Simulate longitudinal case follow-up data from a homogeneous population

Usage

sim_case_data(
  n,
  curve_params,
  antigen_isos = get_biomarker_levels(curve_params),
  max_n_obs = 10,
  dist_n_obs = tibble::tibble(n_obs = 1:max_n_obs, prob = 1/max_n_obs),
  followup_interval = 7,
  followup_variance = 1
)

Arguments

n: integer number of cases to simulate
curve_params: a curve_params object from serocalculator::as_curve_params, assumed to be unstratified
antigen_isos: character vector: which antigen isotypes to simulate
max_n_obs: maximum number of observations
dist_n_obs: distribution of number of observations (tibble::tbl_df)
followup_interval: integer
followup_variance: integer

Value

a case_data object

Examples

set.seed(1)
serocalculator::typhoid_curves_nostrat_100 |>
  sim_case_data(n = 100)
#> # A tibble: 3,020 × 11
#>    id    visit_num timeindays  iter antigen_iso    y0     y1    t1   alpha     r
#>  * <chr>     <int>      <dbl> <int> <fct>       <dbl>  <dbl> <dbl>   <dbl> <dbl>
#>  1 1             1          0    83 HlyE_IgA    1.33   50.8   2.60 2.68e-3  1.54
#>  2 1             1          0    83 HlyE_IgG    3.49  265.    6.08 1.53e-3  1.24
#>  3 1             1          0    83 LPS_IgA     0.878   4.69  3.06 9.84e-4  2.42
#>  4 1             1          0    83 LPS_IgG     1.64  300.    2.35 7.28e-4  1.60
#>  5 1             1          0    83 Vi_IgG      1.30  264.    8.02 5.46e-5  1.26
#>  6 1             2          7    83 HlyE_IgA    1.33   50.8   2.60 2.68e-3  1.54
#>  7 1             2          7    83 HlyE_IgG    3.49  265.    6.08 1.53e-3  1.24
#>  8 1             2          7    83 LPS_IgA     0.878   4.69  3.06 9.84e-4  2.42
#>  9 1             2          7    83 LPS_IgG     1.64  300.    2.35 7.28e-4  1.60
#> 10 1             2          7    83 Vi_IgG      1.30  264.    8.02 5.46e-5  1.26
#> # ℹ 3,010 more rows
#> # ℹ 1 more variable: value <dbl>