spsurv: an R package for semi-parametric survival analysis
Software development innovations and advances in computing have enabled more complex and less costly computations in medical research (survival analysis), engineering studies (reliability analysis), and social sciences event analysis (historical analysis). As a result, many semi-parametric modeling...
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| Tipo de recurso: | tesis de maestría |
| Estado: | Versión publicada |
| Fecha de publicación: | 2020 |
| País: | Brasil |
| Institución: | Universidade Federal de Minas Gerais (UFMG) |
| Repositorio: | Repositório Institucional da UFMG |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.ufmg.br:1843/35581 |
| Acceso en línea: | http://hdl.handle.net/1843/35581 |
| Access Level: | acceso abierto |
| Palabra clave: | Proportional hazards Proportional odds Accelerated failure time Bernstein polynomial Estatística - Teses Análise de sobrevivência (Biometria) - Teses Polinômio de Bernstein - Teses Analise do tempo de falha - Teses |
| Sumario: | Software development innovations and advances in computing have enabled more complex and less costly computations in medical research (survival analysis), engineering studies (reliability analysis), and social sciences event analysis (historical analysis). As a result, many semi-parametric modeling efforts emerged when it comes to time-to-event data analysis. In this context, this work presents a flexible Bernstein polynomial (BP) based framework for survival data modeling. This innovative approach is applied to existing families of models such as proportional hazards (PH), proportional odds (PO), and accelerated failure time (AFT) models to estimate unknown baseline functions. Along with this contribution, this work also presents new automated routines in R, taking advantage of algorithms available in Stan. The proposed computation routines are tested and explored through simulation studies based on artificial datasets. The tools implemented to fit the proposed statistical models are combined and organized in an R package. Also, the BP based proportional hazards (BPPH), proportional odds (BPPO), and accelerated failure time (BPAFT) models are illustrated in real applications related to cancer trial data using maximum likelihood (ML) estimation and Markov chain Monte Carlo (MCMC) methods. |
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