goldilocks (version 0.3.0)

enrollment: Simulate enrollment times

Description

Simulate enrollment time using a piecewise Poisson distribution.

Usage

enrollment(lambda = 1, N_total, lambda_time = 0)

Arguments

lambda

vector. Rate parameter(s) for Poisson distribution.

N_total

integer. Value of total sample size.

lambda_time

vector. Knots (of length(lambda)) indicating regions where a specific hazard rate (lambda) applies. The first element is always lambda_time = 0, denoting the trial start time. Note: final element of lambda is assumed to be constant as lambda_time tends to infinity.

Value

A vector of enrollment times (from time of first patient enrollment) in unit time (e.g. days).

Details

Subject recruitment is assumed to follow a (piecewise stationary) Poisson process. We assume trial recruitment to be an independent process, thus the 'memoryless' property modelling of subject recruitment is used. Since the subject recruitment rate can vary over time, we can account for differential rates over time. Note that the first trial enrollment is assumed to occur at time zero.

To illustrate, suppose we use a piecewise function to specify the change in enrollment rate over time:

$$ \lambda = \left\{ \begin{array}{ll} 0.3 & \textrm{time} \in [0, 5) \\ 0.7 & \textrm{time} \in [5, 10) \\ 0.9 & \textrm{time} \in [10, 15) \\ 1.2 & \textrm{time} \in [15, \infty) \\ \end{array} \right. $$

Then, to simulate individual patient enrollment dates with a sample size (N_total) of 50, we use

enrollment(lambda = c(0.3, 0.7, 0.9, 1.2), N_total = 50, lambda_time = c(0, 5, 10, 15))

See Also

This function is based on the enrollment function from the bayesCT R package.

Examples

# NOT RUN {
enrollment(lambda = c(0.003, 0.7), N_total = 100, lambda_time = c(0, 10))
enrollment(lambda = c(0.3, 0.5, 0.9, 1.2, 2.1), N_total = 200,
           lambda_time = c(0, 20, 30, 40, 60))
# }