cox_rhogamma: Weighted Cox Model with Rho-Gamma Weights

In weightedsurv: Survival Analysis with Subject-Specific (Case Weights) and Time-Dependent Weighting

Weighted Cox Model with Rho-Gamma Weights

Description

Fits a weighted Cox proportional hazards model using flexible time-dependent weights (e.g., Fleming-Harrington, Magirr-Burman). Supports resampling-based inference for variance estimation and bias correction.

Usage

cox_rhogamma(
  dfcount,
  scheme = "fh",
  scheme_params = list(rho = 0, gamma = 0.5),
  draws = 0,
  alpha = 0.05,
  verbose = FALSE,
  lr.digits = 4
)

Arguments

dfcount	List; output from df_counting containing counting process data including time, delta, z, w_hat, survP_all, survG_all, etc.
scheme	Character; weighting scheme. See wt.rg.S for options. Default: "fh".
scheme_params	List; parameters for the selected scheme. Default: list(rho = 0, gamma = 0.5). Required parameters depend on scheme: "fh": rho, gamma "MB": mb_tstar "custom_time": t.tau, w0.tau, w1.tau
draws	Integer; number of resampling draws for variance estimation and bias correction. If 0, only asymptotic inference is performed. Default: 0.
alpha	Numeric; significance level for confidence intervals. Default: 0.05.
verbose	Logical; whether to print detailed output. Default: FALSE.
lr.digits	Integer; number of decimal places for formatted output. Default: 4.

Details

This function solves the weighted Cox partial likelihood score equation: U(beta) = sum_i w_i K_i (dN_0/Y_0 - dN_1/Y_1) = 0

where K_i are time-dependent weights and Y_j, dN_j are risk sets and event counts for group j.

When draws > 0, the function performs resampling to:

• Estimate finite-sample variance (more accurate than asymptotic)

• Compute bias correction for \hat{\beta}

• Provide improved confidence intervals for small samples

The score test at \beta=0 corresponds to the weighted log-rank test.

Value

A list containing:

fit

List with fitted model components:

• bhat: Estimated log hazard ratio

• sig_bhat_asy: Asymptotic standard error

• u.zero: Score statistic at beta=0 (log-rank)

• z.score: Standardized score statistic

• sig2_score: Variance of score statistic

• wt_rg: Vector of time-dependent weights

• bhat_debiased: Bias-corrected estimate (if draws > 0)

• sig_bhat_star: Resampling-based standard error (if draws > 0)

hr_ci_asy

Data frame with asymptotic HR and CI

hr_ci_star

Data frame with resampling-based HR and CI (if draws > 0)

cox_text_asy

Formatted string with HR and asymptotic CI

cox_text_star

Formatted string with HR and resampling CI (if draws > 0)

z.score_debiased

Bias-corrected score statistic (if draws > 0)

zlogrank_text

Formatted log-rank test result

Note

The treatment variable in dfcount must be coded as 0=control, 1=experimental.

References

Magirr, D. and Burman, C. F. (2019). Modestly weighted logrank tests. Statistics in Medicine, 38(20), 3782-3790.

See Also

df_counting for preprocessing wt.rg.S for weighting schemes cox_score_rhogamma for score function

Other survival_analysis: KM_diff(), df_counting(), wt.rg.S()

Other weighted_tests: df_counting(), wt.rg.S()

Examples

# First get counting process data
library(survival)
str(veteran)
veteran$treat <- as.numeric(veteran$trt) - 1

dfcount <- df_counting(
  df = veteran,
  tte.name = "time",
  event.name = "status",
  treat.name = "treat"
)

# Fit weighted Cox model with FH(0,0.5) weights
fit <- cox_rhogamma(
  dfcount = dfcount,
  scheme = "fh",
  scheme_params = list(rho = 0, gamma = 0.5),
  draws = 1000,
  verbose = TRUE
)

print(fit$cox_text_star)  # Resampling-based CI
print(fit$zlogrank_text)  # Weighted log-rank test

# Compare asymptotic and resampling CIs
print(fit$hr_ci_asy)
print(fit$hr_ci_star)

weightedsurv documentation built on Dec. 23, 2025, 1:07 a.m.