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Survival analysis with XLSTAT | All methods (Non-parametric, semi-parametric and parametric)

Need to understand the usage context, the approach and the key concepts that will allow you to perform and interpret life cycle analyses? This course covers the key steps from the presentation of the censored data to the different modeling methods with XLSTAT.


4 Tage




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This course is intended for people who want to perform survival analysis. It is a complete presentation that allows participants to understand the usage context and approach to follow for all of these techniques. They will have the keys to perform and interpret life cycle analyses, from the presentation of censored data to the different modeling methods: Non-Parametric (Kaplan-Meyer and actuarial), Semi-parametric (Cox Models), Parametric (Weibull and exponential laws).

Main topics covered in this training:

  • Censored Data:
    • To the right 
    • To the left 
    • By intervals
    • Type I
  • Truncation 
  • Events
  • Delays and concept of time in survival data 
  • Non-parametric approach, survival curve:
    • Kaplan-Meyer method
    • Actuarial method
    • Log Rank hypothesis test and declinations
  • Semi-parametric statistical modeling (Cox models)
  • Covariate effects
  • Stratified analyses 
  • Time distribution
  • Constant, increasing and  decreasing hazards
  • Parametric statistical modeling (Weibull and exponential laws)

Required experience:

Participants must have a good knowledge of basic statistical tools: descriptive statistics, hypothesis testing, confidence intervals, p-value, alpha risk, etc.


Introduction to survival data vocabulary

  • Dates and durations of follow-up (entry date, point date, last date, setback, status, etc)
  • Censorship concepts (right, left, interval, non-informative, random)
  • Survival estimation: survival function, instantaneous hazard function, cumulative hazard function 

Survival estimation

  • Non-parametric estimator of survival: Kaplan-Meyer estimator
  • Non-parametric estimator of cumulative hazard: Nelson - Aalen estimator
  • Actuarial estimation: time scale set by the user
  • Comparison of survival by groups: Log-Rank test
  • The concept of relative risk 
  • Graphical representation of survival and cumulative risk curves:
    • Representation
    • Interpretation

Semi-parametric modeling

  • The Cox model: context of use of this type of model
  • Hypothesis of proportional hazards
  • Managing simultaneous events
  • Effects of variables
  • Diagnostic measures on co-variables
  • Suitability of the model
  • Diagnostic measures for the assumption on proportional hazards
  • Coding the co-variables
  • Time-dependent variables
  • Stratification

Parametric Modeling 

  • A priori distribution of lifetimes
  • Accelerated models 
  • Exponential model
  • The Weibull model
  • Other models and usage conditions 
  • Model estimation: maximum likelihood
  • Hypothesis testing and modeling quality
  • Left and time-dependent censors
  • Predictions
  • Interpretation of software outputs

Profil des Schulungsleiters

Thierry Anthouard

Statistics instructor


Thierry Anthouard is the head of the Arkesys Group's statistical training program and has always been passionate about the field of statistics. In 1992, he launched the development of the Arkesys Group's statistics training program. His "by example" pedagogical approach  allows him to popularize statistics and to make it accessible to all learners. As a consultant supporting of key accounts, he adapts to all types of contexts and learning issues. 

Jérôme-Philippe Garsi

Statistics instructor


Jérôme-Philippe Garsi is a statistical instructor with 13 years of experience in the training field. Since his doctorate on clinical issues, his work is mainly focused on the interest of populations, their health and well-being. At ease with any audience, he makes pedagogy and the simplification of scientific knowledge a priority. To do so, he always takes the greatest care to be clear in his written documents as well as in his oral presentations.