A general class of promotion time cure rate models with a new biological interpretation

Springer Science and Business Media LLC - Tập 29 - Trang 66-86 - 2022
Yolanda M. Gómez1, Diego I. Gallardo1,2, Marcelo Bourguignon3, Eduardo Bertolli4,5, Vinicius F. Calsavara6
1Departamento de Medicina, Facultad de Medicina, Universidad de Atacama, Copiapó, Chile
2Departamento de Matemática, Facultad de Ingeniería, Universidad de Atacama, Copiapó, Chile
3Departamento de Estatística, Universidade Federal do Rio Grande do Norte, Natal, Brazil
4Skin Cancer Department, A.C. Camargo Cancer Center, São Paulo, Brazil
5Oncology Center, Beneficência Portuguesa, São Paulo, Brazil
6Biostatistics and Bioinformatics Research Center, Cedars-Sinai Medical Center, Los Angeles, USA

Tóm tắt

Over the last decades, the challenges in survival models have been changing considerably and full probabilistic modeling is crucial in many medical applications. Motivated from a new biological interpretation of cancer metastasis, we introduce a general method for obtaining more flexible cure rate models. The proposal model extended the promotion time cure rate model. Furthermore, it includes several well-known models as special cases and defines many new special models. We derive several properties of the hazard function for the proposed model and establish mathematical relationships with the promotion time cure rate model. We consider a frequentist approach to perform inferences, and the maximum likelihood method is employed to estimate the model parameters. Simulation studies are conducted to evaluate its performance with a discussion of the obtained results. A real dataset from population-based study of incident cases of melanoma diagnosed in the state of São Paulo, Brazil, is discussed in detail.

Tài liệu tham khảo

Berrino E et al (2021) High BRAF variant allele frequencies are associated with distinct pathological features and responsiveness to target therapy in melanoma patients. ESMO Open 6:100133 Calsavara VF, Milani EA, Bertolli E, Tomazella V (2020) Long-term frailty modeling using a non-proportional hazards model: application with a melanoma dataset. Stat Methods Med Res 29:2100–2118 Chen T, Du P (2018) Promotion time cure rate model with nonparametric form of covariate effects. Stat Med 37:1625–1635 Chen M-H, Ibrahim JG, Sinha D (1999) A new Bayesian model for survival data with a surviving fraction. J Am Stat Assoc 94:909–919 Cooner F, Banerjee S, Carlin BP, Sinha D (2007) Flexible cure rate modeling under latent activation schemes. J Am Stat Assoc 102:560–572 Coordenação de Prevenção e Vigilância (2017). Instituto Nacional de Cancer José Alencar Gomes da Silva. Estimativa 2018: Incidência de Cancer no Brasil. Coordenação de Prevenção e Vigilância. Rio de Janeiro. http://www1.inca.gov.br/estimativa/2018/ de Andrade CT, Magedanz AMPCB, Escobosa DM, Tomaz WM, Santinho CS, Lopes TO, Lombardo V (2012) The importance of a database in the management of healthcare services. Einstein (São Paulo) 10:360–365 De Castro M, Cancho VG, Rodrigues J (2009) A Bayesian long-term survival model parametrized in the cured fraction. Biom J 51:443–455 Dunn P, Smyth G (1996) Randomized quantile residuals. J Comput Graph Stat 5:236–244 Ervik M, Lam F, Ferlay J, Mery L, Soerjomataram I, Bray F et al (2016) Cancer today Lyon, France: international agency for research on cancer. 2016. Cancer today. https://www.gco.iarc.fr/today. Accessed 01/02/2019 Gershenwald JE, Scolyer RA, Hess KR, Sondak VK, Long GV, Ross MI, Lazar AJ, Faries MB, Kirkwood JM, McArthur GA et al (2017) Melanoma staging: evidence-based changes in the American Joint Committee on Cancer eighth edition cancer staging manual. CA Cancer J Clin 67:472–492 Gómez YM, Gallardo DI, Leão J, Calsavara VF (2021) On a new piecewise regression model with cure rate: diagnostics and application to medical data. Stat Med 40(29):6723–6742 Gupta RC, Gupta PL, Gupta RD (1998) Modeling failure time data by Lehman alternatives. Commun Stat Theory Methods 27:887–904 Hanin L, Huang L (2014) Identifiability of cure models revisited. J Multivar Anal 130(1):261–274 Kalbfleisch JD, Prentice RL (2002) The statistical analysis of failure time data, 2nd edn. Wiley, New York Kim S, Chen MH, Dey DK (2011) A new threshold regression model for survival data with a cure fraction. Lifetime Data Anal 17:101–122 Leão J, Bourguignon M, Saulo H, Santos-Neto M, Calsavara V (2021) The negative binomial beta prime regression model with cure rate: application with a melanoma dataset. J Stat Theory Pract 15(3):1–21 Li CS, Taylor JM, Sy JP (2001) Identifiability of cure models. Stat Probab Lett 54(4):389–395 Molina KC, Calsavara VF, Tomazella VD, Milani EA (2021) Survival models induced by zero-modified power series discrete frailty: application with a melanoma data set. Stat Methods Med Res 29(8):2100–2118 Puglisi R et al (2021) Biomarkers for diagnosis, prognosis and response to immunotherapy in melanoma. Cancers (Basel) 13:2875 R Core Team (2020) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. http://www.R-project.org/ Rigby RA, Stasinopoulos DM (2005) Generalized additive models for location, scale and shape, (with discussion). Appl Stat 54(3):507–554 Rodrigues J, de Castro M, Cancho V, Balakrishnan N (2009) COM-Poisson cure rate survival models and an application to a cutaneous melanoma data. J Stat Plan Inference 139:3605–3611 Rodrigues J, de Castro M, Balakrishnan N (2011) Destructive weighted Poisson cure rate models. Lifetime Data Anal 2011(17):333–346 Rodrigues J, Cordeiro GM, Cancho VG, Balakrishnan N (2016) Relaxed Poisson cure rate models. Biom J 58:397–415 Rodrigues AS, Calsavara VF, Bertolli E, Peres SV, Tomazella VL (2021) Bayesian long-term survival model including a frailty term: application to melanoma data. Chil J Stat 12(1):53–70 Shain AH, Bastian BC (2016) From melanocytes to melanomas. Nat Rev Cancer 16:345–358 Stasinopoulos D, Rigby R (2007) Generalized additive models for location scale and shape (GAMLSS) in R. J Stat Softw 23:1–46 Tournoud M, Ecochard R (2007) Application of the promotion time cure model with time-changing exposure to the study of HIV/AIDS and other infectious diseases. Stat Med 26:1008–1021 Tournoud M, Ecochard R (2008) Promotion time models with timechanging exposure and heterogeneity: application to infectious diseases. Biom J 50:395–407 Tucker S, Thames H, Taylor J (1990) How well is the probability of tumor cure after fractionated irradiation described by Poisson statistics? Radiat Res 24:273–282 Wimme G, Altman G (1996) The multiple Poisson distribution, its characteristics and a variety of forms. Biom J 38:995–1011 Yakovlev AY, Tsodikov AD (1996) Stochastic model of tumor latency and their biostatistical applications. World Scientific, Singapore Yin G, Ibrahim J (2005) A general class of Bayesian survival models with zero and nonzero cure fractions. Biometrics 61:403–412