batch_surv: Batch Survival Analysis

In IOBR: Immune Oncology Biological Research

Batch Survival Analysis

Description

Performs Cox proportional hazards regression analysis on multiple variables. Optionally determines optimal cutoffs to dichotomize continuous predictors before modeling. Returns hazard ratios, confidence intervals, and p-values for each variable.

Usage

batch_surv(
  pdata,
  variable,
  time = "time",
  status = "status",
  best_cutoff = FALSE
)

Arguments

pdata	Data frame containing survival time, event status, and predictor variables.
variable	Character vector specifying the names of predictor variables to analyze.
time	Character string specifying the column name containing follow-up time. Default is '"time"'.
status	Character string specifying the column name containing event status (1 = event occurred, 0 = censored). Default is '"status"'.
best_cutoff	Logical indicating whether to compute optimal cutoffs for continuous variables and analyze dichotomized versions. Default is 'FALSE'.

Value

Data frame containing hazard ratios (HR), 95 and p-values for each variable, sorted by p-value.

Author(s)

Dongqiang Zeng

Examples

# Create small example data
set.seed(123)
test_data <- data.frame(
  OS_time = runif(100, 0, 100),
  OS_status = sample(c(0, 1), 100, replace = TRUE),
  Signature1 = rnorm(100),
  Signature2 = rnorm(100)
)
batch_surv(
  pdata = test_data,
  variable = c("Signature1", "Signature2"),
  time = "OS_time",
  status = "OS_status"
)

IOBR documentation built on May 30, 2026, 5:07 p.m.