Simulate enrollment times

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))

Examples

enrollment(lambda = c(0.003, 0.7), N_total = 100, lambda_time = c(0, 10))
#>   [1]   0   0   9   9   9  11  12  14  14  14  15  17  18  19  23  26  27  27
#>  [19]  30  31  32  33  35  36  37  37  37  38  38  38  43  45  47  50  52  54
#>  [37]  56  56  57  58  67  68  68  70  71  75  75  78  80  80  80  80  82  83
#>  [55]  83  84  86  87  90  91  95  96  97  98  99 100 101 101 103 103 104 104
#>  [73] 104 104 105 107 108 109 110 110 111 113 113 114 114 117 121 122 122 123
#>  [91] 123 124 125 126 129 129 131 131 131 131
enrollment(lambda = c(0.3, 0.5, 0.9, 1.2, 2.1), N_total = 200,
           lambda_time = c(0, 20, 30, 40, 60))
#>   [1]   0   4   5  15  16  16  17  18  20  21  21  21  22  25  26  29  29  29
#>  [19]  30  31  32  33  35  36  38  38  40  40  42  44  44  46  46  46  47  48
#>  [37]  49  49  49  50  50  52  52  52  53  53  54  55  55  55  55  55  55  56
#>  [55]  57  57  57  58  60  61  61  61  61  62  63  65  65  65  65  65  66  67
#>  [73]  67  67  68  69  69  69  69  70  71  71  71  71  71  71  72  72  73  73
#>  [91]  73  74  74  74  75  75  75  76  76  77  79  79  79  80  80  81  81  81
#> [109]  82  83  83  83  83  83  84  84  84  85  85  85  85  86  86  86  86  86
#> [127]  86  87  88  88  88  89  89  91  91  92  92  93  93  94  94  94  94  95
#> [145]  95  96  96  96  96  96  98  98  98  99 100 100 100 100 101 101 101 102
#> [163] 102 102 103 103 103 104 104 105 106 106 107 107 108 108 108 109 109 110
#> [181] 110 111 111 111 111 111 112 114 114 115 116 117 117 117 117 117 118 118
#> [199] 118 119

Usage

Arguments

Value

Details

See also

Examples