Optimizing Statin Treatment Decisions for Diabetes Patients in the Presence of Uncertain Future Adherence
ABSTRACT Statins are an important part of the treatment plan for patients with type 2 diabetes. However, patients who are prescribed statins often take less than the prescribed amount or stop taking the drug altogether. This suboptimal adherence may decrease the benefit of statin initiation.
To estimate the influence of adherence on the optimal timing of statin initiation for patients with type 2 diabetes.
The authors use a Markov decision process (MDP) model to optimize the treatment decision for patients with type 2 diabetes. Their model incorporates a Markov model linking adherence to treatment effectiveness and long-term health outcomes. They determine the optimal time of statin initiation that minimizes expected costs and maximizes expected quality-adjusted life years (QALYs).
In the long run, approximately 25% of patients remain highly adherent to statins. Based on the MDP model, generic statins lower costs in men and result in a small increase in costs in women relative to no treatment. Patients are able to noticeably increase their expected QALYs by 0.5 to 2 years depending on the level of adherence.
Adherence-improving interventions can increase expected QALYs by as much as 1.5 years. Given suboptimal adherence to statins, it is optimal to delay the start time for statins; however, changing the start time alone does not lead to significant changes in costs or QALYs.
Full-textDOI: · Available from: Jennifer Mason Lobo, Jun 09, 2015
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ABSTRACT: Background Real-world patients’ medication adherence is lower than that of clinical trial patients. Hence, the effectiveness of medications in routine practice may differ. Objectives The study objective was to compare the outcomes of an adherence-naive versus a dynamic adherence modeling framework using the case of statins for the primary prevention of cardiovascular (CV) disease. Methods Statin adherence was categorized into three state-transition groups on the basis of an epidemiological cohort study. Yearly adherence transitions were incorporated into a Markov microsimulation using TreeAge software. Tracker variables were used to store adherence transitions, which were used to adjust probabilities of CV events over the patient’s lifetime. Microsimulation loops “random walks” estimated the average accrued quality-adjusted life-years (QALYs) and CV events. For each 1,000-patient microsimulations, 10,000 outer loops were performed to reflect second-order uncertainty. Results The adherence-naive model estimated 0.14 CV events avoided per person, whereas the dynamic adherence model estimated 0.08 CV events avoided per person. Using the adherence-naive model, we found that statin therapy resulted in 0.40 QALYs gained over the lifetime horizon on average per person while the dynamic adherence model estimated 0.22 incremental QALYs gained. Subgroup analysis revealed that maintaining high adherence in year 2 resulted in 0.23 incremental QALYs gained as compared with 0.16 incremental QALYs gained when adherence dropped to the lowest level. Conclusions A dynamic adherence Markov microsimulation model reveals risk reduction and effectiveness that are lower than with an adherence-naive model, and reflective of real-world practice. Such a model may highlight the value of improving or maintaining good adherence.Value in Health 09/2014; 17(6). DOI:10.1016/j.jval.2014.06.010 · 2.89 Impact Factor
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ABSTRACT: Medical decisions often involve tradeoff among competing criteria. For example, patients with third-party health insurance are primarily concerned about maximizing their quality-adjusted lifespan, since the majority of the cost burden typically falls on the third-party payer. On the other hand, third-party payers are incented to minimize total healthcare-related costs. Therefore, third-party payers must weigh the short-term cost of treatment against the long-term benefits of avoiding more costly health outcomes associated with disease progression and adverse events. The goal of the societal perspective is to achieve a reasonable balance among these competing criteria of quality-adjusted lifespan and costs. Treatment of diabetes provides a good example of the need to apply multicriteria decision-making models to treatment decisions. Chronic diseases such as diabetes are associated with high medical costs and a large number of available treatment options. In this paper, we use a Markov decision process (MDP) to show how decision-maker perspectives can influence medical treatment decisions related to cardiovascular risk management in patients with type 2 diabetes. We compare optimal treatment decisions from three different perspectives: societal, patient, and third-party payer. We further formulate an inverse MDP model to estimate the implied monetary value of a year of life, from the societal perspective, according to current U.S. treatment guidelines.Ibm Journal of Research and Development 09/2012; 56(5):8:1-8:12. DOI:10.1147/JRD.2012.2201849 · 0.50 Impact Factor