# Variable Selection in Leading Sets for Survival Models under the S3VS Framework `VS_method` applies the chosen variable-selection algorithm for survival models to each leading set produced by S3VS at every iteration. ```r VS_method_SURV(y, X, surv_model, vsel_method, alpha = 0.5, p_thresh = 0.1, gamma = 0.9, verbose = FALSE, ...) ``` ## Arguments - `y`: Response. A list with components `time` and `status` (1 = event, 0 = censored). - `X`: Predictor matrix. Can be a base matrix or something `as.matrix()` can coerce. No missing values are allowed. - `surv_model`: Character string specifying the survival model. Must be explicitly provided; there is no default. Values are `"Cox"` for proportional hazards models, `"AFT"` for accelerated failure time models. - `vsel_method`: Character string indicating the variable-selection engine used at each iteration. Available options are `"COXGLMNET"` for `surv_model = "COX"` and `"AFTREG"`, `"AFTGEE"`, `"BRIDGE"`, and `"PVAFT"` for `surv_model = "AFT"`. See Details for more information. - `alpha`: Only used when `vsel_method == "ENET"`. Elastic net mixing parameter, with $\alpha \in (0,1)$. - `p_thresh`: Only used with `vsel_method = "AFTGEE"`. p-value threshold for variable selection. - `gamma`: Only used with `vsel_method = "BRIDGE"`. Numeric scalar (default `0.5`) giving the exponent in the bridge penalty. Must be $> 0$. See details. - `verbose`: If `TRUE`, some information is printed. `FALSE`, by default. - `...`: Other arguments to be passed inside `eha::aftreg()` when `surv_model = "AFT"` and `vsel_method = "AFTREG"` ## Returns A list containing: - **sel**: Character vector with names of the selected predictors. - **nosel**: Character vector with names of the predictors not selected. ## Author(s) Nilotpal Sanyal , Padmore N. Prempeh ## See Also `cv.glmnet`, `aftreg`, `aftgee`, bridge_aft , pvaft ## Examples ```r # Simulate survival data (Cox) set.seed(123) n <- 100 p <- 150 X <- matrix(rnorm(n * p), n, p) colnames(X) <- paste0("V", 1:p) eta <- X[,1] + 0.5 * X[,2] base_rate <- 0.05 T_event <- rexp(n, rate = base_rate * exp(eta)) C <- rexp(n, rate = 0.03) time <- pmin(T_event, C) status <- as.integer(T_event <= C) y_surv <- list(time = time, status = status) # Run VS_method VS_method_SURV(y_surv, X, surv_model = "COX", vsel_method = "COXGLMNET", verbose = FALSE) ```

VS_method_SURV function