TY  - JOUR
AU  - Pak, Kyoungjune
AU  - Oh, Sae-Ock
AU  - Goh, Tae Sik
AU  - Heo, Hye Jin
AU  - Han, Myoung-Eun
AU  - Jeong, Dae Cheon
AU  - Lee, Chi-Seung
AU  - Sun, Hokeun
AU  - Kang, Junho
AU  - Choi, Suji
AU  - Lee, Soohwan
AU  - Kwon, Eun Jung
AU  - Kang, Ji Wan
AU  - Kim, Yun Hak
PY  - 2020
DA  - 2020/5/5
TI  - A User-Friendly, Web-Based Integrative Tool (ESurv) for Survival Analysis: Development and Validation Study
JO  - J Med Internet Res
SP  - e16084
VL  - 22
IS  - 5
KW  - survival analysis
KW  - grouped variable selection
KW  - The Cancer Genome Atlas
KW  - web-based tool
KW  - user service
AB  - Background: Prognostic genes or gene signatures have been widely used to predict patient survival and aid in making decisions pertaining to therapeutic actions. Although some web-based survival analysis tools have been developed, they have several limitations. Objective: Taking these limitations into account, we developed ESurv (Easy, Effective, and Excellent Survival analysis tool), a web-based tool that can perform advanced survival analyses using user-derived data or data from The Cancer Genome Atlas (TCGA). Users can conduct univariate analyses and grouped variable selections using multiomics data from TCGA. Methods: We used R to code survival analyses based on multiomics data from TCGA. To perform these analyses, we excluded patients and genes that had insufficient information. Clinical variables were classified as 0 and 1 when there were two categories (for example, chemotherapy: no or yes), and dummy variables were used where features had 3 or more outcomes (for example, with respect to laterality: right, left, or bilateral). Results: Through univariate analyses, ESurv can identify the prognostic significance for single genes using the survival curve (median or optimal cutoff), area under the curve (AUC) with C statistics, and receiver operating characteristics (ROC). Users can obtain prognostic variable signatures based on multiomics data from clinical variables or grouped variable selections (lasso, elastic net regularization, and network-regularized high-dimensional Cox-regression) and select the same outputs as above. In addition, users can create custom gene signatures for specific cancers using various genes of interest. One of the most important functions of ESurv is that users can perform all survival analyses using their own data. Conclusions: Using advanced statistical techniques suitable for high-dimensional data, including genetic data, and integrated survival analysis, ESurv overcomes the limitations of previous web-based tools and will help biomedical researchers easily perform complex survival analyses. 
SN  - 1438-8871
UR  - https://www.jmir.org/2020/5/e16084
UR  - https://doi.org/10.2196/16084
UR  - http://www.ncbi.nlm.nih.gov/pubmed/32369034
DO  - 10.2196/16084
ID  - info:doi/10.2196/16084
ER  -