Joint modeling of two longitudinal outcomes and competing risk data

Statistics in Medicine

Volumn 33, Issue 18, 2014, Pages 3167-3178

  Andrinopoulou, Eleni Rosalina ^a   Rizopoulos, Dimitris ^a   Takkenberg, Johanna J M ^a   Lesaffre, Emmanuel ^a,b  

^a ERASMUS MEDICAL CENTER   (Netherlands)

^b UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

Competing risk; Continuation ratio mixed effects model; Heart valve replacement; Joint model; Linear mixed effects model

Indexed keywords

BIOLOGICAL MARKER;

AORTA VALVE; AORTA VALVE REGURGITATION; AORTIC GRADIENT; ARTICLE; B SPLINE; BAYES THEOREM; COMPETING RISK DATA; DATA ANALYSIS; DEATH; DISEASE COURSE; ECHOCARDIOGRAPHY; FEMALE; FOLLOW UP; HUMAN; INFORMATION; JOINT MODEL; LONGITUDINAL DATA; MAJOR CLINICAL STUDY; MALE; MATHEMATICAL ANALYSIS; ONE CONTINUOUS DATA; ONE ORDINAL DATA; OUTCOME ASSESSMENT; REOPERATION; STATISTICAL CONCEPTS; STATISTICAL MODEL; TIME TO EVENT DATA; ADVERSE EFFECTS; AORTIC VALVE INSUFFICIENCY; BIOLOGICAL MODEL; BIOPROSTHESIS; BIOSTATISTICS; ECHOGRAPHY; HEART VALVE PROSTHESIS; LONGITUDINAL STUDY; PATHOPHYSIOLOGY; RISK; TREATMENT OUTCOME;

AORTIC VALVE; AORTIC VALVE INSUFFICIENCY; BAYES THEOREM; BIOPROSTHESIS; BIOSTATISTICS; ECHOCARDIOGRAPHY; HEART VALVE PROSTHESIS; HUMANS; LINEAR MODELS; LONGITUDINAL STUDIES; MODELS, CARDIOVASCULAR; MODELS, STATISTICAL; REOPERATION; RISK; TREATMENT OUTCOME;

EID: 84903818599     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.6158     Document Type: Article

References (17)

1. Bekkers JA, Klieverik LM, Raap GB, Takkenberg JJM, Bogers AJJC. Re-operations for aortic allograft root failure: experience from a 21-year single-center prospective follow-up study. European Journal Cardio-Thoracic Surgery 2011; 40:35-42.
2. Little R, Rubin DB. Statistical Analysis with Missing Data. Wiley: Hoboken, NJ, 2002.
3. Li N, Elashoff RM, Li G, Saver J. Joint modeling of longitudinal ordinal data and competing risks survival times and analysis of the NINDS tr-PA stroke trial. Statistics in Medicine 2010; 29(5):546-557.
4. Faucett CL, Thomas DC. Simultaneously modelling censored survival data and repeatedly measured covariates: a gibbs sampling approach. Statistics in Medicine 1996; 15:1663-1685.
5. Wulfsohn MS, Tsiatis AA. A joint model for survival and longitudinal data measured with error. Biometrics 1997; 53:330-339.
6. Brown ER, Ibrahim JG. Bayesian approaches to joint cure-rate and longitudinal models with applications to cancer vaccine trials. Biometrics 2003; 59:686-693.
7. Song X, Davidian M, Tsiatis AA. A semiparametric likelihood approach to joint modeling of longitudinal and time-to-event data. Biometrics 2002; 58:742-753.
8. Rizopoulos D, Ghosh P. A Bayesian semiparametric multivariate joint model for multiple longitudinal outcomes and a time-to-event. Statistics in Medicine 2011; 30:1366-1380.
9. Brown ER, Ibrahim JG, DeGruttola V. A flexible B-spline model for multiple longitudinal biomarkers and survival. Biometrics 2005; 61:64-73.
10. Huang X, Li G, Elashoff RM, Pan J. A general joint model for longitudinal measurements and competing risks survival data with heterogeneous random effects. Lifetime Data Analysis 2011; 17:80-100.
11. Elashoff RM, Li G, Li N. A joint model for longitudinal measurements and survival data in the presence of multiple failure types. Biometrics 2008; 64:762-771.
12. Rizopoulos D. Dynamic predictions and prospective accuracy in joint models for longitudinal and time-to-event data. Biometrics 2011; 67:819-829.
13. Proust-Lima C, Taylor JM. 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.
14. Tsiatis AA, Davidian M. Joint modeling of longitudinal and time-to-event data: an overview. Statistica Sinica 2004; 14:809-834.
15. Yu M, Law N, Taylor J, Sandler H. Joint longitudinal-survival-cure models and their application to prostate cancer. Statistica Sinica 2004; 14:835-862.
16. Rizopoulos D. Joint Models for Longitudinal and Time-to-Event Data with Applications in R. Chapman and Hall/CRC Biostatistics Series: Boca Raton, 2012.
17. Harrell FE. Regression Modeling Strategies. Springer Series in Statistics: New York, 2001.