Externally validate high-dimensional Cox models with time-dependent AUC
validate_external( object, x, time, event, x_new, time_new, event_new, tauc.type = c("CD", "SZ", "UNO"), tauc.time )
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.tauc.type: Type of time-dependent AUC. Including "CD" proposed by Chambless and Diao (2006)., "SZ" proposed by Song and Zhou (2008)., "UNO" proposed by Uno et al. (2007).tauc.time: Numeric vector. Time points at which to evaluate the time-dependent AUC.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 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] # Fit Cox model with lasso penalty fit <- fit_lasso( x, survival::Surv(time, event), nfolds = 5, rule = "lambda.1se", seed = 11 ) # External validation with time-dependent AUC val.ext <- validate_external( fit, x, time, event, x_new, time_new, event_new, tauc.type = "UNO", tauc.time = seq(0.25, 2, 0.25) * 365 ) print(val.ext) summary(val.ext) plot(val.ext) # # Test fused lasso, MCP, and Snet models # data(smart) # # Use first 600 samples as training data # # (the data used for internal validation) # x <- as.matrix(smart[, -c(1, 2)])[1:600, ] # time <- smart$TEVENT[1:600] # event <- smart$EVENT[1:600] # # # Take 500 samples as external validation data. # # In practice, usually use data collected in other studies. # x_new <- as.matrix(smart[, -c(1, 2)])[1001:1500, ] # time_new <- smart$TEVENT[1001:1500] # event_new <- smart$EVENT[1001:1500] # # 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) # # val.ext1 <- validate_external( # flassofit, x, time, event, # x_new, time_new, event_new, # tauc.type = "UNO", # tauc.time = seq(0.25, 2, 0.25) * 365) # # val.ext2 <- validate_external( # scadfit, x, time, event, # x_new, time_new, event_new, # tauc.type = "CD", # tauc.time = seq(0.25, 2, 0.25) * 365) # # val.ext3 <- validate_external( # mnetfit, x, time, event, # x_new, time_new, event_new, # tauc.type = "SZ", # tauc.time = seq(0.25, 2, 0.25) * 365) # # print(val.ext1) # summary(val.ext1) # plot(val.ext1) # # print(val.ext2) # summary(val.ext2) # plot(val.ext2) # # print(val.ext3) # summary(val.ext3) # plot(val.ext3)
Chambless, L. E. and G. Diao (2006). Estimation of time-dependent area under the ROC curve for long-term risk prediction. Statistics in Medicine 25, 3474--3486.
Song, X. and X.-H. Zhou (2008). A semiparametric approach for the covariate specific ROC curve with survival outcome. Statistica Sinica 18, 947--965.
Uno, H., T. Cai, L. Tian, and L. J. Wei (2007). Evaluating prediction rules for t-year survivors with censored regression models. Journal of the American Statistical Association 102, 527--537.
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