Un modelo de riesgos competitivos de tiempo discreto basado en regresión por splines e información longitudinal
ilustraciones, gráficas
- Autores:
-
Salazar García, Adriana Marcela
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/80516
- Palabra clave:
- 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas
Análisis de Supervivencia
Survival Analysis
Numerical analysis
Logistic regression analysis
Análisis numérico
Análisis de regresión logística
Modelo Conjunto
Modelo de supervivencia
Modelo Longitudinal
Regresión Logística
Modelo de Cox
Joint model
Survival model
Longitudinal model
Logistic regression
Cox model
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Un modelo de riesgos competitivos de tiempo discreto basado en regresión por splines e información longitudinal |
| dc.title.translated.eng.fl_str_mv |
A model of competitive risks in discrete time based on the spline regression and longitudinal information |
| title |
Un modelo de riesgos competitivos de tiempo discreto basado en regresión por splines e información longitudinal |
| spellingShingle |
Un modelo de riesgos competitivos de tiempo discreto basado en regresión por splines e información longitudinal 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas Análisis de Supervivencia Survival Analysis Numerical analysis Logistic regression analysis Análisis numérico Análisis de regresión logística Modelo Conjunto Modelo de supervivencia Modelo Longitudinal Regresión Logística Modelo de Cox Joint model Survival model Longitudinal model Logistic regression Cox model |
| title_short |
Un modelo de riesgos competitivos de tiempo discreto basado en regresión por splines e información longitudinal |
| title_full |
Un modelo de riesgos competitivos de tiempo discreto basado en regresión por splines e información longitudinal |
| title_fullStr |
Un modelo de riesgos competitivos de tiempo discreto basado en regresión por splines e información longitudinal |
| title_full_unstemmed |
Un modelo de riesgos competitivos de tiempo discreto basado en regresión por splines e información longitudinal |
| title_sort |
Un modelo de riesgos competitivos de tiempo discreto basado en regresión por splines e información longitudinal |
| dc.creator.fl_str_mv |
Salazar García, Adriana Marcela |
| dc.contributor.advisor.none.fl_str_mv |
Huertas Campos, Jaime Abel |
| dc.contributor.author.none.fl_str_mv |
Salazar García, Adriana Marcela |
| dc.subject.ddc.spa.fl_str_mv |
510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas |
| topic |
510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas Análisis de Supervivencia Survival Analysis Numerical analysis Logistic regression analysis Análisis numérico Análisis de regresión logística Modelo Conjunto Modelo de supervivencia Modelo Longitudinal Regresión Logística Modelo de Cox Joint model Survival model Longitudinal model Logistic regression Cox model |
| dc.subject.decs.spa.fl_str_mv |
Análisis de Supervivencia |
| dc.subject.decs.eng.fl_str_mv |
Survival Analysis |
| dc.subject.lemb.eng.fl_str_mv |
Numerical analysis Logistic regression analysis |
| dc.subject.lemb.spa.fl_str_mv |
Análisis numérico Análisis de regresión logística |
| dc.subject.proposal.spa.fl_str_mv |
Modelo Conjunto Modelo de supervivencia Modelo Longitudinal Regresión Logística Modelo de Cox |
| dc.subject.proposal.eng.fl_str_mv |
Joint model Survival model Longitudinal model Logistic regression Cox model |
| description |
ilustraciones, gráficas |
| publishDate |
2021 |
| dc.date.accessioned.none.fl_str_mv |
2021-10-12T17:13:33Z |
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2021-10-12T17:13:33Z |
| dc.date.issued.none.fl_str_mv |
2021-10 |
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Trabajo de grado - Maestría |
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info:eu-repo/semantics/masterThesis |
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info:eu-repo/semantics/acceptedVersion |
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Text |
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https://repositorio.unal.edu.co/handle/unal/80516 |
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Universidad Nacional de Colombia |
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Repositorio Institucional Universidad Nacional de Colombia |
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https://repositorio.unal.edu.co/ |
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https://repositorio.unal.edu.co/handle/unal/80516 https://repositorio.unal.edu.co/ |
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Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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spa |
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spa |
| dc.relation.references.spa.fl_str_mv |
Annesi, I., Moreau, T. and Lellouch, J. (1989). Efficiency of the logistic regression and cox proportional hazards models in longitudinal studies. Statistics in Medicine, 8, 1515 - 1521. Begg, C. B. and Gray, R. (1984). Calculation of polychotomous logistic regression parameters using individualized regressions. Biometrika, 71(1), 11-18. Bowman, A. W. and Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis. The Kernel Approach with S-Plus Illustrations. Oxford Statistical Science Series. Cox, D. R. (1972). Regression Models and Life Tables (with discussion). Journal of the Royal Statistical Society, Series B ; 34, 187-220. Dobson, A. J. (1945). An introduction to generalized linear models. 2nd ed. Chapman hall/CRC Elandt-Johnson, R. C. (1980). Time dependent logistic models in follow-up studies and clinical trials, I. Binary data. Institute of Statistics Mimeo Series, University of North Carolina 1310. Elashoff, R. M., Li, G. and Li, N. (2007). An approach to joint analysis of longitudinal measurements and competing risks failure time data. Statistics in Medicine 26, 2813 - 2835. Elashoff, R. M., Li, G. and Li, N. (2008). A Joint Model for Longitudinal Measurements and Survival Data in the Presence of Multiple Failure Types. Biometrics 64, 762 - 771. Green, M. S. and Symon, M. J. (1983). A comparison of the logistic risk function and the proportional hazards model in prospective epidemiologic studies. Journal of Clinical Epidemiology, 36, 715-723. Guo, X. and Carlin, B. P. (2004). Separate and Joint Modeling of Longitudinal and Event Time Data Using Standard Computer Packages. The American Statistician, 58, 16 - 24. Henderson, R., Diggle, P. and Dobson, A. (2000). Joint Modelling of Longitudinal Measurements and Event Time Data. Biostatistics, 4, 465 - 480. Hougaard, P. (2000). Analysis of Multivariate Survival Data. Springer-Verlag New York. Klein, J. P. and Moeschberger, M. L. (2003). Survival Analysis. Techniques for Censored and Truncated Data. 2nd ed. New York: Springer-Verlag. Klein, J. P. and Moeschberger, M. L. (2005). Survival Analysis: Techniques for Censored and Truncated Data. 3rd ed. Springer. Kleinbaum, D. G. and Klein, M. (2002). Logistic Regression. 2nd ed. Springer. Lawless, J. F. (2003). Statistical Models and Methods for Lifetime Data, 2nd ed. Hobo- ken:Wiley. Li, N., Elashoff, R.M. and Li, G.(2009). Robust joint modeling of longitudinal measurements and competing risks failure time data, Biometrical Journal, 51, 19-30. Lin H., McCulloch, C.E. and Mayne, S.T. (2002). Maximum Likelihood Estimation in the Joint Analysis of Time-to-Event and Multiple Longitudinal Variables. Statistics in Medicine, 21, 2369-82. Liu, L. and Huang, X. (2009). Joint Analysis of Correlated Repeated Measures and Recurrent Events Processes in the Presence of Death, with Application to a Study on Acquired Immune Deficiency Syndrome. Journal of the Royal Statistical Society, 58, 65-81. Luo, S., Kong, X. and Nie, T. (2016). Spline based survival model for credit risk modeling. European Journal of Operational Research, 253, 257 - 269. Myers, M. H., Hankey, B. F. and Mantel, N. A. (1973). Logistic-exponential model for use with response time data involving regression variables. Biometrics, 29, 257-296. SAS Institute. (2005). SAS/STAT 9.1. Cary, North Caroline, US: SAS Institute Inc. Teixeira, L., Sousa, I., Rodriguez, A. and Mendonca, D. (2019). Joint Modelling of Longitudinal and competing risk data in clinical research. REVSTAT - Statistical Journal, 17, 245 - 264. Therneau, T. M. and Grambsch, P. M. (2000). Modeling Survival Data: Extending the Cox Model. New York: Springer-Verlag. Tsiatis, A. A. and Davidian, M. (2004). Joint Modeling of Longitudinal and Timeto- Event Data: An Overview. Statistica Sinica, 14, 809 - 834. Verbeke, G. and Molenberghs, G. (2000). Linear Mixed Models for Longitudinal Data. New York: Springer-Verlag . Williamson, P. R., Kolamunnage - Dona, R., Philipson, P. and Marson, A. G. (2008). Joint modelling of longitudinal and competing risks data. Statistics in Medicine 27, 6426 - 6438. Wu, M. and Carroll, R. (1988). Estimation and Comparison of Changes in the Presence of Informative Right Censoring by Modelling the Censoring Process. Biometrics, 44, 175 - 188. Wulfsohn, M. and Tsiatis, A.A. (1997). A Joint Model for Survival and Longitudinal Data Measured with Error. Biometrics, 53, 330 - 339. Zeng, D. and Cai, J. (2005). Asymptotic Results for Maximum Likelihood Estimators in Joint Analysis of Repeated Measurements and Survival Time. The Annals of Statistics, 33, 2132{2163. |
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iv, 26 páginas |
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Universidad Nacional de Colombia |
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Bogotá - Ciencias - Maestría en Ciencias - Estadística |
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Departamento de Estadística |
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Facultad de Ciencias |
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Bogotá, Colombia |
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Universidad Nacional de Colombia - Sede Bogotá |
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Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Huertas Campos, Jaime Abel159cdc035ba0331904a23be7fbdbfed1600Salazar García, Adriana Marcela794b141dc1bc48db0fcd3ba0dc6048282021-10-12T17:13:33Z2021-10-12T17:13:33Z2021-10https://repositorio.unal.edu.co/handle/unal/80516Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, gráficasModelar datos de supervivencia de riesgos en competencia con covariantes basales y longitudinales, no es apropiado hacerlo mediante modelos clásicos de supervivencias. Para este propósito, Begg y Gray (1984) incluyen directamente la información longitudinal dentro de un modelo logístico, y Luo et al. (2016), mejoran la bondad de ajuste de la función de riesgo de dicha propuesta, con la inclusión de un spline dependiente del tiempo al evento. En el presente trabajo se propone una extensión al modelo anterior mediante la modelación conjunta, donde el proceso de supervivencia corrige sesgos en el modelo longitudinal por causa de retiros informativos, y el modelo longitudinal estimado apropiadamente, se incluye en el modelo logístico para servir como marcador al evento de interés. Ilustramos la aplicación de la propuesta con una base crediticia de datos. (Texto tomado de la fuente).Modeling survival data of competing risk with baseline and longitudinal covariates, it is not appropriate to do so using classical survival models. For this purpose, Begg and Gray (1984) directly include longitudinal information within a logistic model, and Luo et al. (2016), improve the goodness of fit of the risk function of such proposal, with the inclusion of a spline dependent on the time to the event. In the present work, an extension to the previous model is proposed through the joint modeling, where the survival process corrects biases in the longitudinal model due to informative dropout, and the appropriately estimated longitudinal model is included in the logistic model to serve as marker to the event of interest. We illustrate the application of the proposal with a data credit base.MaestríaMagíster en Ciencias - EstadísticaModelos de supervivenciaiv, 26 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias - EstadísticaDepartamento de EstadísticaFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá510 - Matemáticas::519 - Probabilidades y matemáticas aplicadasAnálisis de SupervivenciaSurvival AnalysisNumerical analysisLogistic regression analysisAnálisis numéricoAnálisis de regresión logísticaModelo ConjuntoModelo de supervivenciaModelo LongitudinalRegresión LogísticaModelo de CoxJoint modelSurvival modelLongitudinal modelLogistic regressionCox modelUn modelo de riesgos competitivos de tiempo discreto basado en regresión por splines e información longitudinalA model of competitive risks in discrete time based on the spline regression and longitudinal informationTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAnnesi, I., Moreau, T. and Lellouch, J. (1989). Efficiency of the logistic regression and cox proportional hazards models in longitudinal studies. Statistics in Medicine, 8, 1515 - 1521.Begg, C. B. and Gray, R. (1984). Calculation of polychotomous logistic regression parameters using individualized regressions. Biometrika, 71(1), 11-18.Bowman, A. W. and Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis. The Kernel Approach with S-Plus Illustrations. Oxford Statistical Science Series.Cox, D. R. (1972). Regression Models and Life Tables (with discussion). Journal of the Royal Statistical Society, Series B ; 34, 187-220.Dobson, A. J. (1945). An introduction to generalized linear models. 2nd ed. Chapman hall/CRCElandt-Johnson, R. C. (1980). Time dependent logistic models in follow-up studies and clinical trials, I. Binary data. Institute of Statistics Mimeo Series, University of North Carolina 1310.Elashoff, R. M., Li, G. and Li, N. (2007). An approach to joint analysis of longitudinal measurements and competing risks failure time data. Statistics in Medicine 26, 2813 - 2835.Elashoff, R. M., Li, G. and Li, N. (2008). A Joint Model for Longitudinal Measurements and Survival Data in the Presence of Multiple Failure Types. Biometrics 64, 762 - 771.Green, M. S. and Symon, M. J. (1983). A comparison of the logistic risk function and the proportional hazards model in prospective epidemiologic studies. Journal of Clinical Epidemiology, 36, 715-723.Guo, X. and Carlin, B. P. (2004). Separate and Joint Modeling of Longitudinal and Event Time Data Using Standard Computer Packages. The American Statistician, 58, 16 - 24.Henderson, R., Diggle, P. and Dobson, A. (2000). Joint Modelling of Longitudinal Measurements and Event Time Data. Biostatistics, 4, 465 - 480.Hougaard, P. (2000). Analysis of Multivariate Survival Data. Springer-Verlag New York.Klein, J. P. and Moeschberger, M. L. (2003). Survival Analysis. Techniques for Censored and Truncated Data. 2nd ed. New York: Springer-Verlag.Klein, J. P. and Moeschberger, M. L. (2005). Survival Analysis: Techniques for Censored and Truncated Data. 3rd ed. Springer.Kleinbaum, D. G. and Klein, M. (2002). Logistic Regression. 2nd ed. Springer.Lawless, J. F. (2003). Statistical Models and Methods for Lifetime Data, 2nd ed. Hobo- ken:Wiley.Li, N., Elashoff, R.M. and Li, G.(2009). Robust joint modeling of longitudinal measurements and competing risks failure time data, Biometrical Journal, 51, 19-30.Lin H., McCulloch, C.E. and Mayne, S.T. (2002). Maximum Likelihood Estimation in the Joint Analysis of Time-to-Event and Multiple Longitudinal Variables. Statistics in Medicine, 21, 2369-82.Liu, L. and Huang, X. (2009). Joint Analysis of Correlated Repeated Measures and Recurrent Events Processes in the Presence of Death, with Application to a Study on Acquired Immune Deficiency Syndrome. Journal of the Royal Statistical Society, 58, 65-81.Luo, S., Kong, X. and Nie, T. (2016). Spline based survival model for credit risk modeling. European Journal of Operational Research, 253, 257 - 269.Myers, M. H., Hankey, B. F. and Mantel, N. A. (1973). Logistic-exponential model for use with response time data involving regression variables. Biometrics, 29, 257-296.SAS Institute. (2005). SAS/STAT 9.1. Cary, North Caroline, US: SAS Institute Inc.Teixeira, L., Sousa, I., Rodriguez, A. and Mendonca, D. (2019). Joint Modelling of Longitudinal and competing risk data in clinical research. REVSTAT - Statistical Journal, 17, 245 - 264.Therneau, T. M. and Grambsch, P. M. (2000). Modeling Survival Data: Extending the Cox Model. New York: Springer-Verlag.Tsiatis, A. A. and Davidian, M. (2004). Joint Modeling of Longitudinal and Timeto- Event Data: An Overview. Statistica Sinica, 14, 809 - 834.Verbeke, G. and Molenberghs, G. (2000). Linear Mixed Models for Longitudinal Data. New York: Springer-Verlag .Williamson, P. R., Kolamunnage - Dona, R., Philipson, P. and Marson, A. G. (2008). Joint modelling of longitudinal and competing risks data. Statistics in Medicine 27, 6426 - 6438.Wu, M. and Carroll, R. (1988). Estimation and Comparison of Changes in the Presence of Informative Right Censoring by Modelling the Censoring Process. Biometrics, 44, 175 - 188.Wulfsohn, M. and Tsiatis, A.A. (1997). A Joint Model for Survival and Longitudinal Data Measured with Error. Biometrics, 53, 330 - 339.Zeng, D. and Cai, J. (2005). Asymptotic Results for Maximum Likelihood Estimators in Joint Analysis of Repeated Measurements and Survival Time. The Annals of Statistics, 33, 2132{2163.Público generalLICENSElicense.txtlicense.txttext/plain; charset=utf-83964https://repositorio.unal.edu.co/bitstream/unal/80516/1/license.txtcccfe52f796b7c63423298c2d3365fc6MD51ORIGINAL10154131232021.pdf10154131232021.pdfTesis de Maestría en Ciencias - Estadísticaapplication/pdf865075https://repositorio.unal.edu.co/bitstream/unal/80516/2/10154131232021.pdf1f66cb4936bf89539bdadba1d5087e51MD52THUMBNAIL10154131232021.pdf.jpg10154131232021.pdf.jpgGenerated Thumbnailimage/jpeg4854https://repositorio.unal.edu.co/bitstream/unal/80516/3/10154131232021.pdf.jpg68038ccc74b0e18aafac4edd46e3c156MD53unal/80516oai:repositorio.unal.edu.co:unal/805162024-07-31 23:13:22.286Repositorio Institucional Universidad Nacional de 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