Estimation and identification issues in the promotion time cure model when the same covariates influence long- and short-term survival

Lambert, Philippe; Bremhorst, Vincent

doi:10.1002/bimj.201700250

Article (Scientific journals)

Estimation and identification issues in the promotion time cure model when the same covariates influence long- and short-term survival

Lambert, Philippe; Bremhorst, Vincent

2019 • In Biometrical Journal, 61 (2), p. 275-289

Peer Reviewed verified by ORBi

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https://hdl.handle.net/2268/240975

DOI
10.1002/bimj.201700250

PubMed
30345588

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Keywords :

Bayesian P-splines; Cure model; Identification issues; Insufficiently long follow-up; Survival analysis

Abstract :

[en] The promotion time cure model is a survival model acknowledging that an unidentified proportion of subjects will never experience the event of interest whatever the duration of the follow-up. We focus our interest on the challenges raised by the strong posterior correlation between some of the regression parameters when the same covariates influence long- and short-term survival. Then, the regression parameters of shared covariates are strongly correlated with, in addition, identification issues when the maximum follow-up duration is insufficiently long to identify the cured fraction. We investigate how, despite this, plausible values for these parameters can be obtained in a computationally efficient way. The theoretical properties of our strategy will be investigated by simulation and illustrated on clinical data. Practical recommendations will also be made for the analysis of survival data known to include an unidentified cured fraction. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Disciplines :

Mathematics

Author, co-author :

Lambert, Philippe ; Université de Liège - ULiège > Faculté des sciences sociales > Méthodes quantitatives en sciences sociales

Bremhorst, Vincent; Institut de statistique, biostatistique et sciences actuarielles (ISBA), Université catholique de Louvain, Louvain-la-Neuve, B-1348, Belgium

Language :

English

Title :

Estimation and identification issues in the promotion time cure model when the same covariates influence long- and short-term survival

Publication date :

2019

Journal title :

Biometrical Journal

ISSN :

0323-3847

eISSN :

1521-4036

Publisher :

Wiley-VCH Verlag

Volume :

Issue :

Pages :

275-289

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055255286&doi=10.1002%2fbimj.201700250&partnerID=40&md5=11dfcb0c03de719d63536a02935de58f

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Bibliography

Berkson, J., & Gage, R. (1952). Survival curve for cancer patients following treatment. Journal of the American Statistical Association, 47(259), 501–515.
Bremhorst, V., & Lambert, P. (2016). Flexible estimation in cure survival models using Bayesian P-splines. Computational Statistics and Data Analysis, 93, 270–284.
Bremhorst, V., Kreyenfeld, M., & Lambert, P. (2016). Fertility progression in Germany: An analysis using flexible nonparametric cure survival models. Demographic Research, 35, 505–534.
Bremhorst, V., Kreyenfeld, M., & Lambert, P. (2018). Nonparametric double additive cure survival models : An application to the estimation of the nonlinear effect of age at first parenthood on fertility progression. Statistical Modelling, 19, 1–28.
Chen, M.-H., Ibrahim, J., & Sinha, D. (1999). A new Bayesian model for survival data with a surviving fraction. Journal of the American Statistical Association, 94(447), 909–919.
Cooner, F., Banerjee, S., Carlin, B., & Sinha, D. (2007). Flexible cure rate modeling under latent activation schemes. Journal of the American Statistical Association, 102(478), 560–572.
Cox, D. (1972). Regression models and life tables. Journal of the Royal Statistic Society: Series B, 34, 187–202.
Eilers, P. H. C., & Marx, B. D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science, 11, 89–102.
Eilers, P. H. C., & Marx, B. D. (2010). Splines, knots, and penalties. Wiley Inter-disciplinary Reviews: Computational Statistics, 2, 637–653.
Gelman, A., Roberts, G. O., & Gilks, W. R. (1996). Efficient metropolis jumping rules. In J. M. Bernardo, J. O. Berger, A. P. Dawid, & A. F. M. Smith, (Eds.), Bayesian Statistics 5 (pp. 599–607). Oxford, UK: Oxford University Press.
Gressani, O., & Lambert, P. (2018). Fast Bayesian inference using Laplace approximations in a flexible promotion time cure model based on P-splines. Computational Statistics & Data Analysis, 124, 151–167.
Haario, H., Saksman, E., & Tamminen, J. (2001). An adaptive Metropolis algorithm. Bernoulli, 7(2), 223–242.
Ibrahim, J., Chen, M., & Sinha, D. (2001). Bayesian semiparametric models for survival data with a cure fraction. Biometrics, 57(2), 383–388.
Jullion, A., & Lambert, P. (2007). Robust specification of the roughness penalty prior distribution in spatially adaptive Bayesian P-splines models. Computational Statistics and Data Analysis, 51(5), 2542–2558.
Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.
Lang, S., & Brezger, A. (2004). Bayesian P-splines. Journal of Computational and Graphical Statistics, 13(1), 183–212.
Liu, H., & Shen, Y. (2009). Semiparametric parametric regression cure model for interval-censored data. Journal of the American Statistical Association, 104, 1168–1178.
Moertel, C. G., Fleming, T. R., MacDonald, J. S., Haller, D. G., Laurie, J. A., Goodman, P. J., … Maillard, J. A. (1990). Levamisole and fluorouracil for adjuvant therapy of resected colon carcinoma. New England J of Medicine, 332, 352–358.
Moertel, C. G., Fleming, T. R., MacDonald, J. S., Haller, D. G., Laurie, J. A., Tangen, C. M., … Maillard, J. A. (1991). Fluorouracil plus Levamisole as an effective adjuvant therapy after resection of stage II colon carcinoma: a final report. Annals of Internal Medicine, 122, 321–326.
Polson, N. G., & Scott, J. G. (2012). On the half-Cauchy prior for a global scale parameter. Bayesian Analysis, 7(4), 887–902.
Roberts, G. O., & Rosenthal, J. S. (2001). Optimal scaling for various Metropolis-Hastings algorithms. Statistical Science, 16(4), 351–367.
Rosenberg, P. S. (1995). Hazard function estimation using B-splines. Biometrics, 51, 874–887.
Tsodikov, A. (1998). A proportional hazard model taking account of long-term survivors. Biometrics, 54, 1508–1516.
Tsodikov, A. (2002). Semi-parametric model of long- and short-term survival: An application to the analysis of breast cancer survival in Utah by age and stage. Statistics in Medicine, 21, 895–920.
Tsodikov, A., Ibrahim, J., & Yakovlev, A. (2003). Estimating cure rates from survival data: An alternative to two-component mixture models. Journal of the American Statistical Association, 98(464), 1063–1078.
Yakovlev, A., & Tsodikov, A. (1996). Stochastic models for tumor of latency and their biostatistical applications. Singapore: World Scientific Publishing.
Yin, G., & Ibrahim, J. G. (2005). Cure rate models: A unified approach. The Canadian Journal of Statistics, 33, 559–570.
Zeng, D., Yin, G., & Ibrahim, J. (2006). Semiparametric transformation models for survival data with a cure fraction. Journal of the American Statistical Association, 101(474), 670–684.