A new flexible regression model with application to recovery probability Covid-19 patients

References

• M. Ahsanullah and A. Alzaatreh, Some characterizations of the log-logistic distribution, Stoch. Qual. Control 33 (2018), pp. 23–29.
   Google Scholar
• M. Alkhouli, A. Nanjundappa, F. Annie, M.C. Bates, and D.L. Bhatt, Sex differences in COVID-19 case fatality rate: Insights from a multinational registry, Mayo Clin. Proc. 29 (2020), pp. 1–22.
   Google Scholar
• N. Al-Rousan and H. Al-Najjar, Data analysis of coronavirus COVID-19 epidemic in South Korea based on recovered and death cases, J. Med. Virol. 92 (2020), pp. 1603–1608.
   PubMed Web of Science ®Google Scholar
• S.V. Buuren and M. Fredriks, Worm plot: A simple diagnostic device for modelling growth reference curves, Stat. Med. 20 (2001), pp. 1259–1277.
   PubMed Web of Science ®Google Scholar
• V.G. Cancho, J. Rodrigues, and M. de Castro, A flexible model for survival data with a cure rate: A Bayesian approach, J. Appl. Stat. 38 (2011), pp. 57–70.
   Web of Science ®Google Scholar
• Centers for Disease Control and Prevention, Coronavirus Disease 2019 (COVID-19) (2021). Accessed 2021, April 22. Available at https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html.
   Google Scholar
• G.M. Cordeiro, M. Alizadeh, G. Ozel, B. Hosseinl, E.M.M. Ortega, and E. Altun, The generalized odd log-logistic family of distributions: Properties, regression models and applications, J. Stat. Comput. Simul. 87 (2017), pp. 908–932.
   Web of Science ®Google Scholar
• G.M. Cordeiro, G.M. Rodrigues, E.M.M. Ortega, L.H. de Santana, and R. Vila, An extended Rayleigh model: Properties, regression and COVID-19 application, preprint (2022). Available at https://arxiv.org/submit/4257234/view.
   Google Scholar
• COVID-19 Dashboard by the Center for Systems Science and Engineering. (2021). Accessed 2021, April 22. Available at https://www.arcgis.com/apps/opsdashboard/index.html/bda7594740fd40299423467b48e9ecf6.
   Google Scholar
• M.K. Cowles and B.P. Carlin, Markov chain Monte Carlo convergence diagnostics: A comparative review, J. Amer. Statist. Assoc. 91 (1996), pp. 883–904.
   Web of Science ®Google Scholar
• E. Dong, H. Du, and L. Gardner, An interactive web-based dashboard to track COVID-19 in real time, Lancet Infect. Dis. 20 (2020), pp. 533–534.
   PubMed Web of Science ®Google Scholar
• P.K. Dunn and G.K. Smyth, Randomized quantile residuals, J. Comput. Graph. Stat. 5 (1996), pp. 236–244.
   Google Scholar
• J.U. Gleaton and J.D. Lynch, Properties of generalized log-logistic families of lifetime distributions, J. Probab. Statist. Sci. 4 (2006), pp. 51–64.
   Google Scholar
• J. Hewitt, B. Carter, A. Vilches-Moraga, T.J. Quinn, P. Braude, A. Verduri, L. Pearce, M. Stechman, R. Short, A. Price, J.T. Collins, E. Bruce, A. Einarsson, F. Rickard, E. Mitchell, M. Holloway, J. Hesford, F. Barlow-Pay, E. Clini, P.K. Myint, S.J. Moug, and K. McCarthy, COPE Study Collaborators, The effect of frailty on survival in patients with COVID-19 (COPE): A multicentre, European, observational cohort study, Lancet Public Health 5 (2020), pp. 444–451.
   PubMed Web of Science ®Google Scholar
• J.G. Ibrahim, M.H. Chen, and D. Sinha, Bayesian Survival Analysis, Springer, New York, 2001.
   Google Scholar
• J.-M. Jin, P. Bai, W. He, F. Wu, X.-F. Liu, D.-M. Han, S. Liu, and J.-K. Yang, Gender differences in patients with COVID-19: Focus on severity and mortality, Front. Public Health 8 (2020), pp. 1–6.
   PubMed Web of Science ®Google Scholar
• E.L. Kaplan and P. Meier, Nonparametric estimation from incomplete observations, J. Am. Stat. Assoc. 53 (1958), pp. 457–481.
   Web of Science ®Google Scholar
• J.F. Lawless, Statistical Models and Methods for Lifetime Data, John Wiley & Sons, New Jersey, 2003.
   Google Scholar
• E.T. Lee and J.W. Wang, Statistical Methods for Survival Data Analysis, John Wiley & Sons, New Jersey, 2002.
   Google Scholar
• C.-S. Li, J.M.G. Taylor, and J.P. Sy, Identifiability of cure models, Stat. Probab. Lett. 54 (2001), pp. 389–395.
   Web of Science ®Google Scholar
• R.A. Maller and X. Zhou, Survival Analysis with Long-term Survivors, John Wiley & Sons, New Jersey, 1996.
   Google Scholar
• M.R. Mehra, S.S. Desai, S. Kuy, T.D. Henry, and A.N. Patel, Cardiovascular disease, drug therapy, and mortality in COVID-19, N. Engl. J. Med. 382 (2020), pp. 1–7.
   PubMed Web of Science ®Google Scholar
• Ministério da Saúde – Brasil, Coronavírus: COVID-19 (2021). Accessed 2021, April 22. Available at https://coronavirus.saude.gov.br/sobre-a-doencasintomas.
   Google Scholar
• V. Morena, L. Milazzo, L. Oreni, G. Bestetti, T. Fossali, C. Bassoli, A. Torre, M.V. Cossu, C. Minari, E. Ballone, A. Perotti, D. Mileto, F. Niero, S. Merli, A. Foschi, S. Vimercati, G. Rizzardini, S. Sollima, L. Bradanini, L. Galimberti, R. Combo, V. Micheli, C. Negri, A.L. Ridolfo, L. Meroni, M. Galli, S. Antinori, and M. Corbellino, Off-label use of tocilizumab for the treatment of SARS-CoV-2 pneumonia in Milan, Italy, Eur. J. Intern. Med. 76 (2020), pp. 36–42.
   PubMed Web of Science ®Google Scholar
• E.M.M. Ortega, V.G. Cancho, and V.H. Lachos, A generalized log-gamma mixture model for cure rate: Estimation and sensitivity analysis, Sankhya 71 (2009), pp. 1–29.
   Google Scholar
• E.M.M. Ortega, J.N. da Cruz, and G.M. Cordeiro, The log-odd logistic-Weibull regression model under informative censoring, Model Assist. Stat. Appl. 14 (2019), pp. 239–254.
   Google Scholar
• F. Prataviera, S.M.C. Loibel, K.F. Greco, E.M.M. Ortega, and G.M. Cordeiro, Modelling non-proportional hazard for survival data with different systematic components, Environ. Ecol. Stat. 27 (2020), pp. 467–489.
   Web of Science ®Google Scholar
• F. Prataviera, E.M.M. Ortega, and G.M. Cordeiro, A new bimodal Maxwell regression model with engineering applications, Appl. Math. Inf. Sci. 14 (2020), pp. 817–831.
   Google Scholar
• F. Prataviera, A.M.M. Silva, E.J.B.N. Cardoso, G.M. Cordeiro, and E.M.M. Ortega, A novel generalized odd log-logistic Maxwell-based regression with application to microbiology, Appl. Math. Model. 93 (2021), pp. 148–164.
   Web of Science ®Google Scholar
• D.M. Stasinopoulos, R.A. Rigby, and C. Akantziliotou, Instructions on How to Use the GAMLSS Package in R (2008). Available at http://www.gamlss.com/wp-content/uploads/2013/01/gamlss-manual.pdf.
   Google Scholar
• D.M. Stasinopoulos, R.A. Rigby, G.Z. Heller, V. Voudouris, and F. De Bastiani, Flexible Regression and Smoothing: Using GAMLSS in R, Chapman and Hall/CRC, New York, 2017.
   Google Scholar
• I. Voinsky, G. Baristaite, and D. Gurwitz, Effects of age and sex on recovery from COVID-19: Analysis of 5769 Israeli patients, J. Infect. 81 (2020), pp. 102–103.
   PubMed Web of Science ®Google Scholar
• J. Xie, N. Covassin, Z. Fan, P. Singh, W. Gao, G. Li, T. Kara, and V.K. Somers, Association between hypoxemia and mortality in patients with COVID-19, Mayo Clin. Proc. 95 (2020), pp. 1138–1147.
   PubMed Web of Science ®Google Scholar
• Y. Yan, Y. Yang, F. Wang, H. Ren, S. Zhang, X. Shi, X. Yu, and K. Dong, Clinical characteristics and outcomes of patients with severe Covid-19 with diabetes, BMJ Open Diabetes Res. Care 8 (2020), pp. 1–9.
   Web of Science ®Google Scholar
• A.-P. Yang, J.-P. Liu, W.-Q. Tao, and H.-M. Li, The diagnostic and predictive role of NLR, d-NLR and PLR in COVID-19 patients, Int. Immunopharmacol. 84 (2020), pp. 1–7.
   Web of Science ®Google Scholar