# Screening Predictors As "Leading Variables" By Evaluating Predictor-Response Associations In Survival Models `get_leadvars_SURV` screens some predictors as "leading variables" based on predictor-response associations in survival models. ```r get_leadvars_SURV(y, X, surv_model = c("AFT", "COX"), method = c("topk", "fixedmuthresh", "percmuthresh"), param, varsselected = NULL, varsleft = colnames(X), parallel = 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. - `method`: Screening rule, one of `c("topk", "fixedthresh", "percthresh")`. The association measure is marginal utility. `"topk"` keeps the predictors with the largest $k$ association values; `"fixedthresh"` keeps predictors whose association is greater than or equal to a specified threshold; `"percthresh"` keeps predictors whose association is within a given percentage of the best. - `param`: Tuning parameter for `method`. If `"topk"`, supply an integer $k$ (keep the top $k$). If `"fixedthresh"`, supply a numeric threshold (keep predictors with association $\ge$ threshold). If `"percthresh"`, supply a percentage in $(0,100]$ (keep predictors with association $\ge$ that percent of the highest association). - `varsselected`: A character vector containing the predictors that are already selected in previous iterations. The association measure, conditional utility, is computed controling for these predictors. `NULL`, by default. - `varsleft`: A character vector containing the predictors that are neither selected, nor removed from consideration in previous iterations. Leading predictors are chosen from these predictors. - `parallel`: Logical. If `TRUE`, attempts to perform some computations in parallel mode in `binomial` and `survival` families, which is strongly recommended for faster execution. Defaults to `colnames(X)`. ## Returns A character vector containing the names of the leading varibales. ## Author(s) Nilotpal Sanyal , Padmore N. Prempeh ## 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) # Select leading variables leadvars <- get_leadvars_SURV(y = y_surv, X = X, surv_model = "COX", method = "topk", param = list(k=2), varsselected = NULL, varsleft = colnames(X)) leadvars ```

get_leadvars_SURV function