198
Views
0
CrossRef citations to date
0
Altmetric
Application Notes

The Log-Normal zero-inflated cure regression model for labor time in an African obstetric population

ORCID Icon, ORCID Icon, ORCID Icon, , , , & show all
Pages 2416-2429 | Received 25 Oct 2018, Accepted 22 Feb 2021, Published online: 09 Mar 2021

References

  • W. Barreto-Souza, Long-term survival models with overdispersed number of competing causes, Comput. Stat. Data. Anal. 91 (2015), pp. 51–63.
  • L.J. Beesley and J.M. Taylor, Em algorithms for fitting multistate cure models, Biostatistics 20 (2019), pp. 416–432.
  • J. Berkson and R.P. Gage, Survival curve for cancer patients following treatment, J. Am. Stat. Assoc. 47 (1952), pp. 501–515.
  • W. Bertoli, K.S. Conceição, M.G. Andrade, and F. Louzada, A bayesian approach for some zero-modified poisson mixture models, Stat. Modelling. 20 (2020), pp. 467–501.
  • J.W. Boag, Maximum likelihood estimates of the proportion of patients cured by cancer therapy, J. R. Stat. Soc. Ser. B (Methodological) 11 (1949), pp. 15–53.
  • R. Braekers and Y. Grouwels, A semi-parametric Cox's regression model for zero-inflated left-censored time to event data, Commun. Stat. Theor. Meth. 45 (2016), pp. 1969–1988.
  • V. Bremhorst and P. Lambert, Flexible estimation in cure survival models using Bayesian P-splines, Comput. Stat. Data Anal. 93 (2016), pp. 270–284.
  • V.F. Calsavara, A.S. Rodrigues, R. Rocha, F. Louzada, V. Tomazella, A.C. Souza, R.A. Costa, and R.P. Francisco, Zero-adjusted defective regression models for modeling lifetime data, J. Appl. Stat. 46 (2019), pp. 2434–2459.
  • V.F. Calsavara, A.S. Rodrigues, V.L.D. Tomazella, and M. de Castro, Frailty models power variance function with cure fraction and latent risk factors negative binomial, Commun. Stat. Theor. Meth. 46 (2017), pp. 9763–9776.
  • V.G. Cancho, F. Louzada, D.K. Dey, and G.D. Barriga, A new lifetime model for multivariate survival data with a surviving fraction, J. Stat. Comput. Simul. 86 (2016), pp. 279–292.
  • M.-H. Chen, J.G. Ibrahim, and D. Sinha, A new Bayesian model for survival data with a surviving fraction, J. Am. Stat. Assoc. 94 (1999), pp. 909–919.
  • M.R. de Oliveira, F. Moreira, and F. Louzada, The zero-inflated promotion cure rate model applied to financial data on time-to-default, Cogent Econom. Financ. 5 (2017), pp. 1395950.
  • D.I. Gallardo, H. Bolfarine, and A.C. Pedroso-de Lima, Destructive weighted poisson cure rate models with bivariate random effects: classical and Bayesian approaches, Comput. Stat. Data. Anal. 98 (2016), pp. 31–45.
  • D.I. Gallardo, Y.M. Gómez, and M. de Castro, A flexible cure rate model based on the polylogarithm distribution, J. Stat. Comput. Simul. 88 (2018), pp. 2137–2149.
  • J.F.B. Gonzales, V.L.D. Tomazella, and J.P. Taconelli, Estimação paramétrica do modelo de mistura com fragilidade gama na presença de covariáveis, Rev. Bras. Biom.1 (2013), pp. 233–247.
  • D. Lambert, Zero-inflated poisson regression, with an application to defects in manufacturing, Technometrics 34 (1992), pp. 1–14.
  • J.F Lawless, Statistical Models and Methods for Lifetime Data, Vol. 362, John Wiley & Sons, Hoboken, NJ, 2011.
  • J. Leão, M. Bourguignon, D.I. Gallardo, R. Rocha, and V. Tomazella, A new cure rate model with flexible competing causes with applications to melanoma and transplantation data, Stat. Med. 39 (2020), pp. 3272–3284.
  • L. Liu, Y.-C.T. Shih, R.L. Strawderman, D. Zhang, B.A. Johnson, and H. Chai, Statistical analysis of zero-inflated nonnegative continuous data: a review, Stat. Sci. 34 (2019), pp. 253–279.
  • F. Louzada, F.F. Moreira, and M.R. de Oliveira, A zero-inflated non default rate regression model for credit scoring data, Commun. Stat. Theor. Meth. 47 (2018), pp. 3002–3021.
  • F. Louzada-Neto, Extended hazard regression model for reliability and survival analysis, Lifetime. Data. Anal. 3 (1997), pp. 367–381.
  • A.R. Marinho and R.H. Loschi, Bayesian cure fraction models with measurement error in the scale mixture of normal distribution, Stat. Methods. Med. Res. 29 (2020), pp. 2411–2444.
  • E.Z. Martinez, J.A. Achcar, and T.R. Icuma, Bivariate basu-dhar geometric model for survival data with a cure fraction, Electron. J. Appl. Stat. Anal. 11 (2018), pp. 655–673.
  • B. Neelon, A.J. O'Malley, and V.A. Smith, Modeling zero-modified count and semicontinuous data in health services research part 1: background and overview, Stat. Med. 35 (2016), pp. 5070–5093.
  • O.T. Oladapo, J.P. Souza, M.A. Bohren, B. Fawole, K. Mugerwa, and A.M. Gülmezoglu, WHO better outcomes in labour difficulty (BOLD) project: innovating to improve quality of care around the time of childbirth, Reprod. Health. 12 (2015), pp. 48.
  • O. T. Oladapo, J. P. Souza, B. Fawole, K. Mugerwa, G. Perdoná, D. Alves, H. Souza, R. Reis, L. Oliveira-Ciabati, A. Maiorano, A. Akintan, F. E. Alu, L. Oyeneyin, A. Adebayo, J. Byamugisha, M. Nakalembe, H. A. Idris, O. Okike, F. Althabe, V. Hundley, F. Donnay, R. Pattinson, H. C. Sanghvi, J. E. Jardine, Ö Tunçalp, J. P. Vogel, M. E. Stanton, M. Bohren, J. Zhang, T. Lavender, J. Liljestrand, P. ten Hoope-Bender, M. Mathai, R. Bahl, A. M. Gülmezoglu, and L. Persson, Progression of the first stage of spontaneous labour: A prospective cohort study in two sub-saharan african countries, PLoS Med. 15 (2018), pp. e1002492.
  • R. Ospina and S.L. Ferrari, A general class of zero-or-one inflated beta regression models, Comput. Stat. Data Anal. 56 (2012), pp. 1609–1623.
  • G.C. Perdoná and F. Louzada-Neto, A general hazard model for lifetime data in the presence of cure rate, J. Appl. Stat. 38 (2011), pp. 1395–1405.
  • R Core Team. R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2017.
  • T.G. Ramires, N. Hens, G.M. Cordeiro, and E.M. Ortega, Estimating nonlinear effects in the presence of cure fraction using a semi-parametric regression model, Comput. Stat. 33 (2018), pp. 709–730.
  • V. Rondeau, E. Schaffner, F. Corbière, J.R. Gonzalez, and S. Mathoulin-Pélissier, Cure frailty models for survival data: application to recurrences for breast cancer and to hospital readmissions for colorectal cancer, Stat. Methods. Med. Res. 22 (2013), pp. 243–260.
  • S.M. Ross, Introduction to Probability Models, Academic Press, Los Angeles, CA, 2014.
  • J. Scudilio, V.F. Calsavara, R. Rocha, F. Louzada, V. Tomazella, and A.S. Rodrigues, Defective models induced by gamma frailty term for survival data with cured fraction, J. Appl. Stat. 46 (2019), pp. 484–507.
  • J. P. Souza, O. T Oladapo, M. A Bohren, K. Mugerwa, B. Fawole, L. Moscovici, D. Alves, G. Perdona, L. Oliveira-Ciabati, J. P Vogel, Ö Tunçalp, J. Zhang, J. Hofmeyr, R. Bahl, and A M. Gülmezoglu, The development of a simplified, effective, labour monitoring-to-action (SELMA) tool for better outcomes in labour difficulty (BOLD): study protocol, Reprod. Health 12 (2015), pp. 49.
  • C.-L. Su and F.-C. Lin, Analysis of clustered failure time data with cure fraction using copula, Stat. Med. 38 (2019), pp. 3961–3973.
  • A. Vahratian, J. Zhang, J.F. Troendle, A.C. Sciscione, and M.K. Hoffman, Labor progression and risk of cesarean delivery in electively induced nulliparas, Obstetrics & Gynecology 105 (2005), pp. 698–704.
  • J. Zhang, H.J. Landy, D.W. Branch, R. Burkman, S. Haberman, K.D. Gregory, C.G. Hatjis, M.M. Ramirez, J.L. Bailit, V.H. Gonzalez-Quintero, and J.U. Hibbard Contemporary patterns of spontaneous labor with normal neonatal outcomes, Obstet. Gynecol. 116 (2010), pp. 1281.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.