getTestKM function

getTestKM

This function computes and visualizes the Kaplan-Meier survival curve for a given test dataset, utilizing the cutoff derived from the original model. The function offers flexibility in terms of the type of Kaplan-Meier estimation, whether it's based on the linear predictor, PLS components, or original variables.

getTestKM(
  model,
  X_test,
  Y_test,
  cutoff,
  type = "LP",
  ori_data = TRUE,
  BREAKTIME = NULL,
  n.breaks = 20,
  title = NULL,
  subtitle = NULL
)

Arguments

• model: Coxmos model.
• X_test: Numeric matrix or data.frame. Explanatory variables for test data (raw format). Qualitative variables must be transform into binary variables.
• Y_test: Numeric matrix or data.frame. Response variables for test data. Object must have two columns named as "time" and "event". For event column, accepted values are: 0/1 or FALSE/TRUE for censored and event observations.
• cutoff: Numeric. Cutoff value to split the observations into two groups. Recommended to compute optimal cutoff value with getAutoKM() function.
• type: Character. Kaplan Meier for complete model linear predictor ("LP"), for PLS components ("COMP") or for original variables ("VAR") (default: LP).
• ori_data: Logical. Compute the Kaplan-Meier plot with the raw-data or the normalize-data to compute the best cut-point for splitting the data into two groups. Only used when type = "VAR" (default: TRUE).
• BREAKTIME: Numeric. Size of time to split the data into "total_time / BREAKTIME + 1" points. If BREAKTIME = NULL, "n.breaks" is used (default: NULL).
• n.breaks: Numeric. If BREAKTIME is NULL, "n.breaks" is the number of time-break points to compute (default: 20).
• title: Character. Kaplan-Meier plot title (default: NULL).
• subtitle: Character. Kaplan-Meier plot subtitle (default: NULL).

Returns

Depending on the specified type parameter, the function returns:

• LP: A ggplot object visualizing the Kaplan-Meier survival curve based on the linear predictor, segmented by the specified cutoff.
• COMP: A list of ggplot objects, where each plot represents the Kaplan-Meier survival curve for a specific PLS component in the model, segmented by the respective cutoffs.
• VAR: A list of ggplot objects, where each plot visualizes the Kaplan-Meier survival curve for a specific variable in the test dataset, segmented by the respective cutoffs.

Each plot provides a visual representation of the survival probabilities over time, allowing for a comprehensive evaluation of the test dataset's survival characteristics in the context of the original model.

Details

The getTestKM function is designed to evaluate the survival probabilities of a test dataset based on a pre-trained Coxmos model. The function ensures that the test times are consistent with the training times. Depending on the specified type, the function can compute the Kaplan-Meier curve using:

• The complete model's linear predictor (LP).
• The PLS components (COMP).
• The original variables (VAR).

For the LP type, the function predicts scores for the X_test and subsequently predicts the linear predictor using these scores. For the COMP type, the function predicts scores for each component in the model and computes the Kaplan-Meier curve for each. For the VAR type, the function computes the Kaplan-Meier curve for each variable in the test dataset.

The function also provides the flexibility to compute the Kaplan-Meier plot using raw data or normalized data, which can be useful for determining the optimal cut-point for data segmentation. The time intervals for the Kaplan-Meier estimation can be defined using either the BREAKTIME or n.breaks parameters.

The resulting Kaplan-Meier plot provides a visual representation of the survival probabilities over time, segmented based on the specified cutoff. This allows for a comprehensive evaluation of the test dataset's survival characteristics in the context of the original model.

Examples

data("X_proteomic")
data("Y_proteomic")
set.seed(123)
index_train <- caret::createDataPartition(Y_proteomic$event, p = .5, list = FALSE, times = 1)
X_train <- X_proteomic[index_train,1:50]
Y_train <- Y_proteomic[index_train,]
X_test <- X_proteomic[-index_train,1:50]
Y_test <- Y_proteomic[-index_train,]
splsicox.model <- splsicox(X_train, Y_train, n.comp = 2, penalty = 0.5, x.center = TRUE,
x.scale = TRUE)
KMresult = getAutoKM(type = "LP", model = splsicox.model)
cutoff <- getCutoffAutoKM(result = KMresult)
getTestKM(splsicox.model, X_test, Y_test, cutoff)

References

\insertRef Kaplan_1958Coxmos

Author(s)

Pedro Salguero Garcia. Maintainer: pedsalga@upv.edu.es