Inhalt

[ 951STMOSANK14 ] KV (*)Survival Analysis

Versionsauswahl
(*) Leider ist diese Information in Deutsch nicht verfügbar.
Workload Ausbildungslevel Studienfachbereich VerantwortlicheR Semesterstunden Anbietende Uni
4 ECTS M2 - Master 2. Jahr Statistik Helga Wagner 2 SSt Johannes Kepler Universität Linz
Detailinformationen
Quellcurriculum Masterstudium Statistics and Data Science 2025W
Lernergebnisse
Kompetenzen
(*)Students are able to analyse survival data, to interprete the results correctly and perform a residual analysis of the fitted models
Fertigkeiten Kenntnisse
(*)
  • Knowing and understanding of the basic problems, concepts and methods of survival analysis (k1,k2)
  • Knowing and understanding of the basic problems, concepts and methods of multistate and competing risk models (k1,k2)
  • Fitting, model choice and residual analysis of regression models for survival and competing risk data with statistic software R (k3)
  • Implementing and performing simulation studies for survival data (k2,k3)
(*)
  • Basic concepts for survival times (Survival function, hazard, cumulative hazard) and their relations
  • Missing and incomplete information in survival data (Censoring, truncation)
  • Estimation of the survival function (Kaplan-Meier estimator, Nelson-Aalen estimator)
  • Log-Rank Test
  • Regression models for Survival Times: Accelerated failure time model and proportional hazards model
  • Residual analysis for Cox-PH models
  • Time dependent covariates and time-varying effects
  • Competing risk and multistate models
  • Cumulative incidence function

Beurteilungskriterien (*)Exam
Project report
Lehrmethoden (*)Lecture
Computer Lab
Abhaltungssprache Englisch
Literatur (*)Broström G. (2012). Event History Analysis, Taylor & Francis

Hosmer D. W. and Lemeshow S. (2003). Applied Survival Analysis, Wiley

Klein J. P. and Moeschberger M. L. (1997). Survival Analysis, Springer

Lehrinhalte wechselnd? Nein
Äquivalenzen (*)in collaboration with 951STCOCSTK14: KV Computational Statistics (4 ECTS) equivalent to
4MSCVDPR: PR Computerintensive Verfahren in der Datenanalyse (6 ECTS)
Präsenzlehrveranstaltung
Teilungsziffer 40
Zuteilungsverfahren Zuteilung nach Vorrangzahl