When a patient is registered on renal transplant waiting list, she/he expects a clear information on the likelihood of being transplanted. Nevertheless, this event is in competition with death and usual models for competing events are difficult to interpret for non-specialists. We used a horizontal mixture model. Data were extracted from two French dialysis and transplantation registries. The “Ile-de-France” region was used for external validation. The other patients were randomly divided for training and internal validation. Seven variables were associated with decreased long-term probability of transplantation: age over 40 years, comorbidities (diabetes, cardiovascular disease, malignancy), dialysis longer than 1 year before registration and blood groups O or B. We additionally demonstrated longer mean time-to-transplantation for recipients under the age of 50, overweight recipients, recipients with blood group O or B and with pre-transplantation anti-HLA class I or II immunization. Our model can be used to predict the long-term probability of transplantation and the time in dialysis among transplanted patients, two easily interpretable parts. Discriminative capacities were validated on both the internal and external (AUC at 5 years = 0.72, 95% CI from 0.68 to 0.76) validation samples. However, calibration issues were highlighted and illustrated the importance of complete re-estimation of the model for other countries. We illustrated the ease of interpretation of horizontal modelling, which constitutes an alternative to sub-hazard or cause-specific approaches. Nevertheless, it would be useful to test this in practice, for instance by questioning both the physicians and the patients. We believe that this model should also be used in other chronic diseases, for both etiologic and prognostic studies.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Price includes VAT (USA)
Tax calculation will be finalised during checkout.
Wolfe RA, Ashby VB, Milford EL, Ojo AO, Ettenger RE, Agodoa LY, et al. Comparison of mortality in all patients on dialysis, patients on dialysis awaiting transplantation, and recipients of a first cadaveric transplant. N Engl J Med. 1999;341:1725–30.
Tonelli M, Wiebe N, Knoll G, Bello A, Browne S, Jadhav D, et al. Systematic review: kidney transplantation compared with dialysis in clinically relevant outcomes. Am J Transpl Off J Am Soc Transpl Am Soc Transpl Surg. 2011;11:2093–109.
Wong G, Howard K, Chapman JR, Chadban S, Cross N, Tong A, et al. Comparative survival and economic benefits of deceased donor kidney transplantation and dialysis in people with varying ages and co-morbidities. PLoS ONE. 2012;7:e29591.
Satayathum S, Pisoni RL, McCullough KP, Merion RM, Wikström B, Levin N, et al. Kidney transplantation and wait-listing rates from the international Dialysis Outcomes and Practice Patterns Study (DOPPS). Kidney Int. 2005;68:330–7.
Danovitch GM, Cohen DJ, Weir MR, Stock PG, Bennett WM, Christensen LL, et al. Current status of kidney and pancreas transplantation in the United States, 1994–2003. Am J Transpl Off J Am Soc Transpl Am Soc Transpl Surg. 2005;5:904–15.
Li PK-T, Chu KH, Chow KM, Lau MF, Leung CB, Kwan BCH, et al. Cross sectional survey on the concerns and anxiety of patients waiting for organ transplants. Nephrol. Carlton Vic. 2012;17:514–8.
Akman B, Uyar M, Afsar B, Sezer S, Ozdemir FN, Haberal M. Adherence, depression and quality of life in patients on a renal transplantation waiting list. Transpl. Int. Off. J. Eur. Soc. Organ Transplant. 2007;20:682–7.
Davison SN. The ethics of end-of-life care for patients with ESRD. Clin. J. Am. Soc. Nephrol. CJASN. 2012;7:2049–57.
Vandecasteele SJ, Kurella Tamura M. A patient-centered vision of care for ESRD: dialysis as a bridging treatment or as a final destination? J. Am. Soc. Nephrol. JASN. 2014;25:1647–51.
Fine JP, Gray RJ. A Proportional Hazards Model for the Subdistribution of a Competing Risk. J Am Stat Assoc. 1999;94:496–509.
Prentice RL, Kalbfleisch JD, Peterson AV, Flournoy N, Farewell VT, Breslow NE. The analysis of failure times in the presence of competing risks. Biometrics. 1978;34:541–54.
Cox D. Regression models and life-tables. J. R. Stat. Soc. Ser. B. 1972;34:187–229.
Andersen PK, Geskus RB, Witte T de, Putter H. Competing risks in epidemiology: possibilities and pitfalls. Int. J. Epidemiol. 2012;dyr213.
Sapir-Pichhadze R, Pintilie M, Tinckam KJ, Laupacis A, Logan AG, Beyene J, et al. Survival Analysis in the Presence of Competing Risks: The Example of Waitlisted Kidney Transplant Candidates. Am J Transplant. 2016;16:1958–66.
Fuller R, Dudley N, Blacktop J. Risk communication and older people—understanding of probability and risk information by medical inpatients aged 75 years and older. Age Ageing. 2001;30:473–6.
Larson MG, Dinse GE. A Mixture Model for the Regression Analysis of Competing Risks Data. Appl Stat. 1985;34:201.
Checkley W, Brower RG, Muñoz A. Inference for Mutually Exclusive Competing Events Through a Mixture of Generalized Gamma Distributions. Epidemiology. 2010;21:557–65.
Lau B, Cole SR, Gange SJ. Competing risk regression models for epidemiologic data. Am J Epidemiol. 2009;170:244–56.
Empirical Transition Matrix of Multi-State Models: The etm Package| Allignol| Journal of Statistical Software [Internet]. [cited 2016 Mar 7]. Available from: https://www.jstatsoft.org/article/view/v038i04.
Foucher Y, Danger R. Time dependent ROC curves for the estimation of true prognostic capacity of microarray data. Stat Appl Genet Mol Biol. 2012;11:Article 1.
Blanche P, Dartigues J-F, Jacqmin-Gadda H. Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks. Stat Med. 2013;32:5381–97.
R Development Core Team. R: A Language and Environment for Statistical Computing [Internet]. Computing RF for S, editor. Vienna, Austria; 2010. Available from: http://www.R-project.org/.
Geskus RB. Cause-specific cumulative incidence estimation and the fine and gray model under both left truncation and right censoring. Biometrics. 2011;67:39–49.
Murphy DJ, Burrows D, Santilli S, Kemp AW, Tenner S, Kreling B, et al. The Influence of the Probability of Survival on Patients’ Preferences Regarding Cardiopulmonary Resuscitation. N Engl J Med. 1994;330:545–9.
Andersen PK, Keiding N. Interpretability and importance of functionals in competing risks and multistate models. Stat Med. 2012;31:1074–88.
Henderson R, Keiding N. Individual survival time prediction using statistical models. J Med Ethics. 2005;31:703–6.
Hollnagel H. Explaining risk factors to patients during a general practice consultation. Conveying group-based epidemiological knowledge to individual patients. Scand J Prim Health Care. 1999;17:3–5.
This work was supported by grants from the French Ministry of Health (PHRC, PROG/11/85, 2011) and the French National Agency of Research (ANR-II- JSV1-0008-01). K. Trébern-Launay was also the recipient of a grant for epidemiology and biostatistics research from the RTRS ‘CENTAURE’. We wish to thank the members of The French Réseau Epidémiologie et Information en Néphrologie (REIN) Registry, the French Cristal Registry and the Nephrolor network.
Electronic supplementary material
Below is the link to the electronic supplementary material.
About this article
Cite this article
Trébern-Launay, K., Kessler, M., Bayat-Makoei, S. et al. Horizontal mixture model for competing risks: a method used in waitlisted renal transplant candidates. Eur J Epidemiol 33, 275–286 (2018). https://doi.org/10.1007/s10654-017-0322-3
- Competing events
- Mixture model
- Kidney transplantation
- Prognostic study