title: Analysis of counts with two latent classes, with application to risk assessment based on physician-visit records of cancer survivors.
abstract: Motivated by a cancer survivorship program, this paper explores event counts from two categories of individuals with unobservable membership. We formulate the counts using a latent class model and consider two likelihood-based inference procedures, the maximum likelihood estimation (MLE) and a pseudo-MLE procedure. The pseudo-MLE utilizes additional information on one of the latent classes. It yields reduced computational intensity and potentially increased estimation efficiency. We establish the consistency and asymptotic normality of the proposed pseudo-MLE, and we present an extended Huber sandwich estimator as a robust variance estimator for the pseudo-MLE. The finite-sample properties of the two-parameter estimators along with their variance estimators are examined by simulation. The proposed methodology is illustrated by physician-claim data from the cancer program.
authors.affiliation: Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, BC, Canada V5A 1S6.
authors.forename: Huijing
authors.initials: H
authors.lastname: Wang
authors.forename: X Joan
authors.initials: XJ
authors.lastname: Hu
authors.forename: Mary L
authors.initials: ML
authors.lastname: McBride
authors.forename: John J
authors.initials: JJ
authors.lastname: Spinelli
mesh_terms: D003627:Data Interpretation, Statistical; D006801:Humans; D007345:Insurance Claim Review / Q000706:statistics & numerical data; D016013:Likelihood Functions; D015233:Models, Statistical; D009369:Neoplasms / Q000453:epidemiology; D009819:Office Visits / Q000706:statistics & numerical data; D016012:Poisson Distribution; D018570:Risk Assessment; D017741:Survivors / Q000706:statistics & numerical data
keywords: Efficiency vs. robustness; Mixture Poisson model; Pseudo-maximum likelihood estimation; Robust variance estimation; Supplementary information