Programming the statistical procedures from SAS

Kaplan-Meier(K-M) vs. Cox Regression

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Kaplan-Meier(K-M) vs. Cox Regression

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 run on 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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‎02-16-2016 01:19 PM
Super Contributor
Posts: 271

Re: Kaplan-Meier(K-M) vs. Cox Regression

[ Edited ]

KM has no predictors. It estimates the survival curve for one group only. This can of course be calculated for separate groups, but KM doesnt provide any kind of comparison between groups.

Cox regression aims to estimate rate ratios between groups. In that sense you can say Cox regression and KM complement each other very good.

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Grand Advisor
Posts: 16,875

Re: Kaplan-Meier(K-M) vs. Cox Regression

This KM plot has 3 lines.

 

https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_lifetest_se...

 

PS. This looks like homework so I'll only provide hints not full answers.

Regular Contributor
Posts: 181

Re: Kaplan-Meier(K-M) vs. Cox Regression

Thanks for your reply. Why do you always think my question is a homework? Sometimes people post questions to confirm whether their understanding is correct or not.Smiley Wink

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‎02-16-2016 01:19 PM
Super Contributor
Posts: 271

Re: Kaplan-Meier(K-M) vs. Cox Regression

[ Edited ]

KM has no predictors. It estimates the survival curve for one group only. This can of course be calculated for separate groups, but KM doesnt provide any kind of comparison between groups.

Cox regression aims to estimate rate ratios between groups. In that sense you can say Cox regression and KM complement each other very good.

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