About Tool 11
This GUI-based application streamlines the Cox proportional hazards regression of CSV datasets. Designed with simplicity in mind, it empowers researchers to:
- Load any CSV: select time, event, and covariates via intuitive GUI.
- Model outputs: generate univariate and multivariate hazard ratios, survival curves, and export results.
A super simple Python desktop app for Cox proportional hazards regression — no command-line survival analysis headaches!
About Tool 12
This GUI-based application applies an XGBoost survival model to generate nonlinear risk scores and integrates them into Cox regression alongside other predictors for advanced survival analysis.
- Nonlinear risk scoring: train XGBoost survival model on your data.
- Hybrid analysis: feed risk scores into Cox model with other covariates.
About Tool 13
This GUI-based application uses a Random Survival Forest (RSF) model to compute nonlinear risk scores and integrates them into Cox regression along with other predictors for enhanced survival modeling.
- Ensemble risk estimation: build RSF model for heterogeneous data.
- Integrated regression: combine RSF risk scores with Cox covariates.
About Tool 14
This GUI-based application leverages Gradient Boosting Survival Analysis (GBSA) to produce nonlinear risk scores, which are then used in Cox regression alongside linear predictors.
- Boosted survival modeling: train GBSA on your survival data.
- Hybrid framework: integrate GBSA scores into Cox PH analysis.
About Tool 15
This GUI-based application employs a feed-forward MultiLayer Perceptron (MLP) deep learning model to calculate nonlinear risk scores, integrating them into Cox regression with other predictors.
- Deep risk modeling: train MLP network on survival features.
- Model fusion: merge MLP-derived scores with Cox covariates.
About Tool 16
This GUI-based application uses a Residual Network (ResNet) deep learning model to generate nonlinear risk scores and combines them with traditional Cox regression predictors for comprehensive survival analysis.
- Residual learning: train ResNet architecture on your data.
- Combined insights: integrate ResNet risk outputs into Cox models.