Batch Survival Analysis — batch

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

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"
)
#> # A tibble: 2 × 5
#>   ID             P    HR CI_low_0.95 CI_up_0.95
#>   <chr>      <dbl> <dbl>       <dbl>      <dbl>
#> 1 Signature2 0.317 0.862       0.644       1.15
#> 2 Signature1 0.365 0.863       0.628       1.19