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do Espirito Santo APJ, Cancho VG, Louzada F, Ortega EMM. A survival model for lifetime with long-term survivors and unobserved heterogeneity. BRAZ J PROBAB STAT 2022. [DOI: 10.1214/22-bjps549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
| | - Vicente G. Cancho
- Institute for Mathematical Science and Computing, University of São Paulo, São Carlos, São Paulo, Brazil
| | - Francisco Louzada
- Institute for Mathematical Science and Computing, University of São Paulo, São Carlos, São Paulo, Brazil
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2
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Wallace S, Hall V, Charlett A, Kirwan PD, Cole M, Gillson N, Atti A, Timeyin J, Foulkes S, Taylor-Kerr A, Andrews N, Shrotri M, Rokadiya S, Oguti B, Vusirikala A, Islam J, Zambon M, Brooks TJG, Ramsay M, Brown CS, Chand M, Hopkins S. Impact of prior SARS-CoV-2 infection and COVID-19 vaccination on the subsequent incidence of COVID-19: a multicentre prospective cohort study among UK healthcare workers - the SIREN (Sarscov2 Immunity & REinfection EvaluatioN) study protocol. BMJ Open 2022; 12:e054336. [PMID: 35768083 PMCID: PMC9240450 DOI: 10.1136/bmjopen-2021-054336] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
INTRODUCTION Understanding the effectiveness and durability of protection against SARS-CoV-2 infection conferred by previous infection and COVID-19 is essential to inform ongoing management of the pandemic. This study aims to determine whether prior SARS-CoV-2 infection or COVID-19 vaccination in healthcare workers protects against future infection. METHODS AND ANALYSIS This is a prospective cohort study design in staff members working in hospitals in the UK. At enrolment, participants are allocated into cohorts, positive or naïve, dependent on their prior SARS-CoV-2 infection status, as measured by standardised SARS-CoV-2 antibody testing on all baseline serum samples and previous SARS-CoV-2 test results. Participants undergo monthly antibody testing and fortnightly viral RNA testing during follow-up and based on these results may move between cohorts. Any results from testing undertaken for other reasons (eg, symptoms, contact tracing) or prior to study entry will also be captured. Individuals complete enrolment and fortnightly questionnaires on exposures, symptoms and vaccination. Follow-up is 12 months from study entry, with an option to extend follow-up to 24 months.The primary outcome of interest is infection with SARS-CoV-2 after previous SARS-CoV-2 infection or COVID-19 vaccination during the study period. Secondary outcomes include incidence and prevalence (both RNA and antibody) of SARS-CoV-2, viral genomics, viral culture, symptom history and antibody/neutralising antibody titres. ETHICS AND DISSEMINATION The study was approved by the Berkshire Research Ethics Committee, Health Research Authority (IRAS ID 284460, REC reference 20/SC/0230) on 22 May 2020; the vaccine amendment was approved on 12 January 2021. Participants gave informed consent before taking part in the study.Regular reports to national and international expert advisory groups and peer-reviewed publications ensure timely dissemination of findings to inform decision making. TRIAL REGISTRATION NUMBER ISRCTN11041050.
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Affiliation(s)
- Sarah Wallace
- National Infection Service, UK Health Security Agency, London, UK
| | - Victoria Hall
- National Infection Service, UK Health Security Agency, London, UK
| | - Andre Charlett
- Statistics, Modelling and Economics Unit, UK Health Security Agency, London, UK
| | - Peter D Kirwan
- National Infection Service, UK Health Security Agency, London, UK
- MRC Biostatistics Unit, Cambridge, UK
| | - Michele Cole
- National Infection Service, UK Health Security Agency, London, UK
| | - Natalie Gillson
- National Infection Service, UK Health Security Agency, London, UK
| | - Ana Atti
- National Infection Service, UK Health Security Agency, London, UK
| | - Jean Timeyin
- National Infection Service, UK Health Security Agency, London, UK
| | - Sarah Foulkes
- National Infection Service, UK Health Security Agency, London, UK
| | | | - Nick Andrews
- Statistics, Modelling and Economics Unit, UK Health Security Agency, London, UK
| | | | - Sakib Rokadiya
- National Infection Service, UK Health Security Agency, London, UK
| | - Blanche Oguti
- National Infection Service, UK Health Security Agency, London, UK
| | | | - Jasmin Islam
- National Infection Service, UK Health Security Agency, London, UK
| | - Maria Zambon
- National Infection Service, UK Health Security Agency, London, UK
| | - Tim J G Brooks
- National Infection Service, UK Health Security Agency, London, UK
| | - Mary Ramsay
- National Infection Service, UK Health Security Agency, London, UK
| | - Colin S Brown
- National Infection Service, UK Health Security Agency, London, UK
| | - Meera Chand
- National Infection Service, UK Health Security Agency, London, UK
| | - Susan Hopkins
- National Infection Service, UK Health Security Agency, London, UK
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3
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A survival regression with cure fraction applied to cervical cancer. Comput Stat 2022. [DOI: 10.1007/s00180-022-01233-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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Rahmati M, Rezanejad Asl P, Mikaeli J, Zeraati H, Rasekhi A. Compound Poisson frailty model with a gamma process prior for the baseline hazard: accounting for a cured fraction. J Appl Stat 2021; 49:3377-3391. [DOI: 10.1080/02664763.2021.1947997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Maryam Rahmati
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
- Reproductive Endocrinology Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Parisa Rezanejad Asl
- Department of Biostatistics, Faculty of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Javad Mikaeli
- Autoimmune and Motility Disorders Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Hojjat Zeraati
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Aliakbar Rasekhi
- Biostatistics Department, Medical Sciences Faculty, Tarbiat Modares University, Tehran, Iran
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Molina KC, Calsavara VF, Tomazella VD, Milani EA. Survival models induced by zero-modified power series discrete frailty: Application with a melanoma data set. Stat Methods Med Res 2021; 30:1874-1889. [PMID: 33955295 DOI: 10.1177/09622802211011187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Survival models with a frailty term are presented as an extension of Cox's proportional hazard model, in which a random effect is introduced in the hazard function in a multiplicative form with the aim of modeling the unobserved heterogeneity in the population. Candidates for the frailty distribution are assumed to be continuous and non-negative. However, this assumption may not be true in some situations. In this paper, we consider a discretely distributed frailty model that allows units with zero frailty, that is, it can be interpreted as having long-term survivors. We propose a new discrete frailty-induced survival model with a zero-modified power series family, which can be zero-inflated or zero-deflated depending on the parameter value. Parameter estimation was obtained using the maximum likelihood method, and the performance of the proposed models was performed by Monte Carlo simulation studies. Finally, the applicability of the proposed models was illustrated with a real melanoma cancer data set.
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Affiliation(s)
- Katy C Molina
- Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos, SP, Brazil.,Department of Statistics, Federal University of São Carlos, São Carlos, SP, Brazil
| | - Vinicius F Calsavara
- Department of Epidemiology and Statistics, A.C.Camargo Cancer Center, São Paulo, SP, Brazil.,Biostatistics and Bioinformatics Research Center, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Vera D Tomazella
- Department of Statistics, Federal University of São Carlos, São Carlos, SP, Brazil
| | - Eder A Milani
- Institute of Mathematics and Statistics, Federal University of Goiás, Goiânia, Brazil
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Cancho VG, Barriga GDC, Cordeiro GM, Ortega EMM, Suzuki AK. Bayesian survival model induced by frailty for lifetime with long‐term survivors. STAT NEERL 2021. [DOI: 10.1111/stan.12236] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Affiliation(s)
- Vicente G. Cancho
- Departamento de Matemática Aplicada e Estatística ICMC, Universidade de São Paulo São Carlos Brazil
| | - Gladys D. C. Barriga
- Departamento de Engenharia de Produção FEB, Universidade Estadual Paulista Bauru Brazil
| | - Gauss M. Cordeiro
- Departamento de Estatística CCEN, Universidade Federal de Pernambuco Recife Brazil
| | - Edwin M. M. Ortega
- Departamento de Ciências Exatas ESALQ, Universidade de São Paulo Piracicaba Brazil
| | - Adriano K. Suzuki
- Departamento de Matemática Aplicada e Estatística ICMC, Universidade de São Paulo São Carlos Brazil
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Karamoozian A, Baneshi MR, Bahrampour A. Bayesian mixture cure rate frailty models with an application to gastric cancer data. Stat Methods Med Res 2020; 30:731-746. [PMID: 33243085 DOI: 10.1177/0962280220974699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Mixture cure rate models are commonly used to analyze lifetime data with long-term survivors. On the other hand, frailty models also lead to accurate estimation of coefficients by controlling the heterogeneity in survival data. Gamma frailty models are the most common models of frailty. Usually, the gamma distribution is used in the frailty random variable models. However, for survival data which are suitable for populations with a cure rate, it may be better to use a discrete distribution for the frailty random variable than a continuous distribution. Therefore, we proposed two models in this study. In the first model, continuous gamma as the distribution is used, and in the second model, discrete hyper-Poisson distribution is applied for the frailty random variable. Also, Bayesian inference with Weibull distribution and generalized modified Weibull distribution as the baseline distribution were used in the two proposed models, respectively. In this study, we used data of patients with gastric cancer to show the application of these models in real data analysis. The parameters and regression coefficients were estimated using the Metropolis with Gibbs sampling algorithm, so that this algorithm is one of the crucial techniques in Markov chain Monte Carlo simulation. A simulation study was also used to evaluate the performance of the Bayesian estimates to confirm the proposed models. Based on the results of the Bayesian inference, it was found that the model with generalized modified Weibull and hyper-Poisson distributions is a suitable model in practical study and also this model fits better than the model with Weibull and Gamma distributions.
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Affiliation(s)
- Ali Karamoozian
- Department of Biostatistics and Epidemiology, 48463Kerman University of Medical Sciences, Kerman, Islamic Republic of Iran.,Modeling in Health Research Center, Institute for Futures Studies in Health, 48463Kerman University of Medical Sciences, Kerman, Islamic Republic of Iran
| | - Mohammad Reza Baneshi
- Department of Biostatistics and Epidemiology, 48463Kerman University of Medical Sciences, Kerman, Islamic Republic of Iran.,Modeling in Health Research Center, Institute for Futures Studies in Health, 48463Kerman University of Medical Sciences, Kerman, Islamic Republic of Iran
| | - Abbas Bahrampour
- Department of Biostatistics and Epidemiology, 48463Kerman University of Medical Sciences, Kerman, Islamic Republic of Iran.,Modeling in Health Research Center, Institute for Futures Studies in Health, 48463Kerman University of Medical Sciences, Kerman, Islamic Republic of Iran
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Cancho VG, Suzuki AK, Barriga GDC, Santo APJDE. A multivariate survival model induced by discrete frailty. COMMUN STAT-SIMUL C 2020. [DOI: 10.1080/03610918.2020.1806323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Vicente G. Cancho
- Department of Applied Mathematics and Statistics, University of São Paulo, São Paulo, Brazil
| | - Adriano K. Suzuki
- Department of Applied Mathematics and Statistics, University of São Paulo, São Paulo, Brazil
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Leão J, Bourguignon M, Gallardo DI, Rocha R, Tomazella V. A new cure rate model with flexible competing causes with applications to melanoma and transplantation data. Stat Med 2020; 39:3272-3284. [PMID: 32716081 DOI: 10.1002/sim.8664] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Revised: 05/15/2020] [Accepted: 05/26/2020] [Indexed: 01/08/2023]
Abstract
In this article, we introduce a long-term survival model in which the number of competing causes of the event of interest follows the zero-modified geometric (ZMG) distribution. Such distribution accommodates equidispersion, underdispersion, and overdispersion and captures deflation or inflation of zeros in the number of lesions or initiated cells after the treatment. The ZMG distribution is also an appropriate alternative for modeling clustered samples when the number of competing causes of the event of interest consists of two subpopulations, one containing only zeros (cure proportion), while in the other (noncure proportion) the number of competing causes of the event of interest follows a geometric distribution. The advantage of this assumption is that we can measure the cure proportion in the initiated cells. Furthermore, the proposed model can yield greater or lower cure proportion than that of the geometric distribution when modeling the number of competing causes. In this article, we present some statistical properties of the proposed model and use the maximum likelihood method to estimate the model parameters. We also conduct a Monte Carlo simulation study to evaluate the performance of the estimators. We present and discuss two applications using real-world medical data to assess the practical usefulness of the proposed model.
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Affiliation(s)
- Jeremias Leão
- Department of Statistics, Universidade Federal do Amazonas, Amazonas, Brazil
| | - Marcelo Bourguignon
- Department of Statistics, Universidade Federal do Rio Grande do Norte, Natal, Brazil
| | - Diego I Gallardo
- Department of Mathematics, Universidad de Atacama, Copiapó, Chile
| | - Ricardo Rocha
- Department of Statistics, Universidade Federal da Bahia, Salvador, Brazil
| | - Vera Tomazella
- Department of Statistics, Universidade Federal de São Carlos, São Paulo, Brazil
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Hurtado Rúa SM, Dey DK. A Bayesian piecewise survival cure rate model for spatially clustered data. Spat Spatiotemporal Epidemiol 2019; 29:149-159. [PMID: 31128624 DOI: 10.1016/j.sste.2019.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Revised: 12/21/2018] [Accepted: 12/31/2018] [Indexed: 11/30/2022]
Abstract
This paper proposes a Bayesian hierarchical cure rate survival model for spatially clustered time to event data. We consider a mixture cure rate model with covariates and a flexible (semi)parametric baseline survival distribution for uncured individuals. The spatial correlation structure is introduced in the form of frailties which follow a Multivariate Conditionally Autoregressive distribution on a pre-specified map. We obtain the usual posterior estimates, smoothed by regional level maps of spatial frailties and cure rates. A simulation study demonstrates that the parameters of the models with spatially correlated frailties have smaller relative biases and MSE than the ones obtained using simple frailty models. We apply our methodology to Hodgkin lymphoma cancer survival times for patients diagnosed in the state of Connecticut.
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Affiliation(s)
- Sandra M Hurtado Rúa
- Department of Mathematics and Statistics, Cleveland State University, RT 1510, 2121 Euclid Ave, Cleveland, OH 44115, USA.
| | - Dipak K Dey
- Department of Statistics, University of Connecticut, Unit 4120, 215 Glenbrook Road, Storrs, CT 06269, USA.
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Santos DDS, Cancho V, Rodrigues J. Hypothesis testing for the dispersion parameter of the hyper-Poisson regression model. J STAT COMPUT SIM 2019. [DOI: 10.1080/00949655.2019.1572144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Daiane de Souza Santos
- Department of Applied Mathematics and Statistics, Universidade de São Paulo, São Carlos, Brazil
- Department of Statistics, Universidade Federal de São Carlos, São Carlos, Brazil
| | - Vicente Cancho
- Department of Applied Mathematics and Statistics, Universidade de São Paulo, São Carlos, Brazil
| | - Josemar Rodrigues
- Department of Applied Mathematics and Statistics, Universidade de São Paulo, São Carlos, Brazil
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