Kaplan-Meier Survival Analysis (in 30 seconds)


A simple overview in four steps

  1. This is what it they look like:


  1. Good for:
    • Demonstrating differing survival (or event free survival) curves given different baseline characteristics. Example: to compare survival between patients on a new drug ( blue above ) with no drug ( red above ) or survival in people with one disease vs no disease (or a different stage of disease).
    • Takes into account different lengths of follow up and loss to weekly buy viagra using paypal follow up.
    • Easy for medics to understand (we are simple people) as it is a nice visual way of showing the data.
  2. Not good for:
    • It doesn’t control for confounding factors (if one group has different demographics than another) – if this is the case perhaps consider a Cox Regression analysis.
  3. This is how to do it in SPSS and R:
    • http://www.youtube.com/watch?v=YQ9538U8cPc&feature=plcp

Written by: Dr A.P. McGovern