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Calculate statistical significance on a survival dataset

survival_test.Rd

Performs a survival analysis using the standard log-rank test (LRT), a weighted log-rank test in the Fleming-Harrington family (WLRT), or the modestly-weighted log-rank test of Magirr and Burman (MW), and determines whether the result is statistically significant at the specified alpha level and alternative hypothesis.

Usage

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

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, "WLRT" for a Fleming-Harrington-family weighted log-rank test, or "MW" for the modestly-weighted log-rank test.

alternative

String specifying the alternative hypothesis: "one.sided" (default) or "two.sided".

alpha

Type I error level for the significance test (required, no default). Pass NULL if only Z is needed; Signif is then NA.

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 the modestly weighted test.

s_star

Parameter \(s^*\) used in the modestly weighted test.

return_HR

If TRUE (default), fit a Cox model and return the estimated hazard ratio as observed_HR. If FALSE, the Cox fit is skipped (it does not affect Signif or Z) and observed_HR is NA. The group-sequential and predictive probability code uses FALSE.

Value

A list containing:

Signif

Logical: whether the test is significant at level alpha. FALSE if Z is NA; NA if alpha is NULL.

observed_HR

Estimated hazard ratio from a Cox proportional hazards model, or NA if return_HR = FALSE.

Z

Signed test statistic. For all three methods, positive values favour the arm coded as "Treatment" (the second factor level of group), i.e. positive Z corresponds to a hazard ratio below 1.

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 3
#>  $ Signif     : logi FALSE
#>  $ observed_HR: num 0.646
#>  $ Z          : num 1.21