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Volumn 23, Issue 1, 2014, Pages 3-10

Introduction to the special issue on joint modelling techniques

Author keywords

Dropout; longitudinal data; missing data; random effects; survival data; time dependent covariates

Indexed keywords

CLASSIFICATION; HYPOTHESIS; LONGITUDINAL STUDY; MEDICAL RESEARCH; OUTCOMES RESEARCH; PREDICTION; REVIEW; STATISTICAL MODEL;

EID: 84892599917     PISSN: 09622802     EISSN: 14770334     Source Type: Journal    
DOI: 10.1177/0962280212445800     Document Type: Review
Times cited : (30)

References (30)
  • 1
    • 0028339925 scopus 로고
    • Primary biliary cirrhosis: Prediction of short-term survival based on repeated patient visits
    • Murtaugh P, Dickson E, Van Dam G, et al. Primary biliary cirrhosis: prediction of short-term survival based on repeated patient visits. Hepatology. 1994 ; 20: 126-134
    • (1994) Hepatology , vol.20 , pp. 126-134
    • Murtaugh, P.1    Dickson, E.2    Van Dam, G.3
  • 2
    • 0036712232 scopus 로고    scopus 로고
    • Parameter estimation in longitudinal studies with outcome-dependent follow-up
    • Lipsitz S, Fitzmaurice G, Ibrahim J, et al. Parameter estimation in longitudinal studies with outcome-dependent follow-up. Biometrics. 2002 ; 58: 621-630
    • (2002) Biometrics , vol.58 , pp. 621-630
    • Lipsitz, S.1    Fitzmaurice, G.2    Ibrahim, J.3
  • 3
    • 0000336139 scopus 로고
    • Regression models and life-tables (with discussion)
    • Cox D. Regression models and life-tables (with discussion). J R Stat Soc, Ser B. 1972 ; 34: 187-220
    • (1972) J R Stat Soc, ser B , vol.34 , pp. 187-220
    • Cox, D.1
  • 7
    • 0003597030 scopus 로고    scopus 로고
    • 2nd edn. New York: Oxford University Press, 2002. New York: Oxford University Press
    • Diggle P, Heagerty P, Liang KY, et al Analysis of longitudinal data. 2 nd edn. New York: Oxford University Press, 2002. New York: Oxford University Press ; 2002 :
    • (2002) Analysis of Longitudinal Data
    • Diggle, P.1    Heagerty, P.2    Liang, K.Y.3
  • 10
    • 0001646484 scopus 로고
    • Cox's regression model for counting processes: A large sample study
    • Andersen P, Gill R. Cox's regression model for counting processes: a large sample study. Ann Stat. 1982 ; 10: 1100-1120
    • (1982) Ann Stat , vol.10 , pp. 1100-1120
    • Andersen, P.1    Gill, R.2
  • 11
    • 8644246036 scopus 로고    scopus 로고
    • Joint modeling of longitudinal and time-to-event data: An overview
    • Tsiatis A, Davidian M. Joint modeling of longitudinal and time-to-event data: an overview. Stat Sin. 2004 ; 14: 809-834
    • (2004) Stat Sin , vol.14 , pp. 809-834
    • Tsiatis, A.1    Davidian, M.2
  • 12
    • 60749123292 scopus 로고    scopus 로고
    • Missing data methods in longitudinal studies: A review
    • Ibrahim J, Molenberghs G. Missing data methods in longitudinal studies: a review. Test. 2009 ; 18: 1-43
    • (2009) Test , vol.18 , pp. 1-43
    • Ibrahim, J.1    Molenberghs, G.2
  • 14
    • 0024520844 scopus 로고
    • Surrogate endpoints in clinical trials: Definition and operation criteria
    • Prentice R. Surrogate endpoints in clinical trials: definition and operation criteria. Stat Med. 1989 ; 8: 431-440
    • (1989) Stat Med , vol.8 , pp. 431-440
    • Prentice, R.1
  • 15
    • 45849092498 scopus 로고    scopus 로고
    • Predicting renal graft failure using multivariate longitudinal profiles
    • Fieuws S, Verbeke G, Maes B, et al. Predicting renal graft failure using multivariate longitudinal profiles. Biostatistics. 2008 ; 9: 419-431
    • (2008) Biostatistics , vol.9 , pp. 419-431
    • Fieuws, S.1    Verbeke, G.2    Maes, B.3
  • 16
    • 80052787679 scopus 로고    scopus 로고
    • Dynamic predictions and prospective accuracy in joint models for longitudinal and time-to-event data
    • Rizopoulos D. Dynamic predictions and prospective accuracy in joint models for longitudinal and time-to-event data. Biometrics. 2011 ; 67: 819-829
    • (2011) Biometrics , vol.67 , pp. 819-829
    • Rizopoulos, D.1
  • 17
    • 70149108170 scopus 로고    scopus 로고
    • Development and validation of a dynamic prognostic tool for prostate cancer recurrence using repeated measures of posttreatment PSA: A joint modeling approach
    • Proust-Lima C, Taylor J. Development and validation of a dynamic prognostic tool for prostate cancer recurrence using repeated measures of posttreatment PSA: a joint modeling approach. Biostatistics. 2009 ; 10: 535-549
    • (2009) Biostatistics , vol.10 , pp. 535-549
    • Proust-Lima, C.1    Taylor, J.2
  • 18
    • 0017133178 scopus 로고
    • Inference and missing data
    • Rubin D. Inference and missing data. Biometrika. 1976 ; 63: 581-592
    • (1976) Biometrika , vol.63 , pp. 581-592
    • Rubin, D.1
  • 19
    • 85054607046 scopus 로고    scopus 로고
    • Fitzmaurice G Davidian M Verbeke G Molenberghs G, ed. Boca Raton, FL: Chapman & Hall/CRC Press
    • Verbeke G, Davidian M Longitudinal data analysis. Fitzmaurice G Davidian M Verbeke G Molenberghs G, ed. Boca Raton, FL: Chapman & Hall/CRC Press ; 2009: 319-326.
    • (2009) Longitudinal Data Analysis , pp. 319-326
    • Verbeke, G.1    Davidian, M.2
  • 20
    • 0004156740 scopus 로고    scopus 로고
    • 2nd edn. New York: Springer-Verlag, 2006. New York: Springer-Verlag
    • Nelsen R An introduction to copulas. 2 nd edn. New York: Springer-Verlag, 2006. New York: Springer-Verlag ; 2006 :
    • (2006) An Introduction to Copulas
    • Nelsen, R.1
  • 21
    • 0003866630 scopus 로고    scopus 로고
    • 2nd edn. New York: Marcel Dekker, 2004. New York: Marcel Dekker
    • Baker F, Kim S Item response theory. 2 nd edn. New York: Marcel Dekker, 2004. New York: Marcel Dekker ; 2004 :
    • (2004) Item Response Theory
    • Baker, F.1    Kim, S.2
  • 23
    • 84892604994 scopus 로고    scopus 로고
    • The analysis of multivariate longitudinal data: A review
    • Verbeke G, Fieuws S, Molenberghs G, et al. The analysis of multivariate longitudinal data: a review. Stat Meth Med Res. 2014 ; 23: 42-59
    • (2014) Stat Meth Med Res , vol.23 , pp. 42-59
    • Verbeke, G.1    Fieuws, S.2    Molenberghs, G.3
  • 24
    • 84892600752 scopus 로고    scopus 로고
    • Joint latent class models of longitudinal and time-to-event data in the context of individual dynamic predictions
    • Proust-Lima C, Sene M, Taylor J, et al. Joint latent class models of longitudinal and time-to-event data in the context of individual dynamic predictions. Stat Meth Med Res. 2014 ; 23: 74-90
    • (2014) Stat Meth Med Res , vol.23 , pp. 74-90
    • Proust-Lima, C.1    Sene, M.2    Taylor, J.3
  • 25
    • 84892579021 scopus 로고    scopus 로고
    • Which individuals are responsible for informative dropout
    • Geskus R. Which individuals are responsible for informative dropout. Stat Meth Med Res. 2014 ; 23: 91-106
    • (2014) Stat Meth Med Res , vol.23 , pp. 91-106
    • Geskus, R.1
  • 26
    • 84892607657 scopus 로고    scopus 로고
    • Dropout in crossover and longitudinal studies: Is complete case so bad
    • Matthews J, Henderson R, Farewell D, et al. Dropout in crossover and longitudinal studies: is complete case so bad. Stat Meth Med Res. 2014 ; 23: 60-73
    • (2014) Stat Meth Med Res , vol.23 , pp. 60-73
    • Matthews, J.1    Henderson, R.2    Farewell, D.3
  • 27
    • 84892577265 scopus 로고    scopus 로고
    • On random sample size, ignorability, ancillarity, completeness, separability, and degeneracy: Sequential trials, random sample sizes, and missing data
    • Molenberghs G, Kenward M, Aerts M, et al. On random sample size, ignorability, ancillarity, completeness, separability, and degeneracy: sequential trials, random sample sizes, and missing data. Stat Meth Med Res. 2014 ; 23: 11-41
    • (2014) Stat Meth Med Res , vol.23 , pp. 11-41
    • Molenberghs, G.1    Kenward, M.2    Aerts, M.3
  • 28
    • 34848824770 scopus 로고    scopus 로고
    • Analysis of longitudinal data with drop-out: Objectives, assumptions and a proposal (with discussion)
    • Diggle P, Farewell D, Henderson R. Analysis of longitudinal data with drop-out: objectives, assumptions and a proposal (with discussion). J R Stat Soc, Ser C. 2007 ; 56: 499-550
    • (2007) J R Stat Soc, ser C , vol.56 , pp. 499-550
    • Diggle, P.1    Farewell, D.2    Henderson, R.3
  • 29
    • 4243828610 scopus 로고
    • Informative dropout in longitudinal data analysis (with discussion)
    • Diggle P, Kenward M. Informative dropout in longitudinal data analysis (with discussion). J R Stat Soc, Ser C. 1994 ; 43: 49-93
    • (1994) J R Stat Soc, ser C , vol.43 , pp. 49-93
    • Diggle, P.1    Kenward, M.2
  • 30
    • 84950421496 scopus 로고
    • Analysis of semiparametric regression-models for repeated outcomes in the presence of missing data
    • Robins J, Rotnitzky A, Zhao L. Analysis of semiparametric regression-models for repeated outcomes in the presence of missing data. J Am Stat Assoc. 1995 ; 90: 106-121
    • (1995) J Am Stat Assoc , vol.90 , pp. 106-121
    • Robins, J.1    Rotnitzky, A.2    Zhao, L.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.