Joint modelling of repeated measurements and time-to-event outcomes: The fourth Armitage lecture

Statistics in Medicine

Volumn 27, Issue 16, 2008, Pages 2981-2998

  Diggle, Peter J ^a,b   Sousa, Inês ^a   Chetwynd, Amanda G ^a  

^a LANCASTER UNIVERSITY   (United Kingdom)

^b JOHNS HOPKINS BLOOMBERG SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Joint modelling; Longitudinal analysis; Time to event

Indexed keywords

NEUROLEPTIC AGENT; PLACEBO; PREDNISONE;

ARTICLE; CLINICAL RESEARCH; CLINICAL TRIAL; CONTROLLED CLINICAL TRIAL; CONTROLLED STUDY; CORRELATION ANALYSIS; DATA ANALYSIS; HEART DISEASE; HEART VALVE PROSTHESIS; HUMAN; JOINT MODELLING; KAPLAN MEIER METHOD; KERNEL METHOD; LIVER CIRRHOSIS; LONGITUDINAL STUDY; MAJOR CLINICAL STUDY; MEASUREMENT; PARAMETRIC TEST; RANDOMIZED CONTROLLED TRIAL; REPEAT PROCEDURE; SCHIZOPHRENIA; STATISTICAL ANALYSIS; STATISTICAL DISTRIBUTION; STATISTICAL MODEL;

ALGORITHMS; CLINICAL TRIALS AS TOPIC; HEART VALVE PROSTHESIS IMPLANTATION; HUMANS; LIVER CIRRHOSIS; LONGITUDINAL STUDIES; MODELS, STATISTICAL; SCHIZOPHRENIA; TIME FACTORS;

EID: 48049105068     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.3131     Document Type: Article

References (38)

1. Wulfsohn MS, Tsiatis AA. A joint model for survival and longitudinal data measured with error. Biometrics 1997; 53:330-339.
2. Tsiatis AA, Davidian M. Joint modeling of longitudinal and time-to-event data: an overview. Statistica Sinica 2004; 14:809-834.
3. Little RJA. Modeling the drop-out mechanism in repeated-measures studies. Journal of the American Statistical Association 1995; 90(431):1112-1121.
4. Diggle PJ. Dealing with missing values in longitudinal studies. In Recent Advances in the Statistical Analysis of Medical Data. Everitt BS, Dunn G (eds). Arnold: Paris, 1998; 203-228.
5. Rubi DB. Inference and missing data. Biometrika 1976; 63(3):581-592.
6. Liang KY, Zeger SL. Longitudinal data-analysis using generalized linear-models. Biometrika 1986; 73(1):13-22.
7. Andersen PK, Borgan Ø, Gill RD, Keiding N. Statistical Models based on Counting Processes. Springer: Berlin, 1997.
8. Lim E, Ali A, Theodorou P, Sousa I, Ashrafian H, Chamageorgakis T, Duncan A, Henein M, Diggle P, Pepper J. A longitudinal study of the profile and predictors of left ventricular mass regression after stentless aortic valve replacement. The Annals of Thoracic Surgery 2007; accepted.
9. Hogan JW, Laird NM. Model-based approaches to analysing incomplete longitudinal and failure time data. Statistics in Medicine 1997; 16:259-272.
10. Diggle PJ, Farewell D, Henderson R. Analysis of longitudinal data with drop-out: objectives, assumptions and a proposal. Applied Statistics 2007; 56(5):499-550.
11. Wu MC, Carroll RJ. Estimation and comparison of changes in the presence of informative right censoring by modeling the censoring process. Biometrics 1988; 44:175-188.
12. Laird NM, Ware JH. Random-effects models for longitudinal data. Biometrics 1982; 38:963-974.
13. Faucett CL, Thomas DC. Simultaneously modelling censored survival data and repeatedly measured covariates: a Gibbs sampling approach. Statistics in Medicine 1996; 15:1663-1685.
14. Xu J, Zeger SL. Joint analysis of longitudinal data comprising repeated measures and times to events. Applied Statistics 2001; 50(3):375-387.
15. Henderson R, Diggle PJ, Dobson A. Joint modelling of longitudinal measurements and event time data. Biostatistics 2000; 1(4):465-480.
16. Henderson R, Diggle PJ, Dobson A. Identification Sand efficacy of longitudinal markers for survival. Biostatistics 2002; 3:33-50.
17. Schluchter MD. Methods for the analysis of informatively censored longitudinal data. Statistics in Medicine 1992; 11:1861-1870.
18. Schluchter MD, Greene T, Beck GJ. Analysis of change in the presence of informative censoring: application to a longitudinal clinical trial of progressive renal disease. Statistics in Medicine 2001; 20:989-1007.
19. Goo W, Ratcliffe SJ, Have TT. A random pattern-mixture model for longitudinal data with dropouts. Journal of the American Statistical Association 2004; 99(468):929-937.
20. Vonesh EF, Greene T, Schluchter MD. Shared parameter models for the joint analysis of longitudinal data and event times. Statistics in Medicine 2006; 25:143-163.
21. Chi YY, Ibrahim JG. Joint models for multivariate longitudinal and multivariate survival data. Biometrics 2006; 62:432-445.
22. Robins JM, Rotnitzky A, Zhao LP. Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. Journal of the American Statistical Association 1995; 90(429):106-121.
23. Rotnitzky A, Robins JM, Scharfstein DO. Semiparametric regression for repeated outcomes with nonignorable nonresponse. Journal of the American Statistical Association 1998; 93(444):1321-1339.
24. Scharfstein DO, Rotnitzky A, Robins JM. Adjusting for nonignorable drop-out using semiparametric nonresponse models. Journal of the American Statistical Association 1999; 94:1096-1146.
25. Touloumi G. Impact of missing data due to drop-outs on estimators for rates of change in longitudinal studies: a simulation study. Statistics in Medicine 2001; 20:3715-3728.
26. DeGruttola V, Tu XM. Modelling progression of CD4-lymphocyte count and its relationship to survival time. Biometrics 1994; 50:1003-1014.
27. Cox DR. Some remarks on failure-times, surrogate markers, degradation, wear, and the quality of life. Lifetime Data Analysis 1999; 5:307-314.
28. Pawitan Y. Self S. Modelling disease marker processes in AIDS. Journal of the American Statistical Association 1993; 88:719-726.
29. Buyse M, Molenberghs G. Criteria for the validation of surrogate endpoints in randomised experiments. Biometrics 1998; 54:1014-1029.
30. Diggle PJ. An approach to the analysis of repeated measurements. Biometrics 1988; 44:959-971.
31. Kenward MG. A method for comparing profiles of repeated measurements. Applied Statistics 1987; 36:296-308.
32. Munoz A, Carey V, Schouten JP, Segal M, Rosner B. A parametric family of correlation structures for the analysis of longitudinal data. Biometrics 1992; 48:733-742.
33. Pourahmadi M. Joint mean-covariance models with applications to longitudinal data: unconstrained parametrisation. Biometrika 1999; 86(3):677-690.
34. Pan J, MacKenzie G. On modelling mean-covariance structures in longitudinal studies. Biometrika 2003; 90: 239-244.
35. Ye H, Pan J. Modelling of covariance structures in generalised estimating equations for longitudinal data. Biometrika 2006; 93:927-941.
36. Molenberghs G, Thijs H, Jansen I, Beunckens C, Kenward MG, Malinckrodt C, Carroll RJ. Analysing incomplete longitudinal clinical trial data. Biostatistics 2004; 5:445-464.
37. Xu J, Zeger SL. The evaluation of multiple surrogate endpoints. Biometrics 2001; 57:81-87.
38. Renard D, Geys H, Molenberghs G, Burzykowski T, Buyse M, Vangeneugden T. Validation of a longitudinally measured surrogate marker for a time-to-event endpoint. Journal of Applied Statistics 2003; 30(2):235-247.