Background: Despite growing controversies around Hydroxychloroquine's effectiveness, the drug is still widely prescribed by clinicians to treat COVID19 patients. Therapeutic judgment under uncertainty and imperfect information may be influenced by personal preference, whereby individuals, to confirm a-priori beliefs, may propose drugs without knowing the clinical benefit. To estimate this disconnect between available evidence and prescribing behavior, we created a Bayesian model analyzing a-priori optimistic belief of physicians in Hydroxychloroquine's effectiveness. Methodology: We created a Bayesian model to simulate the impact of different a-priori beliefs related to Hydroxychloroquine's effectiveness on clinical and economic outcome. Results: Our hypothetical results indicate no significant difference in treatment effect (combined survival benefit and harm) up to a presumed drug's effectiveness level of 20%, with younger individuals being negatively affected by the treatment (RR 0.82, 0.55–1.2; (0.95 (1.1) % expected adverse events versus 0.05 (0.98) % expected death prevented). Simulated cost data indicate overall hospital cost (medicine, hospital stay, complication) of 18.361,41€ per hospitalized patient receiving Hydroxychloroquine treatment. Conclusion: Off-label use of Hydroxychloroquine needs a rational, objective and datadriven evaluation, as personal preferences may be flawed and cause harm to patients and to society.
- Bayesian modeling, health economics
- Cognitive bias
- Off-label drug use
- Simulation model
ASJC Scopus subject areas
- Critical Care and Intensive Care Medicine