Calculate statistical significance on a survival dataset

Performs a survival analysis using either the standard log-rank test (LRT) or a weighted log-rank test (WLRT). The function estimates the hazard ratio and determines whether the result is statistically significant based on the specified alpha level and alternative hypothesis.

Usage

survival_test(
  data,
  analysis_method = "LRT",
  alternative = "one.sided",
  alpha = 0.05,
  rho = 0,
  gamma = 0,
  t_star = NULL,
  s_star = NULL
)

Arguments

data: A dataframe containing survival data. Must include columns for survival time, event status, and treatment group.
analysis_method: Method of analysis: "LRT" (default) for standard log-rank test, or "WLRT" for weighted log-rank test.
alternative: String specifying the alternative hypothesis. Must be one of "one.sided" or "two.sided" (default).
alpha: Type I error threshold for significance testing.
rho: Rho parameter for the Fleming-Harrington weighted log-rank test.
gamma: Gamma parameter for the Fleming-Harrington weighted log-rank test.
t_star: Parameter \(t^*\) used in modestly weighted tests.
s_star: Parameter \(s^*\) used in modestly weighted tests.

Value

A list containing:

Signif: Logical indicator of statistical significance based on the chosen test and alpha level.
observed_HR: Estimated hazard ratio from a Cox proportional hazards model.

Examples

set.seed(123)
df <- data.frame(
  survival_time = rexp(40, rate = 0.1),
  status = rbinom(40, 1, 0.8),
  group = rep(c("Control", "Treatment"), each = 20)
)
result <- survival_test(df, analysis_method = "LRT", alpha = 0.05)
str(result)
#> List of 2
#>  $ Signif     : logi FALSE
#>  $ observed_HR: num 0.646