Modelling recurrent events: A tutorial for analysis in epidemiology

International Journal of Epidemiology

Volumn 44, Issue 1, 2015, Pages 324-333

  Amorim, Leila D A F ^a   Cai, Jianwen ^b  

^a FEDERAL UNIVERSITY OF BAHIA   (Brazil)

^b IL   (United States)

Author keywords

Recurrent events; Survival modelling; Time to event data

Indexed keywords

CANCER; CHILD HEALTH; EPIDEMIOLOGY; MULTIVARIATE ANALYSIS; SOFTWARE; SURVIVAL;

ANDERSEN AND GILL MODEL; ARTICLE; BLADDER CANCER; BRAZIL; COMPUTER PROGRAM; FEMALE; FRAILTY MODEL; HUMAN; INFANT; LOWER RESPIRATORY TRACT INFECTION; MALE; MARGINAL MEAN RATE MODEL; MULTIVARIATE ANALYSIS; PRENTICE WILLIAMS AND PETERSON MODEL; PRIORITY JOURNAL; STATISTICAL MODEL; SURVIVAL; ALGORITHM; EPIDEMIOLOGY; STATISTICAL ANALYSIS; STATISTICAL BIAS;

BRAZIL;

ALGORITHMS; BIAS (EPIDEMIOLOGY); DATA INTERPRETATION, STATISTICAL; EPIDEMIOLOGIC METHODS; EPIDEMIOLOGIC STUDIES; HUMANS; MODELS, STATISTICAL;

EID: 84924588896     PISSN: 03005771     EISSN: 14643685     Source Type: Journal    
DOI: 10.1093/ije/dyu222     Document Type: Article

References (31)

1. Pandeya N, Purdie DM, le Green A, Williams G. Repeated occurrence of basal cell carcinoma of the skin and multifailure survival analysis: follow-up data from the Nambour Skin Cancer Prevention Trial. Am J Epidemiol 2005;161: 748-54.
2. Cox DR. Regression models and life-tables (with Discussion). J Royal Stat Soc B 1972;34: 182-220.
3. Andersen PK, Gill RD. Cox's regression model for counting processes: a large sample study. Ann Stat 1982;10: 1100-20.
4. Prentice RL, Williams BJ, Peterson AV. On the regression analysis of multivariate failure time data. Biometrika 1981;68: 373-79.
5. Wei LJ, Lin DY, Weissfeld L. Regression analysis of multivariate incomplete failure time data by modelling marginal distributions. J Am Stat Assoc 1989;84: 1065-73.
6. Therneau TM, Grambsch PM. Modelling Survival Data: Extending The Cox Model. 2nd edn. New York, NY: Springer, 2000.
7. Pepe MS, Cai J. Some graphical displays and marginal regression analysis for recurrent failure times and time dependent covariates. J Am Stat Assoc 1993;88: 811-20.
8. Lin DY, Wei LJ, Yang I, Ying Z. Semiparametric regression for the mean and rate functions of recurrent events. J R Stat Soc B 2000;62: 711-30.
9. Kennedy BS, Kasl SV, Vaccarino V. Repeated hospitalizations and self-rated health among the elderly: a multivariate failure time analysis. Am J Epidemiol 2001;153: 232-41.
10. Gill DP, Zou GY, Jones GR, Speechley M. Comparison of regression models for the analysis of fall risk factors in older veterans. Ann Epidemiol 2009;19: 523-30.
11. Moulton LH, Dibley MJ. Multivariate time-to-event models for studies of recurrent childhood diseases. Int J Epidemiol 1997;26: 1334-39.
12. Kelly PJ, Lim L L-Y. Survival analysis for recurrent event data: an application to childhood infectious diseases. Stat Med 2000;19: 13-33.
13. Cook RJ, Lawless JF. The Statistical Analysis of Recurrent Events. 2nd edn. New York, NY: Springer, 2010.
14. Paes AT, de Lima ACP. A SAS macro for estimating transition probabilities in semiparametric models for recurrent events. Computer Methods Programmes Biomed 2004;75: 59-65.
15. Castañeda J, Gerritse B. Appraisal of several methods to model time to multiple events per subject: modelling time to hospitalizations and death. Rev Colomb Estadística 2010;33: 43-61.
16. Zeng D, Lin DY. Efficient estimation of semiparametric transformation models for counting processes. Biometrika 2006;93: 627-40.
17. Barreto ML, Santos LMP, Assis AMO et al. Effect of vitamin A supplementation on diarrhoea and acute lower-respiratorytract infections in young children in Brazil. Lancet 1994;344: 228-31.
18. Lawless JF. The analysis of recurrent events for multiple subjects. J R Stat Soc C 1995;44: 487-98.
19. Cai J, Schaubel D. Analysis of recurrent event data. In: Balakrishnan N, Rao CR (eds). Handbook of Statistics: Advances in Survival Analysis. Vol. 23. Amsterdam: Elsevier, 2004.
20. Amorim LD, Cai J, Zeng D, Barreto M. Regression splines in the time-dependent coefficient rates model for recurrent event data. Stat Med 2008;27: 5890-906.
21. Andersen PK, Keiding N. Multi-state models for event history analysis. Stat Methods Med Res 2002;11: 91-115.
22. Meira-Machado L, Uña-Alvarez J, Cadarso-Suárez C, Andersen PK. Multi-state models for the analysis of time-to-event data. Stat Methods Med Res 2009;18: 195-222.
23. SAS Institute. SAS/STAT Software. Cary, NC: SAS Institute, 1999-2001.
24. STATACorp. Stata Statistical Software: Release 11.0. College Station, TX: STATACorp, 2010.
25. R Development Core Team. R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing, 2012.
26. Gharibvand L, Liu L. Analysis of survival data with clustered events. Paper 237. SAS Global Forum 2009, 22-25 March. Washington, DC, 2009.
27. Cleves M. Analysis of multiple failure-time data with Stata. Stata Tech Bull 1999;49: 30-39.
28. Dacourt V, Quantin C, Abrahamowicz M, Blinquet C, Alioum A, Faivre J. Modelling recurrence in colorectal cancer. J Clin Epidemiol 2004;57: 243-51.
29. Purroy F, Caballero PEJ, Gorospe A et al. Recurrent transient ischaemic attack and early risk of stroke: data from the PROMAPA Study. J Neurol Neurosurg Psychiatry 2013;84: 596-603.
30. Miloslavsky M, Keles S, van der Laan MJ, Butler S. Recurrent events analysis in the presence of time-dependent covariates and dependent censoring. J R Stat Soc B 2004;66: 239-57.
31. Amorim LD, Cai J, Zeng D. Proportional rate models for recurrent time event data under dependent censoring: a comparative study. In: Bhattacharjee M, Dhar SK, Subramanian S (eds). Recent Advances in Biostatistics: False Discovery Rates, Survival Analysis and Related Topics. Hackensack, NJ: World Scientific, 2011.