Course detail

LCE5809 - Survival Analysis

Credit hours

In-class work
per week
per week
15 weeks
120 hours

Edwin Moises Marcos Ortega
Fábio Prataviera

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.

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.

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