Time-to-event data, also often referred to as survival data, arise when interest is focused on the time elapsing before an event is experienced. By events we mean occurrences that are of interest in scientific studies from various disciplines such as medicine, epidemiology, demography, biology, sociology, economics, engineering, et cetera. Examples of such events are: death, onset of infection, divorce, unemployment, and failure of a mechanical device. All of these may be subject to scientific interest where one tries to understand their cause or establish risk factors. The purpose of this course is to provide a gentle, yet intense, introduction of the most commonly used statistical methods for analyzing time-to-event data. This course covers the following topics:

- Basic concepts and quantities of interest
- Counting processes
- Nonparametric estimation and comparison of survival curves
- Parametric regression models
- Semiparametric proportional hazards regression
- Refinements of the semiparametric proportional hazards model
- Competing risks and multistate models
- Analysis of recurrent events
- Frailty models for related observations

This course comprises 4 contact hours per week (2 hours lectures, 2 hours tutorials) and is intended for students enrolled in the Master of Applied Statistics programme at the Georg August University Göttingen.

- Please use Stud.IP to sign up for this module so that we can send e-mails to all participants from there.

Lecture | Thursday | 08:30 - 10:00 | Lecture room MED 23, Humboldtallee 32 |

Tutorial | Tuesday | 08:30 - 10:00 | Lecture room MED 23, Humboldtallee 32 |

Date | Lecture/Tutorial | Topic | Material |
---|---|---|---|

October 22nd (Tuesday) | Lecture | Basic quantities and concepts | Lecture slides |

October 24th (Thursday) | Lecture | Nonparametric estimation of functions of survival time | Lecture slides, HTML file, RMD file |

November 5th (Tuesday) | Tutorial | Study Sheet 1 | Study sheet 1, R code |

November 7th (Thursday) | Lecture | Nonparametric estimation of functions of survival time (continuation) | HTML file, RMD file |

November 12th (Tuesday) | Tutorial | Study Sheet 2 | Study sheet 2, R code |

November 14th (Thursday) | Lecture | Nonparametric methods for comparing survival distributions | Lecture slides, HTML file, RMD file |

November 19th (Tuesday) | Tutorial | Study sheet 3 | Study sheet 3, melanoma.dat, Description of the melanoma data, R code |

November 21st (Thursday) | Lecture | Parametric regression models | Lecture slides, HTML file, RMD file |

November 26th (Tuesday) | Lecture | Parametric regression models (continuation) | |

November 28th (Thursday) | Tutorial | Study sheet 4 | Study sheet 4, R code |

December 3rd (Tuesday) | Tutorial | Study sheet 5 | Study sheet 5, R code |

December 5th (Thursday) | Tutorial | Study sheet 5 (continuation) | |

December 10th (Tuesday) | Lecture | Semiparametric proportional hazards regression | Lecture slides, HTML file, RMD file Reminder on shrinkage regression and cross-validation |

December 12th (Thursday) | Tutorial | Study sheet 6 | Study sheet 6 |

December 17th (Tuesday) | Lecture | Semiparametric proportional hazards regression (continuation) | |

January 7th (Tuesday) | Tutorial | Study sheet 7 | Study sheet 7 |

Package | Citation info |
---|---|

survival | Therneau, T (2015): A Package for Survival Analysis in S, R package version 2.41-3 URL: http://cran.r-project.org/web/packages/survival |

Epi | Carstensen, B., Plummer, M., Laara, E. and Hills, M. (2017): Epi: A Package for Statistical Analysis in Epidemiology, R package version 2.19 URL: http://cran.r-project.org/web/packages/Epi |

KMsurv | Data sets from Klein and Moeschberger (1997), Survival Analysis, R package version 0.1-5 URL: http://cran.r-project.org/web/packages/KMsurv |

OIsurv | Survival analysis supplement to OpenIntro guide, R package version 0.2 URL: http://cran.r-project.org/web/packages/OIsurv |

km.ci | Confidence intervals for the Kaplan-Meier estimator, R package version 0.5-2 URL: http://cran.r-project.org/web/packages/km.ci |

flexsurv | Flexible Parametric Survival and Multi-State Models, R package version 1.1 URL: https://cran.r-project.org/web/packages/flexsurv/ |

muhaz | Hazard Function Estimation in Survival Analysis, R package version 1.2.6 URL: http://cran.r-project.org/web/packages/muhaz |

eha | Event History Analysis, R package version 2.5.0 URL: http://cran.r-project.org/web/packages/eha |

penalized | L1 (Lasso and Fused Lasso) and L2 (Ridge) Penalized Estimation in GLMs and in the Cox Model, R package version 0.9-50 URL: https://cran.r-project.org/web/packages/penalized |

timereg | Flexible regression models for survival data, R package version 1.9.1 URL: http://cran.r-project.org/web/packages/timereg |

cmprsk | Subdistribution Analysis of Competing Risks, R package version 2.2-7. URL: http://cran.r-project.org/web/packages/cmprsk |

mvna | Nelson-Aalen Estimator of the Cumulative Hazard in Multistate Models, R package version 2.0.1 URL: http://cran.r-project.org/web/packages/mvna |

mstate | Data preparation, estimation and prediction in multi-state models, R package version 0.2.10 URL: http://cran.r-project.org/web/packages/mstate |

etm | Empirical Transition Matrix, R package version 0.6-2 URL: http://cran.r-project.org/web/packages/etm |

compeir | Event-specific incidence rates for competing risks data, R package version 1.0 URL: http://cran.r-project.org/web/packages/compeir |

survrec | Survival analysis for recurrent event data, R package version 1.2-2 URL: http://cran.r-project.org/web/packages/survrec |

frailtypack | General Frailty Models: Shared, Joint and Nested Frailty Models with Prediction, R package version 2.12.6 URL: http://cran.r-project.org/web/packages/frailtypack |

- The R Project for Statistical Computing
- Companion website of the book Competing Risks and Multistate Models with R

- Aalen, O. O., Borgan, O. and Gjessing, H. K. (2008):
*Survival and Event History Analysis: A Process Point of View*, Springer. - Beyersmann, J., Schumacher, M. and Allignol, A. (2012):
*Competing Risks and Multistate Models with R*, Springer. -
Collett, D. (2015):
*Modelling Survival Data in Medical Research*, 3rd edition, Chapman & Hall/CRC. - Hosmer Jr., D. W., Lemeshow, S. and May, S. (2008):
*Applied Survival Analysis: Regression Modelling of Time to Event Data,*2nd edition, Wiley. - Hougaard, P. (2000):
*Analysis of Multivariate Survival Data*, Springer. - Klein, J. P. and Moeschberger, M. L. (2003):
*Survival Analysis: Techniques for Censored and Truncated Data*, 2nd edition, Springer. - Lee, E. T. and Wang, J. W. (2013):
*Statistical Methods for Survival Data Analysis*, 4th edition, Wiley. - Moore, D. F. (2016):
*Applied Survival Analysis Using R*, Springer.

Written exam (duration: 90 minutes). The exam will take place on Friday, February 14th 2020, between 10:15 a.m. and 11:45 a.m. in the departmental library, Department of Medical Statistics, Humboldtallee 32. The permitted examination aids are:

- Unannotated lecture slides;
- One handwritten DIN A4 sheet (one-sided);
- Pocket calculator;
- English dictionary (without personal comments).