Which individuals make dropout informative?

Statistical Methods in Medical Research

Volumn 23, Issue 1, 2014, Pages 91-106

  Geskus, Ronald B ^a  

^a ACADEMIC MEDICAL CENTER   (Netherlands)

Author keywords

AIDS markers; Longitudinal data; missing not at random; random effects selection model

Indexed keywords

HOST FACTOR; MARKER;

ACQUIRED IMMUNE DEFICIENCY SYNDROME; ARTICLE; CENSORSHIP; CONVALESCENCE; DISEASE COURSE; DISEASE MARKER; FOLLOW UP; HUMAN; INFORMATION; LONGITUDINAL STUDY; MEASUREMENT; MEDICAL RESEARCH; PATIENT DROPOUTS; RANDOM SAMPLE; SAMPLING BIAS; SIMULATION;

AIDS MARKERS; LONGITUDINAL DATA; MISSING NOT AT RANDOM; RANDOM EFFECTS SELECTION MODEL;

BIOLOGICAL MARKERS; CD4 LYMPHOCYTE COUNT; DATA INTERPRETATION, STATISTICAL; DISEASE PROGRESSION; HIV INFECTIONS; HUMANS; MODELS, STATISTICAL; PATIENT DROPOUTS; RNA, VIRAL;

EID: 84892579021     PISSN: 09622802     EISSN: 14770334     Source Type: Journal    
DOI: 10.1177/0962280212445840     Document Type: Article

References (46)

1. Taylor JMG, Wang Y, Ahdieh L, et al. Causal pathways for CCR5 genotype and HIV progression. J Acquir Immune Defic Syndr. 2000 ; 23: 160-171
2. Geskus RB, Meyer L, Hubert JB, et al. Causal pathways of the effects of age and the CCR5-Δ32, CCR2-64I and SDF-1 3′A alleles on AIDS development. J Acquir Immune Defic Syndr Hum Retrovirol. 2005 ; 37: 514-521
3. Slate EH, Turnbull BW. Statistical models for longitudinal biomarkers of disease onset. Stat Med. 2000 ; 19: 617-637
4. Dybul M, Fauci AS, Bartlett JG, et al. Guidelines for using antiretroviral agents among HIV-infected adults and adolescents. Ann Int Med. 2002 ; 137: 381-433
5. Carpenter CCJ, Cooper DA, Fischl MA, et al. Antiretroviral therapy in adults. Updated recommendations of the International AIDS Society-USA panel. J Am Med Assoc. 2000 ; 283: 381-390
6. Mellors JW, Muñoz A, Giorgi JV, et al. Plasma viral load and CD4+lymphocytes as prognostic markers of HIV-1 infection. Ann Int Med. 1997 ; 126: 946-954
7. van Houwelingen HC. Dynamic prediction by landmarking in event history analysis. Scand J Stat. 2006 ; 34: 70-85
8. Geskus RB, Miedema FA, Goudsmit J, et al. Prediction of residual time to AIDS and death based on markers and cofactors. J Acquir Immune Defic Syndr Human Retrovirol. 2003 ; 32: 514-521
9. Lin DY, Fischl MA, Schoenfeld DA. Evaluating the role of CD4-lymphocyte counts as surrogate endpoints in human immunodeficiency virus clinical trials. Stat Med. 1993 ; 12: 835-842
10. Fleming TR, Prentice RL, Pepe MS, Glidden D. Surrogate and auxiliary endpoints in clinical trials, with potential applications in cancer and AIDS research. Stat Med. 1994 ; 13: 955-968
11. Fleming TR. Surrogate markers in AIDS and cancer trials. Stat Med. 1994 ; 13: 1423-1435
12. Lodi S, Phillips A, Touloumi G, et al. Time from human immunodeficiency virus seroconversion to reaching CD4+ cell count thresholds <200, <350, and <500 cells/mm3: Assessment of need following changes in treatment guidelines. Clin Infect Dis. 2011 ; 53: 817-825
13. Rubin DB. Inference and missing data. Biometrika. 1976 ; 63: 581-592
14. Little RJA, Rubin DB Statistical analysis with missing data. New York: Wiley ; 1987 :
15. Hogan JW, Roy J, Korkontzelou C. Handling drop-out in longitudinal studies. Stat Med. 2004 ; 23: 1455-1497
16. Diggle PJ, Heagerty PJ, Liang KY, Zeger SL Analysis of longitudinal data. New York: Oxford University Press ; 2002 :
17. Tsiatis AA, Davidian M. Joint modeling of longitudinal and time-to-event data: An overview. Statistica Sinica. 2004 ; 14: 809-834
18. Diggle P, Kenward MG. Informative drop-out in longitudinal data analysis. Appl Statist. 1994 ; 43: 49-93
19. Faucett CL, Thomas DC. Simultaneously modelling censored survival data and repeatedly measured covariates: a Gibbs sampling approach. Stat Med. 1996 ; 15: 1663-1685
20. Wulfsohn MS, Tsiatis AA. A joint model for survival and longitudinal data measured with error. Biometrics. 1997 ; 53: 330-339
21. Berzuini C, Larizza C. A unified approach for modeling longitudinal and failure time data, with application in medical monitoring. IEEE Trans Pattern Anal Mach Intell. 1996 ; 18: 109-123
22. Taylor J, Cumberland W, Sy J. A stochastic model for analysis of longitudinal AIDS data. J Am Statist Assoc. 1994 ; 89: 727-736
23. Putter H, Fiocco M, Geskus RB. Tutorial in biostatistics: competing risks and multi-state models. Stat Med. 2007 ; 26 (11). 2389-2430
24. Rizopoulos D, Ghosh P. A Bayesian semiparametric multivariate joint model for multiple longitudinal outcomes and a time-to-event. Stat Med. 2011 ; 30: 1366-1380
25. Rizopoulos D. JM: An R package for the joint modelling of longitudinal and time-to-event data. J Stat Softw. 2010 ; 35 (9). 1-33
26. R Development Core Team R. A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, 2011. ISBN 3-900051-07-0.
27. Muñoz A, Carey V, Schouten J, Segal M, Rosner B. A parametric family of correlation structures for the analysis of longitudinal data. Biometrics. 1992 ; 48: 733-742
28. Zeger SL, Diggle PJ. Semiparametric models for longitudinal data with application to CD4 cell numbers in HIV seroconverters. Biometrics. 1994 ; 50: 689-699
29. Kiuchi AS, Hartigan JA, Holford TR, Rubinstein P, Stevens CE. Change points in the series of T4 counts prior to AIDS. Biometrics. 1995 ; 51: 236-248
30. De Gruttola V, Tu XM. Modeling progression of CD4 lymphocyte count and its relation to survival time. Biometrics. 1994 ; 50: 1003-1014
31. Tsiatis AA, DeGruttola V, Wulfsohn MS. Modeling the relationship of survival to longitudinal data measured with error. Applications to survival and CD4 counts in patients with AIDS. J Am Statist Assoc. 1995 ; 90: 27-37
32. Pawitan Y, Self S. Modeling disease marker processes in AIDS. J Am Statist Assoc. 1993 ; 88: 719-726
33. Touloumi G, Pocock SJ, Babiker AG, Darbyshire JH. Estimation and comparison of rates of change in longitudinal studies with informative drop-outs. Stat Med. 1999 ; 18: 1215-1233
34. Hughes JP. Mixed effects models with censored data with application to HIV RNA levels. Biometrics. 1999 ; 55: 625-629
35. Jacqmin-Gadda H, Thiébaut R, Chêne G, Commenges D. Analysis of left-censored longitudinal data with application to viral load in HIV infection. Biostatistics. 2000 ; 1: 355-368
36. Lyles RH, Lyles CM, Taylor DJ. Random regression models for human immunodeficiency virus ribonucleic acid data subject to left censoring and informative dropouts. Appl Statist. 2000 ; 49: 485-497
37. Boscardin WJ, Taylor JMG, Law N. Longitudinal models for AIDS marker data. Stat Meth Med Res. 1998 ; 7: 13-27
38. Geskus RB. On the inclusion of prevalent cases in HIV/AIDS natural history studies through a marker-based estimate of time since seroconversion. Stat Med. 2000 ; 19: 1753-1769
39. Carré N, Deveau C, Belanger F, et al. Effect of age and exposure group on the onset of AIDS in heterosexual and homosexual HIV-infected patients. AIDS. 1994 ; 8 (6). 797-802
40. Geskus RB, Prins M, Hubert JB, et al. The HIV RNA setpoint theory revisited. Retrovirology. :
41. Taylor JMG, Law N. Does the covariance structure matter in longitudinal modelling for the prediction of future CD4 counts?. Stat Med. 1998 ; 17: 2381-2394
42. Crainiceanu C, Ruppert D, Wand M. Bayesian analysis for penalized spline regression using WinBUGS. J Stat Softw. 2005 ; 14 (14). 1-24
43. Lunn D, Thomas A, Best N, Spiegelhalter D. WinBUGS - a Bayesian modelling framework: concepts, structure, and extensibility. Stat Comput. 2000 ; 10: 325-337
44. Gilks WR, Richardson S, Spiegelhalter DJ Markov chain Monte Carlo in practice. London: Chapman & Hall ; 1996 :
45. Touloumi G, Pocock AGBSJ, Darbyshire JH. Impact of missing data due to dropouts on estimators for rates of change in longitudinal studies: a simulation study. Stat Med. 2001 ; 20: 3715-3728
46. Pantazis N, Touloumi G, Walker AS, Babiker AG. Bivariate modelling of longitudinal measurements of two human immunodeficiency type 1 disease progression markers in the presence of informative drop-outs. Appl Stat. 2005 ; 54: 405-423