Research map / Probabilistic and scientific modeling / Molecules and proteins
Survival Permanental Processes for Survival Analysis with Time-Varying Covariates
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use denser covariate measurement points (Ju=20 instead of Ju=5) | Survival models on log-linear synthetic data · Synthetic lambda_lin | TLL | Helped |
| Use SurvPP instead of CoxPH on the nonlinear hazard data | SurvPP · Synthetic lambda_non | TLL and AUC | Helped |
| Use CoxPH instead of SurvPP on the log-linear hazard data | CoxPH · Synthetic lambda_lin | TLL | No clear effect |
| Use SurvPP instead of GBM on the nonlinear hazard data | SurvPP · Synthetic lambda_non | TLL and AUC | No clear effect |
| Use SurvPP instead of GBM for hazard estimation speed at Ju=50 | SurvPP · Synthetic lambda_non | CPU time | Helped |
| Use RFM instead of SurvPP on nonlinear data | RFM · Synthetic lambda_non | TLL | Hurt |
| Use SurvPP instead of CoxPH on SANAD | SurvPP · SANAD | predictive performance | Helped |
| Use CoxPH instead of nonparametric models on PBC | CoxPH · PBC | AUC: AUC > 0.9 | No clear effect |
| Add Simpson's 1/3 rule integration instead of Riemann sum for A (ablation) | SurvPP · Synthetic lambda_non | TLL | Helped |
| Use smaller kernel parameter search range (0.01-0.09 instead of 0.1-10) (ablation) | SurvPP · PBC | AUC | Helped |
| Use independent validation sets instead of cross-validation | All compared models · Synthetic lambda_lin and lambda_non | TLL and AUC | No clear effect |
| Scale training events from 818 to 81066 | SurvPP · Synthetic lambda_non | CPU time: 818 -> 81066 | Mixed |
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