1.32) compared to standard survival models (HR=1.41) when comparing older age groups.
- The model provided distinct estimates for factors associated with long-term persistence (Odds Ratios) and time to discontinuation (Hazard Ratios).
Conclusions:
- Mixture cure models offer improved accuracy in medication persistence estimation, particularly when long-term persistent patients are present.
- This advanced statistical approach provides a more nuanced understanding of patient adherence patterns.
- The findings support the use of mixture cure models for more reliable medication persistence analyses in real-world settings.
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