Work

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

Year: 2022

Type: article

Abstract: 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. AVAILABILITY AND IMPLEMENTATION: CondiS is an open-source application implemented with Shiny in R, available free at: https://biostatistics.mdanderson.org/shinyapps/CondiS/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Source: Bioinformatics

DOI: 10.1093/bioinformatics/btac461

Authors: Yizhuo Wang, Christopher R Flowers, Ziyi Li, Xuelin Huang

Institution: The University of Texas MD Anderson Cancer Center

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: Cancer Prevention and Research Institute of Texas, National Institutes of Health, University of Texas MD Anderson Cancer Center, National Cancer Institute