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Find Sample Size for RMST with Covariate-Dependent Censoring (Analytic)

Source: R/dependent_censoring_analytical.R
DC.ss.analytical.Rd

Iteratively finds required per-arm sample size for a target power, using the same IPCW-based analytic variance as DC.power.analytical.

Usage

DC.ss.analytical(
  pilot_data,
  time_var,
  status_var,
  arm_var,
  target_power,
  linear_terms = NULL,
  L,
  alpha = 0.05,
  n_start = 50,
  n_step = 25,
  max_n_per_arm = 2000
)

Arguments

pilot_data

A data.frame containing pilot study data.

time_var

Time variable.

status_var

Event indicator (1=event, 0=censored).

arm_var

Treatment indicator (1/0).

target_power

Desired power.

linear_terms

Optional covariates used in both models.

L

RMST truncation time.

alpha

Two-sided Type I error.

n_start

Starting per-arm N.

n_step

Step size for search.

max_n_per_arm

Maximum per-arm N to search.

Value

A list with:

results_data

data.frame with Target_Power and Required_N_per_Arm.

results_plot

ggplot showing the search path.

results_summary

data.frame summarizing the pilot arm effect.

Details

Uses a single censoring Cox model Surv(time, status==0) ~ linear_terms to form IPCW and fits a weighted RMST regression. Treatment is excluded from the censoring model by default. Competing risks are not modeled. Variance ignores uncertainty in \(\hat G\).

Note: dep_cens_status_var is accepted for API compatibility but ignored here.