Bayesian Calibration Method to Estimate Transition Probabilities for a Markov Model Based on a Continuous Outcome Measure: Application in Parkinson’s Disease
HomePublicationsBayesian Calibration Method to Estimate Transition Probabilities for a Markov Model Based on a Continuous Outcome Measure: Application in Parkinson’s Disease
Bayesian Calibration Method to Estimate Transition Probabilities for a Markov Model Based on a Continuous Outcome Measure: Application in Parkinson’s Disease
2013 Value in health
Neine, M. | Briquet, B. | Mokdad, C.E. | Vataire, A.L. | Aballea, S. |
Volume: 16,
Issue: 7,
Pages: A325-A326,
Disease, model,
Estimating transition probabilities for Markov models is challenging when the effectiveness of the studied intervention is measured using a continuous score, and only aggregate data by treatment are available. We developed a Bayesian calibration method to estimate transition probabilities and applied it in Parkinson’s disease (PD).
