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I am very new to survival analysis. I am looking for differences between these two methods - Kaplan-Meier(K-M) vs. Cox Regression.

  1. KM Survival Analysis cannot use multiple predictors, whereas Cox Regression can.
  2. KM Survival Analysis can run only on a single binary predictor, whereas Cox Regression can use both continuous and binary predictors.
  3. KM is a non-parametric procedure, whereas Cox Regression is a semi-parametric procedure.

Please check the above points. If incomplete or incorrect, please suggest the changes.

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I generally try to use KM as a descriptive statistic and Cox regressions for anything related to my hypothesis. Regarding your questions:

  1. You could in theory split your KM analysis into any number of subgroups, the only limit is the size of your data. It is a rather inefficient method but you can show KM Survival curves for women with diabetes, women wihtout diabetes, men with diabetes and men without diabetes.
  2. Correct. You can though categorize your continuous variables and thus have a categorical variable that you can use for splitting. Note that binary is not necessary, you need a categorical variable.
  3. Correct. I was in the beginning scared of parametric procedures as they rely on assumptions that I found hard to test and I liked the clean non-parametric tests. Unfortunately it turns out that the non-parametric tests, while being mathematically robust, are often hard to interpret. E.g. a low p-value often only indicates that the groups are not the same but it conveys little about in what way they aren't the same.
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