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Titolo: ORDMKV  A COMPUTERPROGRAM FITTING PROPORTIONAL ODDS MODEL FOR MULTISTATE MARKOV PROCESS
Autore: GUO WS; MARSHALL G;
 Indirizzi:
 PONTIFICIA UNIV CATOLICA CHILE,FAC MATEMAT,DEPT ESTADIST,CASILLA 306,CORREO 22 SANTIAGO CHILE UNIV COLORADO,HLTH SCI CTR,SCH MED,DEPT PREVENT MED & BIOMETR DENVER CO 80262
 Titolo Testata:
 Computer methods and programs in biomedicine
fascicolo: 3,
volume: 46,
anno: 1995,
pagine: 257  263
 SICI:
 01692607(1995)46:3<257:OACFP>2.0.ZU;2F
 Fonte:
 ISI
 Lingua:
 ENG
 Keywords:
 MARKOV PROCESS; SURVIVAL ANALYSIS; PROPORTIONAL ODDS MODEL; TIME DEPENDENT COVARIATE;
 Tipo documento:
 Article
 Natura:
 Periodico
 Settore Disciplinare:
 CompuMath Citation Index
 Science Citation Index Expanded
 Science Citation Index Expanded
 Science Citation Index Expanded
 Citazioni:
 7
 Recensione:
 Indirizzi per estratti:



 Citazione:
 W.S. Guo e G. Marshall, "ORDMKV  A COMPUTERPROGRAM FITTING PROPORTIONAL ODDS MODEL FOR MULTISTATE MARKOV PROCESS", Computer methods and programs in biomedicine, 46(3), 1995, pp. 257263
Abstract
ORDMKV is a computer program designed to fit a multistate discretetime Markov model for kstages disease processes having an ordinal structure. The model consists of k transient states representing the increasing severity of the disease process, and the final state can be optionally chosen to be an absorbing state in cases such as death. The ordinal structure of the stages of the disease is modelled by using ordinal response models. Each row of the onestep transition probability matrix is modelled using a proportional odds model based on the cumulative transition probabilities. By using these ordinal response models, the number of parameters used to model the disease process can be reduced significantly not only with respect to a general discretetime model, but also compared with a parsimonuos continuoustime model. A restricted model can be fitted by assuming that the effect of the covariables in the cumulative probability has common regression coefficients inall stages of the disease process. This assumption, if it holds, reduces the number of regression coefficients associated with each covariate to only one. The regression coefficients of this model are estimated via the method of maximum likelihood, using a quasiNewton optimization algorithm. When the last state is considered as an absorbing state, it is possible to compute survival curves from the transient states of the process. The program was written in standard FORTRAN 77 and is illustrated using a fourstate model to determine factors influencing diabetic retinopathy in young subjects with insulindependent diabetesmellitus.
ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 24/09/20 alle ore 23:12:06