Skip to main content
added 257 characters in body
Source Link
hlsmith
  • 156
  • 6

First of all there needs to be clarification on your use of the phrase "survival analysis". In statistics there is a particular area associated with this term: https://en.wikipedia.org/wiki/Survival_analysis These survival methods (e.g., proportional hazards regression) address time to events and include concepts like censoring of observations and competing risks.

Per your description, it seems as though you may be attempting to evaluate an outcome at a set time point (i.e. cross-sections: 30-, 60-days) and have not mentioned time to event as a point of interest prior to say 30-days. This can be achieved using the typical logistic regression or SVM models with no special additional considerations.

A larger question is whether you want to control for time of event, competing outcomes, or censoring of data. If so, survival analysis would be an option. Otherwise you seem to be examining an outcome at a fixed cross-section of time and can move forward as usual.

P.S., You also use the tag "time varying covariate", which typically can get addressed using: https://academic.oup.com/ndt/article/32/suppl_2/ii84/2989980 However, in your post it is not clear this is present or what variables need to get addressed.

First of all there needs to be clarification on your use of the phrase "survival analysis". In statistics there is a particular area associated with this term: https://en.wikipedia.org/wiki/Survival_analysis These survival methods (e.g., proportional hazards regression) address time to events and include concepts like censoring of observations and competing risks.

Per your description, it seems as though you may be attempting to evaluate an outcome at a set time point (i.e. cross-sections: 30-, 60-days) and have not mentioned time to event as a point of interest prior to say 30-days. This can be achieved using the typical logistic regression or SVM models with no special additional considerations.

A larger question is whether you want to control for time of event, competing outcomes, or censoring of data. If so, survival analysis would be an option. Otherwise you seem to be examining an outcome at a fixed cross-section of time and can move forward as usual.

First of all there needs to be clarification on your use of the phrase "survival analysis". In statistics there is a particular area associated with this term: https://en.wikipedia.org/wiki/Survival_analysis These survival methods (e.g., proportional hazards regression) address time to events and include concepts like censoring of observations and competing risks.

Per your description, it seems as though you may be attempting to evaluate an outcome at a set time point (i.e. cross-sections: 30-, 60-days) and have not mentioned time to event as a point of interest prior to say 30-days. This can be achieved using the typical logistic regression or SVM models with no special additional considerations.

A larger question is whether you want to control for time of event, competing outcomes, or censoring of data. If so, survival analysis would be an option. Otherwise you seem to be examining an outcome at a fixed cross-section of time and can move forward as usual.

P.S., You also use the tag "time varying covariate", which typically can get addressed using: https://academic.oup.com/ndt/article/32/suppl_2/ii84/2989980 However, in your post it is not clear this is present or what variables need to get addressed.

Source Link
hlsmith
  • 156
  • 6

First of all there needs to be clarification on your use of the phrase "survival analysis". In statistics there is a particular area associated with this term: https://en.wikipedia.org/wiki/Survival_analysis These survival methods (e.g., proportional hazards regression) address time to events and include concepts like censoring of observations and competing risks.

Per your description, it seems as though you may be attempting to evaluate an outcome at a set time point (i.e. cross-sections: 30-, 60-days) and have not mentioned time to event as a point of interest prior to say 30-days. This can be achieved using the typical logistic regression or SVM models with no special additional considerations.

A larger question is whether you want to control for time of event, competing outcomes, or censoring of data. If so, survival analysis would be an option. Otherwise you seem to be examining an outcome at a fixed cross-section of time and can move forward as usual.