Externally calibrate high-dimensional Cox models
calibrate_external( object, x, time, event, x_new, time_new, event_new, pred.at, ngroup = 5 )
object: Model object fitted by hdnom::fit_*().x: Matrix of training data used for fitting the model.time: Survival time of the training data. Must be of the same length with the number of rows as x.event: Status indicator of the training data, normally 0 = alive, 1 = dead. Must be of the same length with the number of rows as x.x_new: Matrix of predictors for the external validation data.time_new: Survival time of the external validation data. Must be of the same length with the number of rows as x_new.event_new: Status indicator of the external validation data, normally 0 = alive, 1 = dead. Must be of the same length with the number of rows as x_new.pred.at: Time point at which external calibration should take place.ngroup: Number of groups to be formed for external calibration.library("survival") # Load imputed SMART data data(smart) # Use the first 1000 samples as training data # (the data used for internal validation) x <- as.matrix(smart[, -c(1, 2)])[1:1000, ] time <- smart$TEVENT[1:1000] event <- smart$EVENT[1:1000] # Take the next 1000 samples as external calibration data # In practice, usually use data collected in other studies x_new <- as.matrix(smart[, -c(1, 2)])[1001:2000, ] time_new <- smart$TEVENT[1001:2000] event_new <- smart$EVENT[1001:2000] # Fit Cox model with lasso penalty fit <- fit_lasso( x, Surv(time, event), nfolds = 5, rule = "lambda.1se", seed = 11 ) # External calibration cal.ext <- calibrate_external( fit, x, time, event, x_new, time_new, event_new, pred.at = 365 * 5, ngroup = 5 ) print(cal.ext) summary(cal.ext) plot(cal.ext, xlim = c(0.6, 1), ylim = c(0.6, 1)) # # Test fused lasso, MCP, and Snet models # data(smart) # # Use first 500 samples as training data # # (the data used for internal validation) # x <- as.matrix(smart[, -c(1, 2)])[1:500, ] # time <- smart$TEVENT[1:500] # event <- smart$EVENT[1:500] # # # Take 1000 samples as external validation data. # # In practice, usually use data collected in other studies. # x_new <- as.matrix(smart[, -c(1, 2)])[1001:2000, ] # time_new <- smart$TEVENT[1001:2000] # event_new <- smart$EVENT[1001:2000] # # flassofit <- fit_flasso(x, survival::Surv(time, event), nfolds = 5, seed = 11) # scadfit <- fit_mcp(x, survival::Surv(time, event), nfolds = 5, seed = 11) # mnetfit <- fit_snet(x, survival::Surv(time, event), nfolds = 5, seed = 11) # # cal.ext1 <- calibrate_external( # flassofit, x, time, event, # x_new, time_new, event_new, # pred.at = 365 * 5, ngroup = 5) # # cal.ext2 <- calibrate_external( # scadfit, x, time, event, # x_new, time_new, event_new, # pred.at = 365 * 5, ngroup = 5) # # cal.ext3 <- calibrate_external( # mnetfit, x, time, event, # x_new, time_new, event_new, # pred.at = 365 * 5, ngroup = 5) # # print(cal.ext1) # summary(cal.ext1) # plot(cal.ext1) # # print(cal.ext2) # summary(cal.ext2) # plot(cal.ext2) # # print(cal.ext3) # summary(cal.ext3) # plot(cal.ext3)
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