getAutoKM: getAutoKM

In Coxmos: Cox MultiBlock Survival

getAutoKM

Description

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.

Usage

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).

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.

Value

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.

Author(s)

Pedro Salguero Garcia. Maintainer: [e-mail]

References

\insertRef

Kaplan_1958Coxmos

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)

Coxmos documentation built on April 4, 2025, 12:20 a.m.