Semiparametric Modeling of Longitudinal Measurements and Time-to-Event Data–A Two-Stage Regression Calibration Approach
dc.contributor.author | Ye, Wen | en_US |
dc.contributor.author | Lin, Xihong | en_US |
dc.contributor.author | Taylor, Jeremy M. G. | en_US |
dc.date.accessioned | 2010-04-01T15:03:20Z | |
dc.date.available | 2010-04-01T15:03:20Z | |
dc.date.issued | 2008-12 | en_US |
dc.identifier.citation | Ye, Wen; Lin, Xihong; Taylor, Jeremy M. G. (2008). "Semiparametric Modeling of Longitudinal Measurements and Time-to-Event Data–A Two-Stage Regression Calibration Approach." Biometrics 64(4): 1238-1246. <http://hdl.handle.net/2027.42/65518> | en_US |
dc.identifier.issn | 0006-341X | en_US |
dc.identifier.issn | 1541-0420 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/65518 | |
dc.identifier.uri | http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=18261160&dopt=citation | en_US |
dc.description.abstract | In this article we investigate regression calibration methods to jointly model longitudinal and survival data using a semiparametric longitudinal model and a proportional hazards model. In the longitudinal model, a biomarker is assumed to follow a semiparametric mixed model where covariate effects are modeled parametrically and subject-specific time profiles are modeled nonparametrially using a population smoothing spline and subject-specific random stochastic processes. The Cox model is assumed for survival data by including both the current measure and the rate of change of the underlying longitudinal trajectories as covariates, as motivated by a prostate cancer study application. We develop a two-stage semiparametric regression calibration (RC) method. Two variations of the RC method are considered, risk set regression calibration and a computationally simpler ordinary regression calibration. Simulation results show that the two-stage RC approach performs well in practice and effectively corrects the bias from the naive method. We apply the proposed methods to the analysis of a dataset for evaluating the effects of the longitudinal biomarker PSA on the recurrence of prostate cancer. | en_US |
dc.format.extent | 184220 bytes | |
dc.format.extent | 3110 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.publisher | Blackwell Publishing Inc | en_US |
dc.rights | ©2008 International Biometric Society | en_US |
dc.subject.other | Joint Modeling | en_US |
dc.subject.other | Longitudinal Data | en_US |
dc.subject.other | Semiparametric Mixed Models | en_US |
dc.subject.other | Smoothing Splines | en_US |
dc.subject.other | Survival Analysis | en_US |
dc.title | Semiparametric Modeling of Longitudinal Measurements and Time-to-Event Data–A Two-Stage Regression Calibration Approach | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Mathematics | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, U.S.A. | en_US |
dc.contributor.affiliationother | Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, U.S.A. | en_US |
dc.identifier.pmid | 18261160 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/65518/1/j.1541-0420.2007.00983.x.pdf | |
dc.identifier.doi | 10.1111/j.1541-0420.2007.00983.x | en_US |
dc.identifier.source | Biometrics | en_US |
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dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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