Personalizing Medical Treatment Decisions: Integrating Meta-analytic Treatment Comparisons with Patient-Specific Risks and Preferences

• Published in: Medical decision making Vol. 39; no. 8; pp. 998 - 1009
• Main Authors: Weyant, Christopher, Brandeau, Margaret L., Basu, Sanjay
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01-11-2019
• Subjects: medical decision making; schizophrenia; personalized medicine
• ISSN: 0272-989X; 1552-681X
• DOI: 10.1177/0272989X19884927

Background. Network meta-analyses (NMAs) that compare treatments for a given condition allow physicians to identify which treatments have higher or lower probabilities of reducing the risks of disease complications or increasing the risks of treatment side effects. Translating these data into personalized treatment plans requires integration of NMA data with patient-specific pretreatment risk estimates and preferences regarding treatment objectives and acceptable risks. Methods. We introduce a modeling framework to integrate data probabilistically from NMAs with data on individualized patient risk estimates for disease outcomes, treatment preferences (such as willingness to incur greater side effects for increased life expectancy), and risk preferences. We illustrate the modeling framework by creating personalized plans for antipsychotic drug treatment and evaluating their effectiveness and cost-effectiveness. Results. Compared with treating all patients with the drug that yields the greatest quality-adjusted life-years (QALYs) on average (amisulpride), personalizing the selection of antipsychotic drugs for schizophrenia patients over the next 5 years would be expected to yield 0.33 QALYs (95% credible interval [crI]: 0.30–0.37) per patient at an incremental cost of $4849/QALY gained (95% crI: dominant–$12,357), versus 0.29 and 0.04 QALYs per patient when accounting for only risks or preferences, respectively, but not both. Limitations. The analysis uses a linear, additive utility function to reflect patient treatment preferences and does not consider potential variations in patient time discounting. Conclusions. Our modeling framework rigorously computes what physicians normally have to do mentally. By integrating 3 key components of personalized medicine—evidence on efficacy, patient risks, and patient preferences—the modeling framework can provide personalized treatment decisions to improve patient health outcomes.