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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.

Pharmacoepidemiology and Drug Safety
|April 15, 2022
PubMed
Summary

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.

Keywords:
long-term persistent fractionmedication persistencemixture cure modelpharmacoepidemiologysurvival analysis

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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.