df_counting: Weighted and Stratified Survival Analysis

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

Weighted and Stratified Survival Analysis

Description

Performs comprehensive weighted and/or stratified survival analysis, including Cox proportional hazards model, logrank/Fleming-Harrington tests, and calculation of risk/event sets, Kaplan-Meier curves, quantiles, and variance estimates.

Usage

df_counting(
  df,
  tte.name = "tte",
  event.name = "event",
  treat.name = "treat",
  weight.name = NULL,
  strata.name = NULL,
  arms = c("treat", "control"),
  time.zero = 0,
  tpoints.add = c(0),
  by.risk = 6,
  time.zero.label = 0,
  risk.add = NULL,
  get.cox = TRUE,
  cox.digits = 2,
  lr.digits = 2,
  cox.eps = 0.001,
  lr.eps = 0.001,
  verbose = FALSE,
  qprob = 0.5,
  scheme = "fh",
  scheme_params = list(rho = 0, gamma = 0),
  conf_level = 0.95,
  check.KM = TRUE,
  check.seKM = FALSE,
  draws = 0,
  seedstart = 8316951,
  stop.onerror = FALSE,
  censoring_allmarks = TRUE
)

Arguments

df	Data frame containing survival data.
tte.name	Character; name of the time-to-event column in df.
event.name	Character; name of the event indicator column in df (1=event, 0=censored).
treat.name	Character; name of the treatment/group column in df (must be coded as 0=control, 1=experimental).
weight.name	Character or NULL; name of the weights column in df. If NULL, equal weights are used.
strata.name	Character or NULL; name of the strata column in df for stratified analysis.
arms	Character vector of length 2; group labels. Default: c("treat","control").
time.zero	Numeric; time value to use as zero. Default: 0.
tpoints.add	Numeric vector; additional time points to include in calculations. Default: c(0).
by.risk	Numeric; interval for risk set time points. Default: 6.
time.zero.label	Numeric; label for time zero in output. Default: 0.0.
risk.add	Numeric vector or NULL; additional specific risk points to include.
get.cox	Logical; whether to fit Cox proportional hazards model. Default: TRUE.
cox.digits	Integer; number of decimal places for Cox output formatting. Default: 2.
lr.digits	Integer; number of decimal places for logrank output formatting. Default: 2.
cox.eps	Numeric; threshold for Cox p-value formatting (values below shown as "<eps"). Default: 0.001.
lr.eps	Numeric; threshold for logrank p-value formatting. Default: 0.001.
verbose	Logical; whether to print warnings and diagnostic messages. Default: FALSE.
qprob	Numeric in (0,1); quantile probability for KM quantile table. Default: 0.5 (median).
scheme	Character; weighting scheme for logrank/Fleming-Harrington test. Options: "fh", "schemper", "XO", "MB", "custom_time", "fh_exp1", "fh_exp2". Default: "fh".
scheme_params	List; parameters for the selected weighting scheme. Default: list(rho = 0, gamma = 0). For "fh": rho and gamma (Fleming-Harrington parameters) For "MB": mb_tstar (cutoff time) For "custom_time": t.tau, w0.tau, w1.tau
conf_level	Numeric in (0,1); confidence level for quantile intervals. Default: 0.95.
check.KM	Logical; whether to check KM curve validity against survival::survfit. Default: TRUE.
check.seKM	Logical; whether to check KM standard error estimates. Default: FALSE.
draws	Integer; number of draws for resampling-based variance estimation. Default: 0 (no resampling).
seedstart	Integer; random seed for reproducible resampling. Default: 8316951.
stop.onerror	Logical; whether to stop execution on errors (TRUE) or issue warnings (FALSE). Default: FALSE.
censoring_allmarks	Logical; if FALSE, removes events from censored time points. Default: TRUE.

Details

This function implements a comprehensive survival analysis framework supporting:

• Weighted observations via weight.name

• Stratified analysis via strata.name

• Multiple weighting schemes for log-rank tests

• Resampling-based variance estimation

• Automatic validation against survival package results

The function performs time-fixing using survival::aeqSurv to handle tied event times. For stratified analyses, stratum-specific estimates are computed and combined appropriately.

Value

A list with the following components:

cox_results

List with Cox model results including hazard ratio, confidence interval, p-value, and formatted text

logrank_results

List with log-rank test chi-square statistic, p-value, and formatted text

z.score

Standardized weighted log-rank test statistic

at_points

Vector of all time points used in calculations

surv0, surv1

Kaplan-Meier survival estimates for control and treatment groups

sig2_surv0, sig2_surv1

Variance estimates for survival curves

survP

Pooled survival estimates

survG

Censoring distribution estimates

quantile_results

Data frame with median survival and confidence intervals by group

lr, sig2_lr

Weighted log-rank statistic and its variance

riskpoints0, riskpoints1

Risk set counts at specified time points

z.score_stratified

Stratified z-score (if stratified analysis)

Weighting Schemes

fh

Fleming-Harrington: w(t) = S(t)^rho * (1-S(t))^gamma

MB

Magirr-Burman: w(t) = 1/max(S(t), S(t*))

schemper

Schemper: w(t) = S(t)/G(t) where G is the censoring distribution

XO

Xu-O'Quigley: w(t) = S(t)/Y(t) where Y is risk set size

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.

See Also

coxph, survdiff, survfit cox_rhogamma for weighted Cox models KM_diff for Kaplan-Meier differences

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

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

Examples

# Basic survival analysis
library(survival)
str(veteran)
veteran$treat <- as.numeric(veteran$trt) - 1

result <- df_counting(
  df = veteran,
  tte.name = "time",
  event.name = "status",
  treat.name = "treat",
  arms = c("Treatment", "Control")
)

# Print results
print(result$cox_results$cox_text)
print(result$zlogrank_text)

# Fleming-Harrington (0,1) weights (emphasizing late differences)
result_fh <- df_counting(
  df = veteran,
  tte.name = "time",
  event.name = "status",
  treat.name = "treat",
  scheme = "fh",
  scheme_params = list(rho = 0, gamma = 1)
)

# Stratified analysis
result_strat <- df_counting(
  df = veteran,
  tte.name = "time",
  event.name = "status",
  treat.name = "treat",
  strata.name = "celltype"
)

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