Cox Model
Online Calculator
Perform a survival multivariable analysis
without any statistical knowledge
A better way to perform your statistics
COX Model
with no effort
Just pick the variables that you want to study and EasyMedStat will calculate the results of your COX model. There’s no need to code and no statistical knowledge required.
Automated methodology check
To perform a COX model, your data must follow some conditions. We will check everything automatically for you: missing data, extreme values, multicollinearity, etc… all the things you do not want to waste time on.
Generated texts to be included in your article
What should you write in the methods and results section of your articles? No need to worry. We generate all that text for you automatically!
What is a COX model?
The Cox proportional-hazards model is a regression model used in medical research for assessing the association between the survival of patients and one or more predictor variables.
Why is it necessary to perform a multiple regression?
A disease is hardly predictable by only one factor. Many variables may influence the occurrence of a disease: genetics, environmental exposure, way of life, tobacco use, etc. If you want to take into account all the parameters concomitantly to explain the disease you are studying, simple statistical analyses will not be sufficient. You will need to perform a multiple regression. In a multiple regression, we study the simultaneous effect of several predictors on one variable we want to predict.
Multivariable or multivariate analysis?
Both terms are often used interchangeably in the medical domain. However, they are truly different and the difference is easy to understand. A multiple (multivariable) regression is the method used to model one variable according to several other variables. For example, modeling the 5-year survival of a patient according to age, BMI, disease stage, etc. Multivariate analysis also models the relation between variables. However, the outcome you want to model is measured for the same individual at multiple time points (repeated measures). So instead of one variable to predict (multivariable analysis or multiple regression), you actually have multiple variables to predict (multivariate analysis).
