ad: Number of Patients Enrolled During an Interval and Having an...

In lrstat: Power and Sample Size Calculation for Non-Proportional Hazards and Beyond

Number of Patients Enrolled During an Interval and Having an Event by Specified Calendar Times

Description

Obtains the number of patients who are enrolled during a specified enrollment time interval and have an event by the specified calendar times.

Usage

ad(
  time = NA_real_,
  u1 = NA_real_,
  u2 = NA_real_,
  accrualTime = 0L,
  accrualIntensity = NA_real_,
  piecewiseSurvivalTime = 0L,
  lambda = NA_real_,
  gamma = 0L
)

Arguments

time	A vector of calendar times at which to calculate the number of patients having an event.
u1	Lower bound of the accrual time interval.
u2	Upper bound of the accrual time interval.
accrualTime	A vector that specifies the starting time of piecewise Poisson enrollment time intervals. Must start with 0, e.g., c(0, 3) breaks the time axis into 2 accrual intervals: [0, 3) and [3, Inf).
accrualIntensity	A vector of accrual intensities. One for each accrual time interval.
piecewiseSurvivalTime	A vector that specifies the starting time of piecewise exponential survival time intervals. Must start with 0, e.g., c(0, 6) breaks the time axis into 2 event intervals: [0, 6) and [6, Inf). Defaults to 0 for exponential distribution.
lambda	A vector of hazard rates for the event. One for each analysis time interval.
gamma	The hazard rate for exponential dropout, or a vector of hazard rates for piecewise exponential dropout.

Value

A vector of number of patients who are enrolled during a specified enrollment time interval and have an event by the specified calendar times for a given treatment group had the enrollment being restricted to the treatment group. By definition, we must have time >= u2.

Author(s)

Kaifeng Lu, [e-mail]

Examples

# Piecewise accrual, 10 patients per month for the first 3 months, and
# 20 patients per month thereafter. Piecewise exponential survival with
# hazard 0.0533 in the first 6 months, and hazard 0.0309 thereafter,
# and 5% dropout by the end of 1 year.

ad(time = c(9, 15), u1 = 1, u2 = 8, accrualTime = c(0, 3),
   accrualIntensity = c(10, 20), piecewiseSurvivalTime=c(0, 6),
   lambda = c(0.0533, 0.0309), gamma = -log(1-0.05)/12)

lrstat documentation built on Oct. 18, 2024, 9:06 a.m.