title: Two-process models for discrete-time serial categorical response.
abstract: This paper proposes a two-process log-linear model for analysis of polychotomous response data generated on study subjects assessed at successive discrete intervals. Response type at each discrete time may be either a transient response or a cause-specific failure. We view outcome of transient response as fundamentally different from outcome of failure, and, in a competing risk framework, we motivate a separate model for each: one to describe the process for transitions to transient response states and the other to describe the process for transitions to absorbing failure states. We maximize the likelihood for each model separately with use of existing software for iterative proportional fitting.
authors.affiliation: Yale University, New Haven, CT 06510.
authors.forename: R
authors.initials: R
authors.lastname: Dersimonian
authors.forename: S G
authors.initials: SG
authors.lastname: Baker
mesh_terms: D000368:Aged; D003237:Connecticut; D003627:Data Interpretation, Statistical; D005260:Female; D006707:Homes for the Aged / Q000706:statistics & numerical data; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D015233:Models, Statistical; D009735:Nursing Homes / Q000706:statistics & numerical data; D011336:Probability; D011638:Public Housing; D013997:Time Factors