CondiS web app: Imputation of censored lifetimes for machine learning-based survival analysis

Summary: In the era of big data, machine learning techniques are widely applied to every area in biomedical research including survival analysis. It is well recognized that censoring, which is a common missing issue in survival time data, hampers the direct usage of these machine learning techniques. Here, we present CondiS, a web toolkit with graphical user interface to help impute the survival times for censored observations and predict the survival times for future enrolled patients. CondiS imputes a censored survival time based on its distribution conditional on its observed part. When covariates are available, CondiS-X incorporates this information to further increase the imputation accuracy. Users can also upload data of newly enrolled patients and predict their survival times. As the first web-App tool with an imputation function for censored lifetime data, CondiS web can facilitate conducting survival analysis with machine learning approaches.