title: State-space size considerations for disease-progression models.
abstract: Markov models of disease progression are widely used to model transitions in patients' health state over time. Usually, patients' health status may be classified according to a set of ordered health states. Modelers lump together similar health states into a finite and usually small, number of health states that form the basis of a Markov chain disease-progression model. This increases the number of observations used to estimate each parameter in the transition probability matrix. However, lumping together observably distinct health states also obscures distinctions among them and may reduce the predictive power of the model. Moreover, as we demonstrate, precision in estimating the model parameters does not necessarily improve as the number of states in the model declines. This paper explores the tradeoff between lumping error introduced by grouping distinct health states and sampling error that arises when there are insufficient patient data to precisely estimate the transition probability matrix.
authors.affiliation: Defense Resources Management Institute, Naval Postgraduate School, Monterey, CA, U.S.A.
authors.forename: Eva D
authors.initials: ED
authors.lastname: Regnier
authors.forename: Steven M
authors.initials: SM
authors.lastname: Shechter
mesh_terms: D000328:Adult; D018791:CD4 Lymphocyte Count; D018450:Disease Progression; D006678:HIV / Q000254:growth & development; D015658:HIV Infections / Q000276:immunology; D006801:Humans; D008390:Markov Chains; D008875:Middle Aged; D015233:Models, Statistical; D009716:Numerical Analysis, Computer-Assisted; D011237:Predictive Value of Tests; D015203:Reproducibility of Results
keywords: Markov models; disease progression; state aggregation; transition probability estimation