thanks for taking your time digging through my requests:
Another important points (at least for clinicians, I will get to some more biology-focused features later) would be the implementation of multivariate CoxPH models that take into account multiple variables that might affect your time-to-event endpoint.
One of those (that is though limited in terms of the model fitting accuracy, and thus should not be employed with more than 5 variables at a time) is described in the SurvivalAnalysis (analyse_multivariate function, see https://cran.r-project.org/web/packages/survivalAnalysis/vignettes/multivariate.html) package in R or again in the survminer package in R (here its a coxph function if I recall correctly).
To fit more parameters into a multivariate model (which is rarely done), one usually employs regularized CoxPH models (for example LASSO regularized models, which is implmented in the glmnet package in R, see https://glmnet.stanford.edu/articles/glmnet.html). This is not a must-have - but given that the utility of ehrapy is particularly in the dissection of big heterogeneous datasets regularized models might be an idea worthwhile considering.
Description of feature
Hi all,
thanks for taking your time digging through my requests: Another important points (at least for clinicians, I will get to some more biology-focused features later) would be the implementation of multivariate CoxPH models that take into account multiple variables that might affect your time-to-event endpoint. One of those (that is though limited in terms of the model fitting accuracy, and thus should not be employed with more than 5 variables at a time) is described in the SurvivalAnalysis (analyse_multivariate function, see https://cran.r-project.org/web/packages/survivalAnalysis/vignettes/multivariate.html) package in R or again in the survminer package in R (here its a coxph function if I recall correctly). To fit more parameters into a multivariate model (which is rarely done), one usually employs regularized CoxPH models (for example LASSO regularized models, which is implmented in the glmnet package in R, see https://glmnet.stanford.edu/articles/glmnet.html). This is not a must-have - but given that the utility of ehrapy is particularly in the dissection of big heterogeneous datasets regularized models might be an idea worthwhile considering.