Exportar registro bibliográfico


Metrics:

A new long-term survival model with dispersion induced by discrete frailty (2019)

  • Authors:
  • USP affiliated authors: CANCHO, VICENTE GARIBAY - ICMC ; SUZUKI, ADRIANO KAMIMURA - ICMC ; LOUZADA NETO, FRANCISCO - ICMC
  • Unidades: ICMC; ICMC; ICMC
  • DOI: 10.1007/s10985-019-09472-2
  • Subjects: ANÁLISE DE SOBREVIVÊNCIA; VEROSSIMILHANÇA; SIMULAÇÃO (ESTATÍSTICA); MELANOMA
  • Keywords: Discrete frailty; Zero-inflated power series distribution; Cure rate models; Overdispersion; Maximum likelihood estimation
  • Agências de fomento:
  • Language: Inglês
  • Imprenta:
  • Source:
  • Informações sobre o DOI: 10.1007/s10985-019-09472-2 (Fonte: oaDOI API)
    • Este periódico é de assinatura
    • Este artigo NÃO é de acesso aberto
    • Cor do Acesso Aberto: closed

    How to cite
    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas

    • ABNT

      CANCHO, Vicente Garibay; MACERA, Márcia Aparecida Centanin; SUZUKI, Adriano Kamimura; LOUZADA, Francisco; ZAVALETA, Katherine Elizabeth Coaguila. A new long-term survival model with dispersion induced by discrete frailty. Lifetime Data Analysis, New York, Springer, 2019. Disponível em: < http://dx.doi.org/10.1007/s10985-019-09472-2 > DOI: 10.1007/s10985-019-09472-2.
    • APA

      Cancho, V. G., Macera, M. A. C., Suzuki, A. K., Louzada, F., & Zavaleta, K. E. C. (2019). A new long-term survival model with dispersion induced by discrete frailty. Lifetime Data Analysis. doi:10.1007/s10985-019-09472-2
    • NLM

      Cancho VG, Macera MAC, Suzuki AK, Louzada F, Zavaleta KEC. A new long-term survival model with dispersion induced by discrete frailty [Internet]. Lifetime Data Analysis. 2019 ;Available from: http://dx.doi.org/10.1007/s10985-019-09472-2
    • Vancouver

      Cancho VG, Macera MAC, Suzuki AK, Louzada F, Zavaleta KEC. A new long-term survival model with dispersion induced by discrete frailty [Internet]. Lifetime Data Analysis. 2019 ;Available from: http://dx.doi.org/10.1007/s10985-019-09472-2

    Referências citadas na obra
    Adamidis K, Loukas S (1998) A lifetime distribution with decreasing failure rate. Stat Probab Lett 39:35–42
    Ata N, Özel G (2013) Survival functions for the frailty models based on the discrete compound Poisson process. J Stat Comput Simul 83:2105–2116
    Barral AM (2001) Immunological studies in malignant melanoma: importance of tnf and the thioredoxin system. Linkoping, Sweden: Doctorate Thesis, Linkoping University
    Barreto-Souza W, De Morais AL, Cordeiro GM (2011) The Weibull-geometric distribution. J Stat Comput Simul 81:645–657
    Barriga GDC, Louzada F (2014) The zero-inflated Conway–Maxwell–Poisson distribution: Bayesian inference, regression modeling and influence diagnostic. Stat Methodol 21:23–34
    Berkson J, Gage RP (1952) Survival curve for cancer patients following treatment. J Am Stat Assoc 47:501–515
    Boag JW (1949) Maximum likelihood estimates of the proportion of patients cured by cancer therapy. J R Stat Soc B 11:15–53
    Cancho VG, Louzada F, Ortega EM (2013) The power series cure rate model: an application to a cutaneous melanoma data. Commun Stat Simul Comput 42:586–602
    Caroni C, Crowder M, Kimber A (2010) Proportional hazards models with discrete frailty. Lifetime Data Anal 16:374–384
    del Castillo J, Pérez-Casany M (2005) Overdispersed and underdispersed Poisson generalizations. J Stat Plann Inference 134:486–500
    Chahkandi M, Ganjali M (2009) On some lifetime distributions with decreasing failure rate. Comput Stat Data Anal 53:4433–4440
    Chen MH, Ibrahim JG, Sinha D (1999) A new Bayesian model for survival data with a surviving fraction. J Am Stat Assoc 94:909–919
    Choo-Wosoba H, Levy SM, Datta S (2015) Marginal regression models for clustered count data based on zero-inflated Conway–Maxwell–Poisson distribution with applications. Biometrics 2:606–618
    Consul PC, Jain GC (1973) A generalization of the Poisson distribution. Technometrics 15:791–799
    Cordeiro GM, Cancho VG, Ortega EMM, Barriga GDC (2016) A model with long-term survivors: negative binomial Birnbaum–Saunders. Commun Stat Theory Methods 45:1370–1387
    Coskun K (2007) A new lifetime distribution. Comput Stat Data Anal 51:4497–4509
    Cox DR (1972) Regression models and life-tables. J R Stat Soc Ser B 34:187–220
    Cox DR, Hinkley DV (1979) Theoretical statistics. Chapman & Hall/CRC, Washington DC
    Duchateau L, Janssen P (2008) The frailty model. Springer, New York
    Dunn PK, Smyth GK (1996) Randomized quantile residuals. J Comput Gr Stat 5:236–244
    Efron B (1979) Bootstrap methods: another look at the jackknife. Ann Stat 7:1–26
    Efron B (1981) Censored data and the bootstrap. J Am Stat Assoc 76:312–319
    Efron B, Tibshirani R (1986) Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Stat Sci 1:54–75
    Eudes AM, Tomazella VLD, Calsavara VF (2013) Modelagem de sobrevivência com fração de cura para dados de tempo de vida weibull modificada. Rev Bras Biom 30:326–342
    Gupta PL, Gupta RC, Tripathi RC (1995) Inflated modified power series distributions with applications. Commun Stat Theory Methods 24:2355–2374
    Hougaard P (1986) A class of multivariate failure time distributions. Biometrika 73:671–678
    Hougaard PA (1984) Life table methods for heterogeneous populations: distributions describing the heterogeneity. Biometrika 71:75–83
    Ibrahim JG, Chen MH, Sinha D (2005) Bayesian survival analysis. Springer, New York
    Kirkwood JM, Ibrahim JG, Sondak VK, Richards J, Flaherty LE, Ernstoff MS, Smith TJ, Rao U, Steele M, Blum RH (2000) High- and low-dose interferon alfa-2b in high-risk melanoma: first analysis of intergroup trial E1690/S9111/C9190. J Clin Oncol 18:2444–2458
    Li CS, Taylor JMG, Sy JP (2001) Identifiability of cure models. Stat Probab Lett 54:389–395
    Li H, Zhong X (2002) Multivariate survival models induced by genetic frailties, with application to linkage analysis. Biostatistics 3:57–75
    Maller R, Zhou X (1996) Survival analysis with long-term survivors. Wiley, New York
    Milani EA, Tomazella VLD, Dias TCM, Louzada F et al (2015) The generalized time-dependent logistic frailty model: an application to a population-based prospective study of incident cases of lung cancer diagnosed in Northern Ireland. Braz J Probab Stat 29:132–144
    Moger TA, Aalen OO, Halvorsen TO, Storm HH, Tretli S (2004) Frailty modelling of testicular cancer incidence using scandinavian data. Biostatistics 5:1–14
    Morel JG, Neerchal NK (2012) Overdispersion models in SAS. SAS Institute Inc., Cary
    Morita LHM, Tomazella VL, Louzada-Neto F (2016) Accelerated lifetime modelling with frailty in a non-homogeneous Poisson Process for analysis of recurrent events data. Qual Technol Quant Manag 15:1–21
    Ortega EMM, Cordeiro GM, Campelo AK, Kattan MW, Cancho VG (2015) A power series beta weibull regression model for predicting breast carcinoma. Stat Med 34:1366–1388
    Press WH, Flannery BP, Teukolsky SA, Vetterling WT, Kramer PB (2007) Numerical recipes: the art of scientific computing. Cambridge University Press, New York
    R Development Core Team (2010) R: A language and environment for statistical computing. R Foundation for statistical computing, Vienna, Austria. http://www.R-project.org
    Rodrigues J, Cancho VG, de Castro M, Louzada-Neto F (2009) On the unification of long-term survival models. Stat Probab Lett 79:753–759
    Samani EB, Amirian Y, Ganjali M (2012) Likelihood estimation for longitudinal zero-inflated power series regression models. J Appl Stat 39:1965–1974
    Santos DM, Davies RB, Francis B (1995) Nonparametric hazard versus nonparametric frailty distribution in modelling recurrence of breast cancer. J Stat Plann Inference 47:111–127
    Sun FB, Kececloglu DB (1999) A new method for obtaining the ttt plot for a censored sample. In: Proceedings on annual reliability and maintainability symposium, IEEE, pp 112–117
    Tahmasbi R, Rezaei S (2008) A two-parameter lifetime distribution with decreasing failure rate. Comput Stat Data Anal 52:3889–3901
    Tsodikov AD, Ibrahim JG, Yakovlev AY (2003) Estimating cure rates from survival data: an alternative to two-component mixture models. J Am Stat Assoc 98:1063–1078
    Vaupel JW, Manton KG, Stallard E (1979) The impact of heterogeneity in individual frailty on the dynamics of mortality. Demography 16:439–454
    Van den Broek J (1995) A score test for zero inflation in a Poisson distribution. Biometrics 51:738–743
    Wienke A (2010) Frailty models in survival analysis. Chapman and Hall/CRC, New York
    Xue X, Brookmeyer R (1997) Regression analysis of discrete time survival data under heterogeneity. Stat Med 16:1983–1993
    Yakovlev A, Yu AB, Bardou VJ, Fourquet A, Hoang T, Rochefodiere A, Tsodikov AD (1993) A simple stochastics model of tumor recurrence an its aplications to data on premenopausal breast cancer. In: de Biométrie SF (ed) Biometrie et Analyse de Donnes Spatio-Temporelles No 12, B, France, pp 33–82
    Yakovlev AY, Tsodikov AD (1996) Stochastic Models of tumor latency and their biostatistical applications. World Scientific, New Jersey

Digital Library of Intellectual Production of Universidade de São Paulo     2012 - 2020