Hidden Markov models and maximum likelihood methods can be used to simultaneously estimate disease progression rates, mismeasurement, and covariate effects in lung transplant recipients.
Hidden Markov models can be used to estimate disease progression rates and covariate effects in lung transplant recipients monitored with irregularly measured FEV1.
Chronic rejection in lung transplant recipients is monitored by repeated measurement of forced expiratory volume in one second (FEV1). This marker is measured at irregular intervals and is also affected by covariates and short-term fluctuation. This paper describes the use of hidden Markov models for the underlying staged functional decline. Maximum likelihood methods are used to simultaneously estimate disease progression rates and the effects of mismeasurement and covariates.
Jackson et al. (Fri,) conducted a other in Bronchiolitis obliterans syndrome in lung transplant recipients. Hidden Markov models was evaluated on Disease progression rates and effects of mismeasurement and covariates. Hidden Markov models and maximum likelihood methods can be used to simultaneously estimate disease progression rates, mismeasurement, and covariate effects in lung transplant recipients.