wt.rg.S: Compute Time-Dependent Weights for Survival Analysis

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

Compute Time-Dependent Weights for Survival Analysis

Description

Calculates time-dependent weights for survival analysis according to various schemes (Fleming-Harrington, Schemper, XO, MB, custom).

Usage

wt.rg.S(
  S,
  scheme = c("fh", "schemper", "XO", "MB", "custom_time", "fh_exp1", "fh_exp2"),
  rho = NULL,
  gamma = NULL,
  Scensor = NULL,
  Ybar = NULL,
  tpoints = NULL,
  t.tau = NULL,
  w0.tau = 0,
  w1.tau = 1,
  mb_tstar = NULL,
  details = FALSE
)

Arguments

S	Numeric vector of survival probabilities at each time point.
scheme	Character; weighting scheme. One of: 'fh', 'schemper', 'XO', 'MB', 'custom_time', 'fh_exp1', 'fh_exp2'.
rho	Numeric; rho parameter for Fleming-Harrington weights. Required if scheme='fh'.
gamma	Numeric; gamma parameter for Fleming-Harrington weights. Required if scheme='fh'.
Scensor	Numeric vector; censoring KM curve for Schemper weights. Must have same length as S.
Ybar	Numeric vector; risk set sizes for XO weights. Must have same length as S.
tpoints	Numeric vector; time points corresponding to S. Required for MB and custom_time schemes.
t.tau	Numeric; cutoff time for custom_time weights.
w0.tau	Numeric; weight before t.tau for custom_time weights.
w1.tau	Numeric; weight after t.tau for custom_time weights.
mb_tstar	Numeric; cutoff time for Magirr-Burman weights.
details	Logical; if TRUE, returns list with weights and diagnostic info. Default: FALSE.

Details

This function implements several weighting schemes for weighted log-rank tests: See vignette for details This function implements several weighting schemes for weighted log-rank tests:

Fleming-Harrington (fh)

w(t) = S(t-)^rho * (1-S(t-))^gamma. See vignette for common choices.

Schemper

w(t) = S(t-)/G(t-) where G is the censoring distribution.

Xu-O'Quigley (XO)

w(t) = S(t-)/Y(t) where Y is risk set size.

Magirr-Burman (MB)

w(t) = 1/max(S(t-), S(t*)).

Custom time

Step function with weight w_0 before t* and w_1 after t*.

Value

If details=FALSE (default): Numeric vector of weights with length equal to S.

If details=TRUE: List containing:

weights

Numeric vector of calculated weights

S

Input survival probabilities

S_left

Left-continuous survival probabilities

scheme

Scheme name

Note

All weights are calculated using left-continuous survival probabilities S(t-) to ensure consistency with counting process notation.

References

Fleming, T. R. and Harrington, D. P. (1991). Counting Processes and Survival Analysis. Wiley.

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

Schemper, M., Wakounig, S., and Heinze, G. (2009). The estimation of average hazard ratios by weighted Cox regression. Statistics in Medicine, 28(19), 2473-2489.

See Also

Other survival_analysis: KM_diff(), cox_rhogamma(), df_counting()

Other weighted_tests: cox_rhogamma(), df_counting()

Examples

# Generate example survival curve
time <- seq(0, 24, by = 0.5)
surv <- exp(-0.05 * time)  # Exponential survival

# Fleming-Harrington (0,1) weights
w_fh01 <- wt.rg.S(surv, scheme = "fh", rho = 0, gamma = 1, tpoints = time)

# Magirr-Burman weights
w_mb <- wt.rg.S(surv, scheme = "MB", mb_tstar = 12, tpoints = time)

# Standard log-rank (equal weights)
w_lr <- wt.rg.S(surv, scheme = "fh", rho = 0, gamma = 0)

# Plotting examples
plot(time, w_fh01, type = "l", main = "FH(0,1) Weights")
plot(time, w_mb, type = "l", main = "MB(12) Weights")

# Compare multiple schemes
plot(time, w_lr, type = "l", ylim = c(0, 2))
lines(time, w_fh01, col = "blue")
legend("topleft", c("Log-rank", "FH(0,1)"), col = 1:2, lty = 1)

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