getAutoKM function

getAutoKM

Generates a Kaplan-Meier plot for the specified Coxmos model. The plot can be constructed based on the model's Linear Predictor value, the PLS-COX component, or the original variable level.

getAutoKM(
  type = "LP",
  model,
  comp = 1:2,
  top = 10,
  ori_data = TRUE,
  BREAKTIME = NULL,
  n.breaks = 20,
  minProp = 0.2,
  only_sig = FALSE,
  alpha = 0.05,
  title = NULL,
  subtitle = NULL,
  verbose = FALSE
)

Arguments

• type: Character. Kaplan Meier for complete model linear predictor ("LP"), for PLS components ("COMP") or for original variables ("VAR") (default: LP).
• model: Coxmos model.
• comp: Numeric vector. Vector of length two. Select which components to plot (default: c(1,2)).
• top: Numeric. Show "top" first variables. If top = NULL, all variables are shown (default: 10).
• 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).
• minProp: Numeric. Minimum proportion rate (0-1) for the group of lesser observation when computing an optimal cutoff for numerical variables (default: 0.2).
• only_sig: Logical. If "only_sig" = TRUE, then only significant log-rank test variables are returned (default: FALSE).
• alpha: Numeric. Numerical values are regarded as significant if they fall below the threshold (default: 0.05).
• title: Character. Kaplan-Meier plot title (default: NULL).
• subtitle: Character. Kaplan-Meier plot subtitle (default: NULL).
• verbose: Logical. If verbose = TRUE, extra messages could be displayed (default: FALSE).

Returns

A list of two elements per each model in the list: info_logrank_num: A list of two data.frames with the numerical variables categorize as qualitative and the cutpoint to divide the data into two groups. LST_PLOTS: A list with the Kaplan-Meier Plots.

Details

The getAutoKM function offers a flexible approach to visualize survival analysis results using the Kaplan-Meier method. Depending on the type parameter, the function can generate plots based on different aspects of the Coxmos model:

• "LP": Uses the Linear Predictor value of the model.
• "COMP": Utilizes the PLS-COX component.
• "VAR": Operates at the original variable level.

The function provides options to customize the number of components (comp), the number of top variables (top), and whether to use raw or normalized data (ori_data). Additionally, users can specify the time intervals (BREAKTIME and n.breaks) for the Kaplan-Meier plot. If significance testing is desired, the function can filter out non-significant variables based on the log-rank test (only_sig and alpha parameters).

It's essential to ensure that the provided model is of the correct class (Coxmos). The function will return an error message if an incompatible model is supplied.

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)
getAutoKM(type = "LP", model = splsicox.model)

References

\insertRef Kaplan_1958Coxmos

Author(s)

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