View Item 

Show simple item record

Two-Level Proportional Hazards Models
dc.contributor.authorMaples, Jerry J.en_US
dc.contributor.authorMurphy, Susan A.en_US
dc.contributor.authorAxinn, William G.en_US
dc.date.accessioned2010-04-01T15:05:15Z
dc.date.available2010-04-01T15:05:15Z
dc.date.issued2002-12en_US
dc.identifier.citationMaples, Jerry J.; Murphy, Susan A.; Axinn, William G. (2002). "Two-Level Proportional Hazards Models." Biometrics 58(4): 754-763. <http://hdl.handle.net/2027.42/65551>en_US
dc.identifier.issn0006-341Xen_US
dc.identifier.issn1541-0420en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/65551
dc.identifier.urihttp://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=12495129&dopt=citationen_US
dc.description.abstractWe extend the proportional hazards model to a two-level model with a random intercept term and random coefficients. The parameters in the multilevel model are estimated by a combination of EM and Newton-Raphson algorithms. Even for samples of 50 groups, this method produces estimators of the fixed effects coefficients that are approximately unbiased and normally distributed. Two different methods, observed information and profile likelihood information, will be used to estimate the standard errors. This work is motivated by the goal of understanding the determinants of contraceptive use among Nepalese women in the Chitwan Valley Family Study (Axinn, Barber, and Ghimire, 1997). We utilize a two-level hazard model to examine how education and access to education for children covary with the initiation of permanent contraceptive use.en_US
dc.format.extent1054873 bytes
dc.format.extent3110 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.publisherBlackwell Publishing Ltden_US
dc.rightsThe International Biometric Society, 2002en_US
dc.subject.otherEM Algorithmen_US
dc.subject.otherFrailty Modelen_US
dc.subject.otherHazard Modelen_US
dc.subject.otherMultilevelen_US
dc.subject.otherProfile Likelihooden_US
dc.subject.otherRandom Coefficienten_US
dc.subject.otherSemiparametric Likelihooden_US
dc.subject.otherSurvival Analysisen_US
dc.titleTwo-Level Proportional Hazards Modelsen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelMathematicsen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDepartment of Statistics and Institute for Social Research, University of Michigan, 4092 Frieze Building, Ann Arbor, Michigan 48109–1285, U.S.A.en_US
dc.contributor.affiliationumDepartment of Sociology and Institute for Social Research, University of Michigan, ISR-4046, 426 Thompson Street, Ann Arbor, Michigan 48106–1248, U.S.A.en_US
dc.contributor.affiliationotherThe Methodology Center and Department of Statistics, Pennsylvania State University,326 Thomas Building, University Park, Pennsylvania 16802, U.S.A.en_US
dc.identifier.pmid12495129en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/65551/1/j.0006-341X.2002.00754.x.pdf
dc.identifier.doi10.1111/j.0006-341X.2002.00754.xen_US
dc.identifier.sourceBiometricsen_US
dc.identifier.citedreferenceAalen, O. ( 1976 ). Nonparametric inference in connection with multiple decrement models. Scandinavian Journal of Statistics 3, 15 – 27.en_US
dc.identifier.citedreferenceAndersen, P., Borgan, O., Gill, R., and Keiding, N. ( 1988 ). Censoring, truncation and filtering in statistical methods based on counting processes. Contemporary Mathematics 80, 19 – 60.en_US
dc.identifier.citedreferenceAndersen, P., Borgan, O., Gill, R., and Keiding, N. ( 1993 ). Statistical Models Based on Counting Processes. New York : Springer-Verlag.en_US
dc.identifier.citedreferenceArjas, E. and Haara, P. ( 1984 ). A marked point process approach to censored failure data with complicated covariates. Scandinavian Journal of Statistics 11, 193 – 209.en_US
dc.identifier.citedreferenceAxinn, W. ( 1993 ). The effects of children's schooling on fertility limitation. Population Studies 47, 481 – 493.en_US
dc.identifier.citedreferenceAxinn, W. and Barber, J. ( 2001 ). Mass education and fertility limitation. American Sociological Review 66, 481 – 505.en_US
dc.identifier.citedreferenceAxinn, W. and Yabiku, S. ( 2001 ). Social change, the social organization of families, and fertility limitation. American Journal of Sociology 106, 1219 – 1261.en_US
dc.identifier.citedreferenceAxinn, W., Barber, J., and Ghimire, D. ( 1997 ). Sociological methodology. In : The Neighborhood History Calendar, A. Raftery ( ed. ), 355 – 392. Oxford : Blackwell Publishers.en_US
dc.identifier.citedreferenceAxinn, W., Pearce, L., and Ghimire, D. ( 1999 ). Innovations in life history calendar applications. Social Science Research 28, 243 – 264.en_US
dc.identifier.citedreferenceBarber, J., Murphy, S., Axinn, W., and Maples, J. ( 2000 ). Discrete time multilevel hazards analysis. Sociological Methodology 30, 201 – 235.en_US
dc.identifier.citedreferenceBryk, A. and Raudenbush, S. ( 1992 ). Hierarchical Linear Models. Thousand Oaks, California : Sage Publications.en_US
dc.identifier.citedreferenceClayton, D. ( 1978 ). A model for association in bivariate life tables. Biometrika 65, 141 – 151.en_US
dc.identifier.citedreferenceClayton, D. and Cuzick, J. ( 1985 ). Multivariate generalizations of the proportional hazards model. Journal of the Royal Statistical Society, Series A 148, 82 – 117.en_US
dc.identifier.citedreferenceCox, D. ( 1972 ). Regression models and life tables. Journal of the Royal Statistical Society, Series B 34, 187 – 220.en_US
dc.identifier.citedreferenceDempster, A., Laird, N., and Rubin, D. ( 1977 ). Maximum likelihood estimation from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B 39, 1 – 38.en_US
dc.identifier.citedreferenceDiamond, I. D., McDonald, J. W., and Shah, I. H. ( 1986 ). Proportional hazards models for current status data: Application to the study of differentials in age at weaning in Pakistan. Demography 23, 607 – 620.en_US
dc.identifier.citedreferenceFahrmeir, L. and Tutz, G. ( 1994 ). Multivariate Statistical Modelling Based on Generalized Linear Models. New York : Springer-Verlag.en_US
dc.identifier.citedreferenceFreedman, D., Thornton, A., Camburn, D., Alwin, D., and Yong-DeMarco, L. ( 1988 ). The life history calendar: A technique for collecting retrospective data. In Sociological Methodology 1988, C. C. Clogg ( ed. ), 37 – 38. Washington, D.C. : American Sociological Association.en_US
dc.identifier.citedreferenceGilks, W., Clayton, D., Spiegelhalter, D., Best, N., and McNeil, A. ( 1993 ). Modeling complexity: Applications of Gibbs sampling in medicine. Journal of the Royal Statistical Society, Series B 55, 39 – 52.en_US
dc.identifier.citedreferenceGuo, G. and Rodriguez, G. ( 1992 ). Estimating a multivariate proportional hazards model for clustered data using the EM algorithm. Journal of the American Statistical Association 87, 969 – 976.en_US
dc.identifier.citedreferenceGustafson, P. ( 1997 ). Large hierarchical Bayesian analysis of multivariate survival data. Biometrics 53, 230 – 242.en_US
dc.identifier.citedreferenceHedeker, D. and Gibbons, R. ( 1994 ). A random-effects ordinal regression model for multilevel analysis. Biometrics 50, 993 – 944.en_US
dc.identifier.citedreferenceHedeker, D. and Gibbons, R. ( 1996 ). MIXOR: A computer program for mixed effects ordinal regression analysis. Computer Methods and Programs in Biomedicine 49, 157 – 176.en_US
dc.identifier.citedreferenceKalbfleisch, J. and Prentice, R. ( 1980 ). The Statistical Analysis of Failure Time Data. New York : Wiley.en_US
dc.identifier.citedreferenceKlein, J. ( 1992 ). Semiparametric estimation of random effects using the Cox model based on the EM algorithm. Biometrics 48, 795 – 806.en_US
dc.identifier.citedreferenceKreft, I., Leeuw, J., and Aiken, L. ( 1994 ). The effect of different forms of centering in hierarchical linear models. Technical Report 30. Research Triangle Park, North Carolina: National Institute of Statistical Sciences.en_US
dc.identifier.citedreferenceLiang, K., Self, S., and Chang, Y. ( 1993 ). Modeling marginal hazards in multivariate failure time data. Journal of the Royal Statistical Society, Series B 55, 441 – 453.en_US
dc.identifier.citedreferenceLouis, T. ( 1982 ). Finding observed information using the EM algorithm. Journal of the Royal Statistical Society, Series B 44, 98 – 130.en_US
dc.identifier.citedreferenceMcGilchrist, C. and Aisbett, C. ( 1991 ). Regression with frailty in survival analysis. Biometrics 47, 461 – 466.en_US
dc.identifier.citedreferenceMurphy, S. and van der Vaart, A. ( 1996 ). Semiparametric likelihood ratio inference. Technical Report 96–03. University Park, Pennsylvania: Pennsylvania State University, Department of Statistics.en_US
dc.identifier.citedreferenceMurphy, S. and van der Vaart, A. ( 2000 ). On profile likelihood. Journal of the American Statistical Association 95, 449 – 485.en_US
dc.identifier.citedreferenceNielsen, G., Gill, R., Andersen, P., and Sorensen, T. ( 1992 ). A counting process approach to maximum likelihood estimation in frailty models. Scandinavian Journal of Statistics 19, 25 – 43.en_US
dc.identifier.citedreferenceOakes, D. ( 1982 ). A model for bivariate survival data. Journal of the Royal Statistical Society, Series B 44, 414 – 422.en_US
dc.identifier.citedreferenceOakes, D. ( 1989 ). Bivariate survival models induced by frailties. Journal of the American Statistical Association 84, 487 – 493.en_US
dc.identifier.citedreferencePatefield, W. ( 1977 ). On the maximized likelihood function. Sankhya, Series B 39, 92 – 96.en_US
dc.identifier.citedreferenceRaudenbush, S., Yang, M., and Yosef, M. ( 2000 ). Maximum likelihood for generalized linear models with nested random effects via high-order, multivariate Laplace approximation. Journal of Computational and Graphical Statistics 9, 141 – 157.en_US
dc.identifier.citedreferenceRodriguez, G. and Goldman, N. ( 1995 ). An assessment of estimation procedures for multilevel models with binary responses. Journal of the Royal Statistical Society, Series A 158, 73 – 89.en_US
dc.identifier.citedreferenceSargent, D. ( 1998 ). A general framework for random effects survival analysis in the Cox proportional hazards setting. Biometrics 54, 1486 – 1497.en_US
dc.identifier.citedreferenceSastry, N. ( 1997 ). A nested frailty model for survival data. Journal of the American Statistical Association 92, 426 – 435.en_US
dc.identifier.citedreferenceSinha, D. and Dey, D. ( 1997 ). Semiparametric Bayesian analysis of survival data. Journal of the American Statistical Association 92, 1195 – 1212.en_US
dc.identifier.citedreferenceVaida, F. and Xu, R. ( 2000 ). Proportional hazards model with random effects. Statistics in Medicine 19, 3309 – 3324.en_US
dc.identifier.citedreferenceVaupel, J., Manton, K., and Stallard, E. ( 1979 ). The impact of heterogeneity in individual frailty on the dynamics of mortality. Demography 16, 439 – 454.en_US
dc.identifier.citedreferenceYashin, A., Vaupel, J., and Iachine, I. ( 1995 ). Correlated individual frailty: An advantageous approach to survival analysis of bivariate data. Demography 34, 31 – 48.en_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Name:
j.0006-341X.2002.00754.x.pdf
Size:
1.006MB
Format:
PDF
View/Open

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.