Generate a Deterministic Enrollment and Dropout Schedule

Creates a time-indexed schedule with piecewise-constant enrollment and dropout rates. Every call with the same inputs returns the same schedule, so all replicates follow an identical enrollment pattern.

Usage

deterministic_schedule(sample_size, arms, allocation, enrollment, dropout)

Arguments

sample_size: integer Trial sample size.
arms: character vector of arm identifiers.
allocation: numeric vector of allocation ratios.
enrollment: list with end_time (numeric period endpoints) and rate (subjects/unit time for each period).
dropout: list with end_time and rate (same structure).

Value

data.frame with columns: time (integer period), arm, enroll (subjects enrolled in that period), drop (subjects dropped). Aggregated counts - multiple subjects per row.

Details

Use this when you have a well-characterized operational plan and want to isolate endpoint and analysis variability from timing variability. For a stochastic (random) schedule see stochastic_schedule().

Examples

deterministic_schedule(
  sample_size = 100,
  arms = c("A", "B"),
  allocation = c(2, 1),
  enrollment = list(
    end_time = c(4, 8, 12),
    rate = c(6, 12, 18)
  ),
  dropout = list(
    end_time = c(5, 9, 13),
    rate = c(0, 3, 6)
  )
)
#> # A tibble: 20 × 4
#>     time arm   enroll  drop
#>    <int> <chr>  <int> <int>
#>  1     1 A          4     0
#>  2     2 A          4     0
#>  3     3 A          4     0
#>  4     4 A          4     0
#>  5     5 A          8     0
#>  6     6 A          8     2
#>  7     7 A          8     2
#>  8     8 A          8     2
#>  9     9 A         12     2
#> 10    10 A          7     2
#> 11     1 B          2     0
#> 12     2 B          2     0
#> 13     3 B          2     0
#> 14     4 B          2     0
#> 15     5 B          4     0
#> 16     6 B          4     1
#> 17     7 B          4     1
#> 18     8 B          4     1
#> 19     9 B          6     1
#> 20    10 B          3     1