Graduate Program

__Credit hours__

In-class work per week |
Practice per week |
Credits |
Duration |
Total |

3 |
1 |
8 |
15 weeks |
120 hours |

__Instructor__

Edwin Moises Marcos Ortega

Fábio Prataviera

__Objective__

The set of techniques and statistical models for the analysis of data whose response variable is the time

until the occurrence of an event (for example, the death of an individual) is called survival analysis.

These data are often censured, i.e. the comments are incomplete in that, for some reason it was not

possible to observe the occurrence of the event. In this sense, will be developed and applied techniques

of analysis of survival in agronomic experiments and related areas, showing the estimation techniques,

verification of adjustment of the models, analysis of waste and diagnostics, as well as inference and

obtaining the confidence intervals. The specific objectives are: (i). To study and to determine the

distribution of the times of failure; ii) compare the times of failure for different groups. (iii) study the

prognostic value of possible risk factors.

__Content__

1 Introduction 2. Basic concepts: time of failure, types of censorship, data representation of survival. 3.

Functions of interest: Function of survival, function of risk, relationship between the functions. 4. Nonparametric

methods for the analysis of data on survival: the Kaplan-Meier analysis, actuarial estimator

or ironing board of life, Nelson-Aalen estimator, the comparison of survival curves. 5. Proportional

Hazards Model: Cox regression model, pets of physical, checking the assumption of the Cox proportional

hazards and analysis of waste. 6. Parametric methods for the analysis of data on survival: basic

distributions in survival analysis (Exponential, Weibull, Log-normal, extreme value, generalized Range,

Weibull Multiple, Weibull-exponenciado), pets of parameters, confidence interval of parameters, choose

the appropriate distribution. 7. Models of regression in the analysis of survival: the logistic regression

models, exponential, Weibull widespread range, inference in logistic regression models and analysis of

waste and diagnosis. 8. Regression models with cure fraction. 9. Regression models bivariates with

censored data. 10. Regression models with random effect.

__Bibliography__

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Collet, A. Modelling Survival Data in Medical Research. Chapman and Hall, London. 2003.

Cox, D. R. e Oakes, D. Analysis of Survival Data. Chapman and Hall, London. 1984.

Hosmer, D. W. e Lemeshow, J. F. Applied Survival Analysis. John Wiley and Sons, New York. 1992.

Ibrahim, J. G., Chen, M, H., e Sinha, D. Bayesian Survival Analysis. Springer -Verlag, New York. 2001.

Kleinbaum, David G.e Mitchel Klein. Survival Analysis a Self-Learning Tex. 2005 - 2ed.Springer, USA.

Kaplan, E. L. e Meier, P. Nonparametric estimation from incomplete observations. Journal of the

American Statistical Association, 53, 457-481. 1958

Lawless, J. F. Statistical Models and Methods for Lifetime Data. John Wiley and Sons, New York. 2003.

Lee, E.T. and Wang, J. W. Statistical Methods for Survival Data Analysis. Fourth Edition. Wiley, New

York. 2013.

Moore, D. F. Applied Survival Analysis Using R. Springer. 2016.

Tableman, M. and Kim, J.S. Survival Analysis using S. Anlaysis of Time-to-Event Data. Chapmann and

Hall. New York. 2003.

Nelson, W. Accelerated Life Testing: Statistical Models, Data Analysis and Test Plans. John Wiley and

Sons, New York. 1990

Ortega, E. M. M. Análise de Influência Local e Resíduos nos Modelos de Regressão Log-gama

Generalizados. São Paulo: Tese de Doutorado. 2001.