XGBoost Model with Modified Loss Function for Subgroup Identification with Binary Outcomes

Description

Function for training XGBoost model with customized loss function for binary outcomes

Usage

XGBoostSub_bin(
  X_data,
  y_data,
  trt,
  pi,
  Loss_type = "A_learning",
  params = list(),
  nrounds = 50,
  disable_default_eval_metric = 1,
  verbose = TRUE
)

Arguments

Details

XGBoostSub_bin: Function for Training XGBoost Model with Customized Loss Function for binary outcomes

This function trains an XGBoost model using a customized loss function based on the A-learning and weight-learning.

This function requires the 'xgboost' library. Make sure to install and load the 'xgboost' library before using this function.

After running this function, the returned model can be used like a regular xgboost model.

Value

Trained XGBoostSub_bin model.

Examples

X_data <- matrix(rnorm(100 * 10), ncol = 10)  # 100 samples with 10 features
y_data <- rbinom(100, 1, 0.5)  # binary outcomes (0 or 1)
trt <- sample(c(1, -1), 100, replace = TRUE)  # treatment indicator (1 or -1)
pi <- runif(100, min = 0.3, max = 0.7)  # propensity scores between 0 and 1

# Define XGBoost parameters
params <- list(
  max_depth = 3,
  eta = 0.1,
  subsample = 0.8,
  colsample_bytree = 0.8
)

# Train the model using A-learning loss
model_A <- XGBoostSub_bin(
  X_data = X_data,
  y_data = y_data,
  trt = trt,
  pi = pi,
  Loss_type = "A_learning",
  params = params,
  nrounds = 5,
  disable_default_eval_metric = 1,
  verbose = TRUE
)

# Train the model using Weight-learning loss
model_W <- XGBoostSub_bin(
  X_data = X_data,
  y_data = y_data,
  trt = trt,
  pi = pi,
  Loss_type = "Weight_learning",
  params = params,
  nrounds = 5,
  disable_default_eval_metric = 1,
  verbose = TRUE
)

XGBoost Model with Modified Loss Function for Subgroup Identification with Continuous Outcomes

Description

Function for training XGBoost model with customized loss function for continuous outcomes

Usage

XGBoostSub_con(
  X_data,
  y_data,
  trt,
  pi,
  Loss_type = "A_learning",
  params = list(),
  nrounds = 50,
  disable_default_eval_metric = 1,
  verbose = TRUE
)

Arguments

Details

XGBoostSub_con: Function for Training XGBoost Model with Customized Loss Function for continuous outcomes

This function trains an XGBoost model using a customized loss function based on the A-learning and weight-learning.

This function requires the 'xgboost' library. Make sure to install and load the 'xgboost' library before using this function.

After running this function, the returned model can be used like a regular xgboost model.

Value

Trained XGBoostSub_con model.

Examples

X_data <- matrix(rnorm(100 * 10), ncol = 10)  # 100 samples with 10 features
y_data <- rnorm(100)  # continuous outcome variable
trt <- sample(c(1, -1), 100, replace = TRUE)  # treatment indicator (1 or -1)
pi <- runif(100, min = 0.3, max = 0.7)  # propensity scores between 0 and 1

# Define XGBoost parameters
params <- list(
  max_depth = 3,
  eta = 0.1,
  subsample = 0.8,
  colsample_bytree = 0.8
)

# Train the model using A-learning loss
model_A <- XGBoostSub_con(
  X_data = X_data,
  y_data = y_data,
  trt = trt,
  pi = pi,
  Loss_type = "A_learning",
  params = params,
  nrounds = 5,
  disable_default_eval_metric = 1,
  verbose = TRUE
)

# Train the model using Weight-learning loss
model_W <- XGBoostSub_con(
  X_data = X_data,
  y_data = y_data,
  trt = trt,
  pi = pi,
  Loss_type = "Weight_learning",
  params = params,
  nrounds = 5,
  disable_default_eval_metric = 1,
  verbose = TRUE
)

XGBoost Model with Modified Loss Function for Subgroup Identification with Survival Outcomes

Description

Function for training XGBoost model with customized loss function for survival outcomes

Usage

XGBoostSub_sur(
  X_data,
  y_data,
  trt,
  pi,
  censor,
  Loss_type = "Weight_learning",
  params = list(),
  nrounds = 50,
  disable_default_eval_metric = 1,
  verbose = TRUE
)

Arguments

Details

XGBoostSub_sur: Function for Training XGBoost Model with Customized Loss Function for survival outcomes

This function trains an XGBoost model using a customized loss function based on the A-learning and weight-learning.

This function requires the 'xgboost' library. Make sure to install and load the 'xgboost' library before using this function.

Value

Trained XGBoostSub_sur model.

Examples

X_data <- matrix(rnorm(100 * 10), ncol = 10)  # 100 samples with 10 features
y_data <- rexp(100, rate = 0.1)  # survival times, simulated as exponential
trt <- sample(c(1, -1), 100, replace = TRUE)  # treatment indicator (1 or -1)
pi <- runif(100, min = 0.3, max = 0.7)  # propensity scores between 0 and 1
censor <- rbinom(100, 1, 0.7)  # censoring indicator (1 = censored, 0 = observed)

# Define XGBoost parameters
params <- list(
  max_depth = 3,
  eta = 0.1,
  subsample = 0.8,
  colsample_bytree = 0.8
)

# Train the model using A-learning loss
model_A <- XGBoostSub_sur(
  X_data = X_data,
  y_data = y_data,
  trt = trt,
  pi = pi,
  censor = censor,
  Loss_type = "A_learning",
  params = params,
  nrounds = 5,
  disable_default_eval_metric = 1,
  verbose = TRUE
)

# Train the model using Weight-learning loss
model_W <- XGBoostSub_sur(
  X_data = X_data,
  y_data = y_data,
  trt = trt,
  pi = pi,
  censor = censor,
  Loss_type = "Weight_learning",
  params = params,
  nrounds = 5,
  disable_default_eval_metric = 1,
  verbose = TRUE
)

Summarize Categorical Variables in Subgroup

Description

This function provides a summary of categorical variables in a dataset.

Usage

cat_summary(
  yvar,
  yname,
  xvars,
  xname.list,
  data,
  yvar.display = yvar,
  xvars.display = xvars
)

Arguments

Value

A list containing the contingency table, frequency table, and percentage table.

Examples

# Load a sample dataset
data <- data.frame(
  outcome = sample(c("A", "B", "C"), 100, replace = TRUE),  # categorical outcome
  group1 = sample(c("Male", "Female"), 100, replace = TRUE),  # group variable 1
  group2 = sample(c("Young", "Old"), 100, replace = TRUE)  # group variable 2
)

# Summarize categorical outcome by two grouping variables
cat_summary(
  yvar = "outcome",
  yname = c("A", "B", "C"),  # ordered categories for outcome
  xvars = c("group1", "group2"),
  xname.list = list(c("Male", "Female"), c("Young", "Old")),
  data = data,
  yvar.display = "Outcome Category",
  xvars.display = c("Gender", "Age Group")
)

CDF Plot for a biomarker

Description

Cumulative Distribution Function (CDF) plot for a biomarker.

Usage

cdf_plot(xvar, data, y.int = 5, xlim = NULL, xvar.display = xvar, group = NULL)

Arguments

Value

A ggplot object representing the CDF inverse plot.

Examples

# Load a sample dataset
  data <- data.frame(
  biomarker = rnorm(100, mean = 50, sd = 10),
  group = sample(c("Group A", "Group B"), 100, replace = TRUE)
)

# Basic CDF plot for a single biomarker without groups
  cdf_plot(
  xvar = "biomarker",
  data = data,
  y.int = 10,
  xlim = c(30, 70),
  xvar.display = "Biomarker Level"
)
# CDF plot for a biomarker with groups
cdf_plot(
  xvar = "biomarker",
  data = data,
  y.int = 10,
  xlim = c(30, 70),
  xvar.display = "Biomarker Level",
  group = "group"
)

Cutoff Performance Evaluation

Description

This function evaluates the performance of a predictive model at a selected cutoff point.

Usage

cut_perf(
  yvar,
  censorvar = NULL,
  xvar,
  cutoff,
  dir,
  xvars.adj = NULL,
  data,
  type,
  yvar.display = yvar,
  xvar.display = xvar
)

Arguments

Value

A list containing various performance metrics and optionally, plots.

Examples

# Load a sample dataset
data <- data.frame(
  survival_time = rexp(100, rate = 0.1),  # survival time
  status = sample(c(0, 1), 100, replace = TRUE),  # censoring status
  biomarker = rnorm(100, mean = 0, sd = 1),  # biomarker levels
  covariate1 = rnorm(100, mean = 50, sd = 10)  # an additional covariate
)
# Perform cutoff performance evaluation for continuous outcome
data$continuous_outcome <- rnorm(100, mean = 10, sd = 5)
cut_perf(
  yvar = "continuous_outcome",
  xvar = "biomarker",
  cutoff = 0.5,
  dir = ">=",
  data = data,
  type = "c",
  yvar.display = "Continuous Outcome",
  xvar.display = "Biomarker Level"
)

# Perform cutoff performance evaluation for binary outcome
data$binary_outcome <- sample(c(0, 1), 100, replace = TRUE)
cut_perf(
  yvar = "binary_outcome",
  xvar = "biomarker",
  cutoff = 0,
  dir = "<=",
  data = data,
  type = "b",
  yvar.display = "Binary Outcome",
  xvar.display = "Biomarker Level"
)

Evaluation Metrics for XGBoostSub_bin Model

Description

Function for evaluating XGBoostSub_bin model performance.

Usage

eval_metric_bin(model, X_feature, y_label, pi, trt, Loss_type = "A_learning")

Arguments

Details

eval_metric: Function for Evaluating XGBoostSub_bin Model Performance

This function evaluates the performance of an XGBoostSub_bin model using a A-learning or weight-learning function.

Value

Evaluation result of the XGBoostSub_bin model.

Evaluation Metrics for XGBoostSub_con Model

Description

Function for evaluating XGBoostSub_con model performance.

Usage

eval_metric_con(model, X_feature, y_label, pi, trt, Loss_type = "A_learning")

Arguments

Details

eval_metric: Function for Evaluating XGBoostSub_con Model Performance

This function evaluates the performance of an XGBoostSub_con model using a A-learning or weight-learning function.

Value

Evaluation result of the XGBoostSub_con model.

Evaluation Metrics for XGBoostSub_sur Model

Description

Function for evaluating XGBoostSub_sur model performance.

Usage

eval_metric_sur(
  model,
  X_feature,
  y_label,
  pi,
  trt,
  censor,
  Loss_type = "A_learning"
)

Arguments

Details

eval_metric: Function for Evaluating XGBoostSub_con Model Performance

This function evaluates the performance of an XGBoostSub_con model using a A-learning or weight-learning function.

Value

Evaluation result of the XGBoostSub_sur model.

Fixed Cutoff Analysis for Individual Biomarker Associated with Binary Outcome Variables

Description

This function conducts fixed cutoff analysis for individual biomarker associated with binary outcome variables.

Usage

fixcut_bin(
  yvar,
  xvar,
  dir,
  cutoffs,
  data,
  method = "Fisher",
  yvar.display = yvar,
  xvar.display = xvar,
  vert.x = FALSE
)

Arguments

Value

A list containing statistical summaries, selected cutoff statistics, selected cutoff value, confusion matrix, and a ggplot object for visualization.

Examples

# Load a sample dataset
data <- data.frame(
  outcome = sample(c(0, 1), 100, replace = TRUE),
  biomarker = rnorm(100, mean = 0, sd = 1)
)

# Perform fixed cutoff analysis using the "Fisher" method for a biomarker
fixcut_bin(
  yvar = "outcome",
  xvar = "biomarker",
  dir = ">",
  cutoffs = seq(-2, 2, by = 0.5),
  data = data,
  method = "Fisher",
  yvar.display = "Binary Outcome",
  xvar.display = "Biomarker Level",
  vert.x = TRUE
)

# Perform fixed cutoff analysis using the "Youden" method
fixcut_bin(
  yvar = "outcome",
  xvar = "biomarker",
  dir = "<",
  cutoffs = seq(-2, 2, by = 0.5),
  data = data,
  method = "Youden",
  yvar.display = "Binary Outcome",
  xvar.display = "Biomarker Level",
  vert.x = FALSE
)

# Perform fixed cutoff analysis using "Accuracy" method with different direction
fixcut_bin(
  yvar = "outcome",
  xvar = "biomarker",
  dir = ">=",
  cutoffs = c(-1, 0, 1),
  data = data,
  method = "Accuracy",
  yvar.display = "Binary Outcome",
  xvar.display = "Biomarker Level",
  vert.x = TRUE
)

Fixed Cutoff Analysis for Individual Biomarker Associated with Continuous Outcome

Description

This function conducts fixed cutoff analysis for individual biomarker associated with continuous outcome variables.

Usage

fixcut_con(
  yvar,
  xvar,
  dir,
  cutoffs,
  data,
  method = "t.test",
  yvar.display = yvar,
  xvar.display = xvar,
  vert.x = FALSE
)

Arguments

Value

A list containing statistical summaries, selected cutoff statistics, selected cutoff value, group statistics, and a ggplot object for visualization.

Examples

# Load a sample dataset
data <- data.frame(
  outcome = rnorm(100, mean = 10, sd = 5),
  biomarker = rnorm(100, mean = 0, sd = 1)
)

# Perform fixed cutoff analysis using the "t.test" method with '>' direction
fixcut_con(
  yvar = "outcome",
  xvar = "biomarker",
  dir = ">",
  cutoffs = seq(-2, 2, by = 0.5),
  data = data,
  method = "t.test",
  yvar.display = "Continuous Outcome",
  xvar.display = "Biomarker Level",
  vert.x = TRUE
)

# Perform fixed cutoff analysis with '<=' direction
fixcut_con(
  yvar = "outcome",
  xvar = "biomarker",
  dir = "<=",
  cutoffs = c(-1, 0, 1),
  data = data,
  method = "t.test",
  yvar.display = "Continuous Outcome",
  xvar.display = "Biomarker Level",
  vert.x = FALSE
)

Fixed Cutoff Analysis for Individual Biomarker Associated with Survival Outcome

Description

This function conducts fixed cutoff analysis for Individual Biomarker Associated with survival outcome variables.

Usage

fixcut_sur(
  yvar,
  censorvar,
  xvar,
  dir,
  cutoffs,
  data,
  method = "logrank",
  yvar.display = yvar,
  xvar.display = xvar,
  vert.x = FALSE
)

Arguments

Value

A list containing statistical summaries, selected cutoff statistics, selected cutoff value, group statistics, and a ggplot object for visualization.

Examples

# Load a sample dataset
data <- data.frame(
  time = rexp(100, rate = 0.1),  # survival time
  status = sample(c(0, 1), 100, replace = TRUE),  # censoring status
  biomarker = rnorm(100, mean = 0, sd = 1)  # biomarker levels
)

fixcut_sur(
  yvar = "time",
  censorvar = "status",
  xvar = "biomarker",
  dir = "<=",
  cutoffs = c(-1, 0, 1),
  data = data,
  method = "logrank",
  yvar.display = "Survival Time",
  xvar.display = "Biomarker Level",
  vert.x = FALSE
)

GAM Contrast Plot

Description

Computes and plots the contrasts between treatment and control group based on a GAM for exploring the relationship be-tween treatment benefit and biomarker.

Usage

gam_ctr_plot(
  yvar,
  censorvar = NULL,
  xvar,
  xvars.adj = NULL,
  sxvars.adj = NULL,
  trtvar = NULL,
  type,
  data,
  k,
  title = "Group Contrast",
  ybreaks = NULL,
  xbreaks = NULL,
  rugcol.var = NULL,
  link.scale = TRUE,
  prt.sum = TRUE,
  prt.chk = FALSE,
  outlier.rm = FALSE
)

Arguments

Value

A list containing the p-value table, summarized p-value table, s-value table, summarized s-value table, and the plot.

Examples

# Load a sample dataset
data <- data.frame(
  response = rnorm(100),
  biomarker = rnorm(100, mean = 50, sd = 10),
  censor = sample(c(0, 1), 100, replace = TRUE),
  treatment = sample(c(0, 1), 100, replace = TRUE),
  age = rnorm(100, mean = 60, sd = 10),
  group = sample(c("Group A", "Group B"), 100, replace = TRUE)
)

# Generate a GAM contrast plot for a continuous response variable
gam_ctr_plot(
  yvar = "response",
  xvar = "biomarker",
  trtvar = "treatment",
  type = "c",
  data = data,
  xvars.adj = "age",
  k = 5,
  title = "GAM Contrast Plot for Treatment vs. Control"
)

# Generate a GAM contrast plot for survival analysis
gam_ctr_plot(
  yvar = "response",
  censorvar = "censor",
  xvar = "biomarker",
  trtvar = "treatment",
  type = "s",
  data = data,
  k = 5,
  title = "GAM Contrast Plot for Survival Data"
)

# Generate a GAM contrast plot for a binary response variable
data$binary_response <- as.numeric(data$response > 0)
gam_ctr_plot(
  yvar = "binary_response",
  xvar = "biomarker",
  trtvar = "treatment",
  type = "b",
  data = data,
  k = 5,
  title = "GAM Contrast Plot for Binary Outcome"
)

GAM Plot

Description

Generates a generalized additive model (GAM) plot for exploring the relationship between a response variable and a biomarker.

Usage

gam_plot(
  yvar,
  censorvar = NULL,
  xvar,
  xvars.adj = NULL,
  sxvars.adj = NULL,
  type,
  data,
  k,
  pred.type = "iterms",
  link.scale = TRUE,
  title = "Trend Plot",
  ybreaks = NULL,
  xbreaks = NULL,
  rugcol.var = NULL,
  add.points = FALSE,
  prt.sum = TRUE,
  prt.chk = FALSE,
  outlier.rm = FALSE,
  newdat = NULL
)

Arguments

Value

A list containing p-table, s-table, GAM summary, GAM check, and the plot.

Examples

# Load a sample dataset
data <- data.frame(
  response = rnorm(100),
  biomarker = rnorm(100, mean = 50, sd = 10),
  censor = sample(c(0, 1), 100, replace = TRUE),
  age = rnorm(100, mean = 60, sd = 10),
  group = sample(c("Group A", "Group B"), 100, replace = TRUE)
)

# Generate a GAM plot for a continuous response variable
gam_plot(
  yvar = "response",
  xvar = "biomarker",
  type = "c",
  data = data,
  xvars.adj = "age",
  sxvars.adj = NULL,
  k = 5,
  pred.type = "iterms",
  title = "GAM Plot of Biomarker and Response"
)

# Generate a GAM plot for survival analysis
gam_plot(
  yvar = "response",
  censorvar = "censor",
  xvar = "biomarker",
  type = "s",
  data = data,
  k = 5,
  title = "GAM Survival Plot for Biomarker"
)

# Generate a GAM plot for a binary response variable
data$binary_response <- as.numeric(data$response > 0)
gam_plot(
  yvar = "binary_response",
  xvar = "biomarker",
  type = "b",
  data = data,
  k = 5,
  pred.type = "response",
  title = "GAM Plot for Binary Response"
)

Get Subgroup Results

Description

This function predicts the treatment assignment for each patient based on a cutoff value.

Usage

get_subgroup_results(model, X_feature, subgroup_label = NULL, cutoff = 0.5)

Arguments

Value

A data frame containing each subject and assigned treatment (1 for treatment, 0 for control). If subgroup labels are provided, it also returns the prediction accuracy of the subgroup labels.

Examples

X_data <- matrix(rnorm(100 * 10), ncol = 10)  # 100 samples with 10 features
y_data <- rnorm(100)  # continuous outcome variable
trt <- sample(c(1, -1), 100, replace = TRUE)  # treatment indicator (1 or -1)
pi <- runif(100, min = 0.3, max = 0.7)  # propensity scores between 0 and 1

# Define XGBoost parameters
params <- list(
  max_depth = 3,
  eta = 0.1,
  subsample = 0.8,
  colsample_bytree = 0.8
)

# Train the model using A-learning loss
model_A <- XGBoostSub_con(
  X_data = X_data,
  y_data = y_data,
  trt = trt,
  pi = pi,
  Loss_type = "A_learning",
  params = params,
  nrounds = 5,
  disable_default_eval_metric = 1,
  verbose = TRUE
)
subgroup_results=get_subgroup_results(model_A, X_data, subgroup_label=NULL, cutoff = 0.5)

Outlier Detection Based on Boxplot

Description

This function identifies outliers in a numeric vector based on the interquartile range (IQR) method used in boxplots.

Usage

isout(x)

Arguments

Value

A logical vector indicating which elements of the input vector are outliers (TRUE if an outlier, FALSE otherwise).

Plot Predictive Biomarker Importance based on XGBoost-based Subgroup Model

Description

This function calculates and plots the importance of biomarkers in a trained XGBoostSub_con, XGBoostSub_bin or XGBoostSub_sur model.

Usage

predictive_biomarker_imp(model)

Arguments

Value

A barplot showing the biomarker importance.

Examples

X_data <- matrix(rnorm(100 * 10), ncol = 10)  # 100 samples with 10 features
y_data <- rnorm(100)  # continuous outcome variable
trt <- sample(c(1, -1), 100, replace = TRUE)  # treatment indicator (1 or -1)
pi <- runif(100, min = 0.3, max = 0.7)  # propensity scores between 0 and 1

# Define XGBoost parameters
params <- list(
  max_depth = 3,
  eta = 0.1,
  subsample = 0.8,
  colsample_bytree = 0.8
)

# Train the model using A-learning loss
model_A <- XGBoostSub_con(
  X_data = X_data,
  y_data = y_data,
  trt = trt,
  pi = pi,
  Loss_type = "A_learning",
  params = params,
  nrounds = 5,
  disable_default_eval_metric = 1,
  verbose = TRUE
)
biomarker_imp=predictive_biomarker_imp(model_A)

AUC ROC Table for Biomarkers Associated with Binary Outcomes

Description

Computes the area under the receiver operating characteristic (ROC) curve for Biomarkers Associated with Binary Outcomes, and returns the results as a table.

Usage

roc_bin(yvar, xvars, dirs, data, yvar.display = yvar, xvars.display = xvars)

Arguments

Value

A table containing the AUC values for each biomarker.

Examples

# Load a sample dataset
data <- data.frame(
  outcome = sample(c(0, 1), 100, replace = TRUE),
  biomarker1 = rnorm(100, mean = 0, sd = 1),
  biomarker2 = rnorm(100, mean = 5, sd = 2)
)

# Compute AUC for a single biomarker with auto direction
roc_bin(
  yvar = "outcome",
  xvars = "biomarker1",
  dirs = "auto",
  data = data,
  yvar.display = "Binary Outcome",
  xvars.display = "Biomarker 1"
)

# Compute AUC for multiple biomarkers with specified directions
roc_bin(
  yvar = "outcome",
  xvars = c("biomarker1", "biomarker2"),
  dirs = c("auto", "<"),
  data = data,
  yvar.display = "Binary Outcome",
  xvars.display = c("Biomarker 1", "Biomarker 2")
)

ROC Plot Biomarkers Associated with Binary Outcomes

Description

Generates ROC plots for different biomarkers associated with binary outcomes.

Usage

roc_bin_plot(
  yvar,
  xvars,
  dirs,
  data,
  yvar.display = yvar,
  xvars.display = xvars
)

Arguments

Value

ROC plots for different biomarkers associated with binary outcomes.

Examples

# Load a sample dataset
data <- data.frame(
  outcome = sample(c(0, 1), 100, replace = TRUE),
  biomarker1 = rnorm(100, mean = 0, sd = 1),
  biomarker2 = rnorm(100, mean = 5, sd = 2)
)

# Generate ROC plot for a single biomarker with auto direction
roc_bin_plot(
  yvar = "outcome",
  xvars = "biomarker1",
  dirs = "auto",
  data = data,
  yvar.display = "Binary Outcome",
  xvars.display = "Biomarker 1"
)

# Generate ROC plots for multiple biomarkers with specified directions
roc_bin_plot(
  yvar = "outcome",
  xvars = c("biomarker1", "biomarker2"),
  dirs = c("auto", "<"),
  data = data,
  yvar.display = "Binary Outcome",
  xvars.display = c("Biomarker 1", "Biomarker 2")
)

Scatter Plot for a Biomarker Associated with Continuous Outcome

Description

Generates a scatter plot for exploring the relationship between a continuous response variable and a biomarker variable.

Usage

scat_cont_plot(
  yvar,
  xvar,
  data,
  ybreaks = NULL,
  xbreaks = NULL,
  yvar.display = yvar,
  xvar.display = xvar
)

Arguments

Value

A list containing correlation coefficients, scatter plot, slope, and intercept.

Examples

data <- data.frame(
  outcome = rnorm(100, mean = 10, sd = 2),
  biomarker = rnorm(100, mean = 0, sd = 1)
)

# Generate a scatter plot with default axis breaks
scat_cont_plot(
  yvar = "outcome",
  xvar = "biomarker",
  data = data,
  yvar.display = "Continuous Outcome",
  xvar.display = "Biomarker Level"
)

# Generate a scatter plot with specified axis breaks
scat_cont_plot(
  yvar = "outcome",
  xvar = "biomarker",
  data = data,
  ybreaks = seq(5, 15, by = 1),
  xbreaks = seq(-2, 2, by = 0.5),
  yvar.display = "Continuous Outcome",
  xvar.display = "Biomarker Level"
)

Subgroup Performance Evaluation for Prognostic Cases

Description

This function evaluates subgroup performance based on different types of response variables.

Usage

subgrp_perf(
  yvar,
  censorvar = NULL,
  grpvar,
  grpname,
  xvars.adj = NULL,
  data,
  type,
  yvar.display = yvar,
  grpvar.display = grpvar
)

Arguments

Value

A list containing subgroup performance results including logrank p-value, median and mean survival, Cox model p-value, ANOVA p-value, and more based on the specified response variable type.

Examples

# Load a sample dataset
data <- data.frame(
  survival_time = rexp(100, rate = 0.1),  # survival time
  status = sample(c(0, 1), 100, replace = TRUE),  # censoring status
  group = sample(c("Low", "Medium", "High"), 100, replace = TRUE),  # subgroup variable
  covariate = rnorm(100, mean = 50, sd = 10)  # an additional covariate
)

# Perform subgroup performance evaluation for survival analysis
subgrp_perf(
  yvar = "survival_time",
  censorvar = "status",
  grpvar = "group",
  grpname = c("Low", "Medium", "High"),
  data = data,
  type = "s",
  yvar.display = "Survival Time",
  grpvar.display = "Risk Group"
)

# Perform subgroup performance evaluation for continuous outcome
data$continuous_outcome <- rnorm(100, mean = 10, sd = 5)
subgrp_perf(
  yvar = "continuous_outcome",
  grpvar = "group",
  grpname = c("Low", "Medium", "High"),
  data = data,
  type = "c",
  yvar.display = "Continuous Outcome",
  grpvar.display = "Risk Group"
)

# Perform subgroup performance evaluation for binary outcome
data$binary_outcome <- sample(c(0, 1), 100, replace = TRUE)
subgrp_perf(
  yvar = "binary_outcome",
  grpvar = "group",
  grpname = c("Low", "Medium", "High"),
  data = data,
  type = "b",
  yvar.display = "Binary Outcome",
  grpvar.display = "Risk Group"
)

Subgroup Performance Evaluation for Predictive Cases

Description

This function evaluates the performance of subgroups based on different types of response variables in predictive cases.

Usage

subgrp_perf_pred(
  yvar,
  censorvar = NULL,
  grpvar,
  grpname,
  trtvar,
  trtname,
  xvars.adj = NULL,
  data,
  type,
  yvar.display = yvar,
  grpvar.display = grpvar,
  trtvar.display = trtvar
)

Arguments

Value

A list containing the comparison results, group results, and possibly a plot.

Examples

# Load a sample dataset
data <- data.frame(
  response = rnorm(100, mean = 10, sd = 5),  # continuous response
  survival_time = rexp(100, rate = 0.1),  # survival time
  status = sample(c(0, 1), 100, replace = TRUE),  # censoring status
  group = sample(c("Low", "Medium", "High"), 100, replace = TRUE),  # subgroup variable
  treatment = sample(c("A", "B"), 100, replace = TRUE)  # treatment variable
)

# Subgroup performance evaluation for predictive cases - survival analysis
subgrp_perf_pred(
  yvar = "survival_time",
  censorvar = "status",
  grpvar = "group",
  grpname = c("Low", "Medium", "High"),
  trtvar = "treatment",
  trtname = c("A", "B"),
  data = data,
  type = "s",
  yvar.display = "Survival Time",
  grpvar.display = "Risk Group",
  trtvar.display = "Treatment"
)

# Subgroup performance evaluation for predictive cases - continuous outcome
subgrp_perf_pred(
  yvar = "response",
  grpvar = "group",
  grpname = c("Low", "Medium", "High"),
  trtvar = "treatment",
  trtname = c("A", "B"),
  data = data,
  type = "c",
  yvar.display = "Response",
  grpvar.display = "Risk Group",
  trtvar.display = "Treatment"
)

# Subgroup performance evaluation for predictive cases - binary outcome
data$binary_response <- sample(c(0, 1), 100, replace = TRUE)
subgrp_perf_pred(
  yvar = "binary_response",
  grpvar = "group",
  grpname = c("Low", "Medium", "High"),
  trtvar = "treatment",
  trtname = c("A", "B"),
  data = data,
  type = "b",
  yvar.display = "Binary Response",
  grpvar.display = "Risk Group",
  trtvar.display = "Treatment"
)

Tutorial Data

Description

A dataset containing sample data for demonstrating the functionalities of the BioPred package.

Usage

data(tutorial_data)

Format

A data frame with the following columns:

x1

Numeric. A biomarker variable.

x2

Numeric. A biomarker variable.

x3

Numeric. A biomarker variable.

x4

Numeric. A biomarker variable.

x5

Numeric. A biomarker variable.

x6

Numeric. A biomarker variable.

x7

Numeric. A biomarker variable.

x8

Numeric. A biomarker variable.

x9

Numeric. A biomarker variable.

x10

Numeric. A biomarker variable.

y.con

Numeric. A continuous outcome variable.

y.bin

Binary. A binary outcome variable, where 0 represents one class and 1 represents another class.

y.time

Numeric. The time in months, used for survival analysis.

y.event

Binary. Event indicator variable, where 0 indicates censoring and 1 indicates the event of interest occurred.

subgroup_label

Binary. Ground truth of subgroup label. In real-world scenarios, this information is typically unavailable.

treatment

Binary. Treatment indicator variable, where 0 represents control and 1 represents treatment.

treatment_categorical

Factor. A categorical version of the treatment variable, with levels "Placebo" and "Treatment".

risk_category

Factor.

Details

This dataset is used to illustrate various functions within the BioPred package, including predictive modeling and subgroup analysis. The columns represent different types of data typically encountered in clinical studies.

Examples

data(tutorial_data)
head(tutorial_data)