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Volumn 3, Issue 4, 2009, Pages 855-868

A review on joint models in biometrical research

Author keywords

Joint model; Mixed effects model; Shared random effects; Survival analysis

Indexed keywords


EID: 85008794156     PISSN: 15598608     EISSN: 15598616     Source Type: Journal    
DOI: 10.1080/15598608.2009.10411965     Document Type: Article
Times cited : (8)

References (44)
  • 4
    • 9244253051 scopus 로고    scopus 로고
    • A joint model for longitudinal data profiles and associated event risks with application to a depression study
    • Bowman, F.D., Manatunga, A.K., 2005. A joint model for longitudinal data profiles and associated event risks with application to a depression study. Applied Statistics, 54, 301–316.
    • (2005) Applied Statistics , vol.54 , pp. 301-316
    • Bowman, F.D.1    Manatunga, A.K.2
  • 5
    • 0037869564 scopus 로고    scopus 로고
    • A Bayesian semiparametric joint hierarchical model for longitudinal and survival data
    • Brown, E.R., Ibrahim, J.G., 2003. A Bayesian semiparametric joint hierarchical model for longitudinal and survival data. Biometrics, 59, 221–228.
    • (2003) Biometrics , vol.59 , pp. 221-228
    • Brown, E.R.1    Ibrahim, J.G.2
  • 6
    • 33745288267 scopus 로고    scopus 로고
    • Joint models for multivariate longitudinal and multivariate survival data
    • Chi, Y.-Y., Ibrahim, J.G., 2006. Joint models for multivariate longitudinal and multivariate survival data. Biometrics, 62, 432–445.
    • (2006) Biometrics , vol.62 , pp. 432-445
    • Chi, Y.-Y.1    Ibrahim, J.G.2
  • 7
    • 33745234831 scopus 로고    scopus 로고
    • Prostate-specific antigen (PSA) alone is not an appropriate surrogate marker of long-term therapeutic benefit in prostate cancer trials
    • Collette, L., Burzykowski, T., Schröder, F.H., 2006. Prostate-specific antigen (PSA) alone is not an appropriate surrogate marker of long-term therapeutic benefit in prostate cancer trials. European Journal of Cancer, 42, 1344–1350.
    • (2006) European Journal of Cancer , vol.42 , pp. 1344-1350
    • Collette, L.1    Burzykowski, T.2    Schröder, F.H.3
  • 8
    • 33846844812 scopus 로고    scopus 로고
    • Using trajectories from a bivariate growth curve as predictors in a Cox regression model
    • Dang, Q., Mazumdar, S., Anderson, S.J., Houck, P.R., Reynolds, C.F., 2007. Using trajectories from a bivariate growth curve as predictors in a Cox regression model. Statistics in Medicine, 26, 800–811.
    • (2007) Statistics in Medicine , vol.26 , pp. 800-811
    • Dang, Q.1    Mazumdar, S.2    Erson, S.J.3    Houck, P.R.4    Reynolds, C.F.5
  • 9
    • 62349108495 scopus 로고    scopus 로고
    • Magnetic resonance imaging as an outcome in MS clinical trials
    • Daumer, M., Neuhaus, A., Morrissey, S.P., Hintzen, R., Ebers, G., 2009. Magnetic resonance imaging as an outcome in MS clinical trials. Neurology, 72, 705–711.
    • (2009) Neurology , vol.72 , pp. 705-711
    • Daumer, M.1    Neuhaus, A.2    Morrissey, S.P.3    Hintzen, R.4    Ebers, G.5
  • 10
    • 0028627762 scopus 로고
    • Modelling progression of CD4-lymphocyte count and its relationship to survival time
    • DeGruttola, V., Tu, X.M., 1994. Modelling progression of CD4-lymphocyte count and its relationship to survival time. Biometrics, 50, 1003–1014.
    • (1994) Biometrics , vol.50 , pp. 1003-1014
    • Degruttola, V.1    Tu, X.M.2
  • 11
    • 34249899104 scopus 로고    scopus 로고
    • An approach to joint analysis of longitudinal measurements and competing risks failure time data
    • Elashoff, R.M., Li, G., Li, N., 2007. An approach to joint analysis of longitudinal measurements and competing risks failure time data. Statistics in Medicine, 26, 2813–2835.
    • (2007) Statistics in Medicine , vol.26 , pp. 2813-2835
    • Elashoff, R.M.1    Li, G.2    Li, N.3
  • 12
    • 1342289473 scopus 로고    scopus 로고
    • Separate and joint modelling of longitudinal and event time data using standard computer packages
    • Guo, X., Carlin, B.P., 2004. Separate and joint modelling of longitudinal and event time data using standard computer packages. The American Statistician, 58, 16–24.
    • (2004) The American Statistician , vol.58 , pp. 16-24
    • Guo, X.1    Carlin, B.P.2
  • 13
    • 0141742204 scopus 로고    scopus 로고
    • Estimating frailty models via Poisson hierarchical generalized linear models
    • Ha, I.D., Lee, Y., 2003a. Estimating frailty models via Poisson hierarchical generalized linear models. Journal of Computational and Graphical Statistics, 12, 663–681.
    • (2003) Journal of Computational and Graphical Statistics , vol.12 , pp. 663-681
    • Ha, I.D.1    Lee, Y.2
  • 14
    • 0141429907 scopus 로고    scopus 로고
    • Joint modelling of repeated measures and survival time data
    • Ha, I.D., Park, T., Lee, Y., 2003b. Joint modelling of repeated measures and survival time data. Biometrical Journal, 45, 647–658.
    • (2003) Biometrical Journal , vol.45 , pp. 647-658
    • Ha, I.D.1    Park, T.2    Lee, Y.3
  • 15
    • 0000710136 scopus 로고    scopus 로고
    • Joint modelling of longitudinal measurements and event time data
    • Henderson, R., Diggle, P., Dobson, A., 2000. Joint modelling of longitudinal measurements and event time data. Biostatistics, 1, 465–480.
    • (2000) Biostatistics , vol.1 , pp. 465-480
    • Henderson, R.1    Diggle, P.2    Dobson, A.3
  • 16
    • 0031032359 scopus 로고    scopus 로고
    • Mixture models for the joint distribution of repeated measures and event times
    • Hogan, J.W., Laird, N.M., 1997. Mixture models for the joint distribution of repeated measures and event times. Statistics in Medicine, 16, 239–257.
    • (1997) Statistics in Medicine , vol.16 , pp. 239-257
    • Hogan, J.W.1    Laird, N.M.2
  • 17
    • 10944241763 scopus 로고    scopus 로고
    • Mixtures for varying coefficient models for longitudinal data with discrete or continuous nonignorable dropout
    • Hogan, J.W., Lin, X., Herman, B., 2004. Mixtures for varying coefficient models for longitudinal data with discrete or continuous nonignorable dropout. Biometrics, 60, 854–864.
    • (2004) Biometrics , vol.60 , pp. 854-864
    • Hogan, J.W.1    Lin, X.2    Herman, B.3
  • 18
    • 33845482505 scopus 로고    scopus 로고
    • Joint modeling of survival and longitudinal data: Likelihood approach revisited
    • Hsieh, F., Tseng, Y.-K., Wand, J.-L., 2006. Joint modeling of survival and longitudinal data: likelihood approach revisited. Biometrics, 62, 1037–1043.
    • (2006) Biometrics , vol.62 , pp. 1037-1043
    • Hsieh, F.1    Tseng, Y.-K.2    Wand, J.-L.3
  • 19
    • 3442883055 scopus 로고    scopus 로고
    • Bayesian methods for joint modeling of longitudinal and survival data with applications to cancer vaccine trials
    • Ibrahim, J.G., Chen, M.-H., Sinha, D., 2004. Bayesian methods for joint modeling of longitudinal and survival data with applications to cancer vaccine trials. Statistica Sinica, 14, 863–883.
    • (2004) Statistica Sinica , vol.14 , pp. 863-883
    • Ibrahim, J.G.1    Chen, M.-H.2    Sinha, D.3
  • 21
    • 0021035886 scopus 로고
    • Rating neurologic impairment in multiple sclerosis: An expanded disability status scale (EDSS)
    • Kurtzke, J.F., 1983. Rating neurologic impairment in multiple sclerosis: An expanded disability status scale (EDSS). Neurology, 33, 1444–1452.
    • (1983) Neurology , vol.33 , pp. 1444-1452
    • Kurtzke, J.F.1
  • 22
    • 36749005103 scopus 로고    scopus 로고
    • Joint models for a primary endpoint and multiple longitudinal covariate processes
    • Li, E., Wang, N., Wang, N.-Y., 2007. Joint models for a primary endpoint and multiple longitudinal covariate processes. Biometrics, 63, 1068–1078.
    • (2007) Biometrics , vol.63 , pp. 1068-1078
    • Li, E.1    Wang, N.2    Wang, N.-Y.3
  • 23
    • 0010772182 scopus 로고
    • Semiparametric inference for the accelerated life model with time-dependent covariates
    • Lin, D.Y., Zhiliang, Y., 1995. Semiparametric inference for the accelerated life model with time-dependent covariates. Journal of Statistical Planning and Inference, 44, 47–63.
    • (1995) Journal of Statistical Planning and Inference , vol.44 , pp. 47-63
    • Lin, D.Y.1    Zhiliang, Y.2
  • 24
    • 0037199763 scopus 로고    scopus 로고
    • Maximum likelihood estimation in the joint analysis of time-to-event and multiple longitudinal variables
    • Lin, H., McCulloch, C.E., Mayne, S.E., 2002a. Maximum likelihood estimation in the joint analysis of time-to-event and multiple longitudinal variables. Statistics in Medicine, 21, 2369–2382.
    • (2002) Statistics in Medicine , vol.21 , pp. 2369-2382
    • Lin, H.1    McCulloch, C.E.2    Mayne, S.E.3
  • 25
    • 0036489045 scopus 로고    scopus 로고
    • Latent class models for joint analysis of lon8 gitudinal biomarker and event process data: Application to longitudinal prostate-specific antigen readings and prostate cancer
    • Lin, H., Turnbull, B.W., McCulloch, C.E., Slate, E.H., 2002b. Latent class models for joint analysis of lon8 gitudinal biomarker and event process data: Application to longitudinal prostate-specific antigen readings and prostate cancer. Journal of the American Statistical Association, 97, 53–65.
    • (2002) Journal of the American Statistical Association , vol.97 , pp. 53-65
    • Lin, H.1    Turnbull, B.W.2    McCulloch, C.E.3    Slate, E.H.4
  • 27
    • 34547657276 scopus 로고    scopus 로고
    • Joint analysis of longitudinal data with informative right censoring
    • Liu, M., Ying, Z., 2007. Joint analysis of longitudinal data with informative right censoring. Biometrics, 63, 363–371.
    • (2007) Biometrics , vol.63 , pp. 363-371
    • Liu, M.1    Ying, Z.2
  • 28
    • 10944224477 scopus 로고    scopus 로고
    • Joint modeling of longitudinal and survival data via a common frailty
    • Ratcliffe, S.J., Guo, W., TenHave, T.R., 2004. Joint modeling of longitudinal and survival data via a common frailty. Biometrics, 60, 892–899.
    • (2004) Biometrics , vol.60 , pp. 892-899
    • Ratcliffe, S.J.1    Guo, W.2    Tenhave, T.R.3
  • 30
    • 0035971784 scopus 로고    scopus 로고
    • A methodology for analysing a repeated measures and survival outcome simultaneously
    • Rochon, J., Gillespie, B.W., 2001. A methodology for analysing a repeated measures and survival outcome simultaneously. Statistics in Medicine, 20, 1173–1184.
    • (2001) Statistics in Medicine , vol.20 , pp. 1173-1184
    • Rochon, J.1    Gillespie, B.W.2
  • 31
    • 0027092589 scopus 로고
    • Methods for the analysis of informatively censored longitudinal data
    • Schluchter, M.D., 1992. Methods for the analysis of informatively censored longitudinal data. Statistics in Medicine, 11, 1861–1870.
    • (1992) Statistics in Medicine , vol.11 , pp. 1861-1870
    • Schluchter, M.D.1
  • 32
    • 0037093814 scopus 로고    scopus 로고
    • Joint modelling the relationship between survival and pulmonary function in cystic fibrosis patients
    • Schluchter, M.D., Konstan, M.W., Davis, P.B., 2002. Joint modelling the relationship between survival and pulmonary function in cystic fibrosis patients. Statistics in Medicine, 21, 1271–1287.
    • (2002) Statistics in Medicine , vol.21 , pp. 1271-1287
    • Schluchter, M.D.1    Konstan, M.W.2    Davis, P.B.3
  • 33
    • 2342525727 scopus 로고    scopus 로고
    • An estimator for the proportional hazards model with multiple longitudinal covariates measured with error
    • Song, X., Davidian, M., Tsiatis, A.A., 2002a. An estimator for the proportional hazards model with multiple longitudinal covariates measured with error. Biostatistics, 3, 511–528.
    • (2002) Biostatistics , vol.3 , pp. 511-528
    • Song, X.1    Davidian, M.2    Tsiatis, A.A.3
  • 34
    • 0036966285 scopus 로고    scopus 로고
    • A semiparametric likelihood approach to joint modeling of longitudinal and time-to-event data
    • Song, X., Davidian, M., Tsiatis, A.A., 2002b. A semiparametric likelihood approach to joint modeling of longitudinal and time-to-event data. Biometrics, 58, 742–753.
    • (2002) Biometrics , vol.58 , pp. 742-753
    • Song, X.1    Davidian, M.2    Tsiatis, A.A.3
  • 35
    • 0036916799 scopus 로고    scopus 로고
    • Surrogate markers and joint models for longitudinal and survival data
    • Taylor, J. M.G., Wang, Y., 2002. Surrogate markers and joint models for longitudinal and survival data. Controlled Clinical Trials, 23, 626–634.
    • (2002) Controlled Clinical Trials , vol.23 , pp. 626-634
    • Taylor, J.M.G.1    Wang, Y.2
  • 36
    • 24144440046 scopus 로고    scopus 로고
    • Joint modelling of accelerated failure time and longitudinal data
    • Tseng, Y.-K., Hsieh, F., Wang, J.-L., 2005. Joint modelling of accelerated failure time and longitudinal data. Biometrika, 92, 587–603.
    • (2005) Biometrika , vol.92 , pp. 587-603
    • Tseng, Y.-K.1    Hsieh, F.2    Wang, J.-L.3
  • 37
    • 0001659147 scopus 로고    scopus 로고
    • A semiparametric estimator for proportional hazards model with longi34 tudinal covariates measured with error
    • Tsiatis, A.A., Davidian, M., 2001. A semiparametric estimator for proportional hazards model with longi34 tudinal covariates measured with error. Biometrika, 88, 447–458.
    • (2001) Biometrika , vol.88 , pp. 447-458
    • Tsiatis, A.A.1    Davidian, M.2
  • 38
    • 8644246036 scopus 로고    scopus 로고
    • Joint modelling of longitudinal and time-to-event data: An overview
    • Tsiatis, A.A., Davidian, M., 2004. Joint modelling of longitudinal and time-to-event data: An overview. Statistica Sinica, 14, 809–834.
    • (2004) Statistica Sinica , vol.14 , pp. 809-834
    • Tsiatis, A.A.1    Davidian, M.2
  • 39
    • 21844506206 scopus 로고
    • Modeling the relationship of survival to longitudinal data measured with error. Applications to survival and CD4 counts in patients with AIDS
    • Tsiatis, A.A., DeGruttola, V., Wulfson, M.S., 1995. Modeling the relationship of survival to longitudinal data measured with error. Applications to survival and CD4 counts in patients with AIDS. Journal of the American Statistical Association, 90, 27–37.
    • (1995) Journal of the American Statistical Association , vol.90 , pp. 27-37
    • Tsiatis, A.A.1    Degruttola, V.2    Wulfson, M.S.3
  • 40
    • 31344445432 scopus 로고    scopus 로고
    • Shared parameter models for the joint analysis of longitudinal data and event times
    • Vonesh, E.F., Greene, T., Schluchter, M.D., 2006. Shared parameter models for the joint analysis of longitudinal data and event times. Statistics in Medicine, 25, 143–163.
    • (2006) Statistics in Medicine , vol.25 , pp. 143-163
    • Vonesh, E.F.1    Greene, T.2    Schluchter, M.D.3
  • 41
    • 0033933686 scopus 로고    scopus 로고
    • Regression analysis when covariates are regression parameters of a random effects model for observed longitudinal measurements
    • Wang, C.Y., Wang, N., Wang, S., 2000. Regression analysis when covariates are regression parameters of a random effects model for observed longitudinal measurements. Biometrics, 56, 487–495.
    • (2000) Biometrics , vol.56 , pp. 487-495
    • Wang, C.Y.1    Wang, N.2    Wang, S.3
  • 42
    • 0442327792 scopus 로고    scopus 로고
    • Joint modelling longitudinal and event time data with application to acquired immunodeficiency syndrome
    • Wang, Y., Taylor, J.M.G., 2001. Joint modelling longitudinal and event time data with application to acquired immunodeficiency syndrome. Journal of the American Statistical Association, 96, 895–905.
    • (2001) Journal of the American Statistical Association , vol.96 , pp. 895-905
    • Wang, Y.1    Taylor, J.M.G.2
  • 43
    • 0030893266 scopus 로고    scopus 로고
    • A joint model for survival and longitudinal data measured with error
    • Wulfson, M.S., Tsiatis, A.A., 1997. A joint model for survival and longitudinal data measured with error. Biometrics, 53, 330–339.
    • (1997) Biometrics , vol.53 , pp. 330-339
    • Wulfson, M.S.1    Tsiatis, A.A.2
  • 44
    • 17444429292 scopus 로고    scopus 로고
    • Simultaenous modelling of survival and longitudinal data with an application to repeated quality of life measures
    • Zeng, D., Cai, J., 2005. Simultaenous modelling of survival and longitudinal data with an application to repeated quality of life measures. Lifetime Data Analysis, 11, 151–174.
    • (2005) Lifetime Data Analysis , vol.11 , pp. 151-174
    • Zeng, D.1    Cai, J.2


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