gsDesignNB (version 0.2.6)

blinded_ssr: Blinded sample size re-estimation for recurrent events

Description

Estimates the blinded dispersion and event rate from aggregated interim data and calculates the required sample size to maintain power, assuming the planned treatment effect holds. This function supports constant rates (Friede & Schmidli 2010) and accommodates future extensions for time-varying rates (Schneider et al. 2013) by using the exposure-adjusted rate.

Usage

blinded_ssr(
  data,
  ratio = 1,
  lambda1_planning,
  lambda2_planning,
  rr0 = 1,
  power = 0.8,
  alpha = 0.025,
  method = "friede",
  accrual_rate,
  accrual_duration,
  trial_duration,
  dropout_rate = 0,
  max_followup = NULL,
  event_gap = NULL
)

Value

A list containing:

n_total_blinded

Re-estimated total sample size using blinded estimates.

dispersion_blinded

Estimated dispersion parameter (k) from blinded data.

lambda_blinded

Estimated overall event rate from blinded data.

info_fraction

Estimated information fraction at interim (blinded information / target information).

blinded_info

Estimated statistical information from the blinded interim data.

target_info

Target statistical information required for the planned power.

Arguments

data

A data frame containing the blinded interim data. Must include columns events (number of events) and tte (total exposure/follow-up time). This is typically the output of cut_data_by_date().

ratio

Planned allocation ratio (experimental / control). Default is 1.

lambda1_planning

Planned event rate for the control group used in original calculation.

lambda2_planning

Planned event rate for the experimental group used in original calculation.

rr0

Rate ratio under the null hypothesis (lambda2/lambda1). Default is 1.

power

Target power (1 - beta). Default is 0.8.

alpha

One-sided significance level. Default is 0.025.

method

Method for sample size recalculation. Currently "friede" (Friede & Schmidli 2010) is implemented, which uses the blinded nuisance parameter estimates.

accrual_rate

Vector of accrual rates (patients per unit time).

accrual_duration

Vector of durations for each accrual rate. Must be same length as accrual_rate.

trial_duration

Total planned duration of the trial.

dropout_rate

Dropout rate (hazard rate). Default is 0.

max_followup

Maximum follow-up time for any patient. Default is NULL (infinite).

event_gap

Gap duration after each event during which no new events are counted. Default is NULL (no gap).

References

Friede, T., & Schmidli, H. (2010). Blinded sample size reestimation with count data: methods and applications in multiple sclerosis. Statistics in Medicine, 29(10), 1145--1156. tools:::Rd_expr_doi("10.1002/sim.3861")

Schneider, S., Schmidli, H., & Friede, T. (2013). Blinded sample size re-estimation for recurrent event data with time trends. Statistics in Medicine, 32(30), 5448--5457. tools:::Rd_expr_doi("10.1002/sim.5977")

Examples

interim <- data.frame(events = c(1, 2, 1, 3), tte = c(0.8, 1.0, 1.2, 0.9))
blinded_ssr(
  interim,
  ratio = 1,
  lambda1_planning = 0.5,
  lambda2_planning = 0.3,
  power = 0.8,
  alpha = 0.025,
  accrual_rate = 10,
  accrual_duration = 12,
  trial_duration = 18
)