Simulate from ER model

Usage

sim_er(
  ermod,
  newdata = NULL,
  n_draws_sim = NULL,
  seed_sample_draws = NULL,
  output_type = c("draws", "median_qi"),
  qi_width = 0.95,
  .nrow_cov_data = NULL
)

Arguments

ermod: An object of class ermod
newdata: New data to use for simulation. Default is NULL (use the data in the model object).
n_draws_sim: Number of draws for simulation. If NULL (default), all draws in the model object are used.
seed_sample_draws: Seed for sampling draws. Default is NULL.
output_type: Type of output. "draws" returns the raw draws from the simulation, and "median_qi" returns the median and quantile interval.
qi_width: Width of the quantile interval. Default is 0.95. Only used when output_type = "median_qi".
.nrow_cov_data: Number of rows in the covariate data, used for internal purposes. Users should not set this argument.

Value

ersim object, which is a tibble with the simulated responses with some additional information in object attributes. It has three types of predictions - .linpred, .epred, .prediction. .linpred and .epred are similar in a way that they both represent "expected response", i.e. without residual variability. They are the same for models with continuous endpoits (Emax model). For models with binary endpoints, .linpred is the linear predictor (i.e. on the logit scale) and .epred is on the probability scale. .prediction is the predicted response with residual variability (or in case of binary endpoint, the predicted yes (1) or no (0) for event occurrence). See tidybayes::add_epred_draws() for more details.

In case of output_type = "median_qi", it returns ersim_med_qi object.

Examples

# \donttest{
data(d_sim_binom_cov_hgly2)

ermod_bin <- dev_ermod_bin(
  data = d_sim_binom_cov_hgly2,
  var_resp = "AEFLAG",
  var_exposure = "AUCss_1000",
  var_cov = "BHBA1C_5",
)

ersim <- sim_er(
  ermod_bin,
  n_draws_sim = 500, # This is set to make the example run faster
  output_type = "draws"
)

ersim_med_qi <- sim_er(
  ermod_bin,
  n_draws_sim = 500, # This is set to make the example run faster
  output_type = "median_qi"
)

ersim
#> # A tibble: 250,000 × 24
#> # Groups:   ID, AETYPE, AEFLAG, Dose_mg, AUCss, Cmaxss, Cminss, BAGE, BWT,
#> #   BGLUC, BHBA1C, RACE, VISC, AUCss_1000, BAGE_10, BWT_10, BHBA1C_5, .row
#> #   [500]
#>       ID AETYPE AEFLAG Dose_mg AUCss Cmaxss Cminss  BAGE   BWT BGLUC BHBA1C
#>    <int> <chr>   <int>   <dbl> <dbl>  <dbl>  <dbl> <dbl> <dbl> <dbl>  <dbl>
#>  1     1 hgly2       0     200  866.   64.3   10.1  84.4  74.1  4.65   31.5
#>  2     1 hgly2       0     200  866.   64.3   10.1  84.4  74.1  4.65   31.5
#>  3     1 hgly2       0     200  866.   64.3   10.1  84.4  74.1  4.65   31.5
#>  4     1 hgly2       0     200  866.   64.3   10.1  84.4  74.1  4.65   31.5
#>  5     1 hgly2       0     200  866.   64.3   10.1  84.4  74.1  4.65   31.5
#>  6     1 hgly2       0     200  866.   64.3   10.1  84.4  74.1  4.65   31.5
#>  7     1 hgly2       0     200  866.   64.3   10.1  84.4  74.1  4.65   31.5
#>  8     1 hgly2       0     200  866.   64.3   10.1  84.4  74.1  4.65   31.5
#>  9     1 hgly2       0     200  866.   64.3   10.1  84.4  74.1  4.65   31.5
#> 10     1 hgly2       0     200  866.   64.3   10.1  84.4  74.1  4.65   31.5
#> # ℹ 249,990 more rows
#> # ℹ 13 more variables: RACE <chr>, VISC <fct>, AUCss_1000 <dbl>, BAGE_10 <dbl>,
#> #   BWT_10 <dbl>, BHBA1C_5 <dbl>, .row <int>, .chain <int>, .iteration <int>,
#> #   .draw <int>, .epred <dbl>, .linpred <dbl>, .prediction <int>
ersim_med_qi
#> # A tibble: 500 × 27
#>       ID AETYPE AEFLAG Dose_mg AUCss Cmaxss Cminss  BAGE   BWT BGLUC BHBA1C
#>    <int> <chr>   <int>   <dbl> <dbl>  <dbl>  <dbl> <dbl> <dbl> <dbl>  <dbl>
#>  1     1 hgly2       0     200  866.   64.3   10.1  84.4  74.1  4.65   31.5
#>  2     2 hgly2       0     200 1707.  166.    27.3  59.1  88.2  7.24   41.9
#>  3     3 hgly2       0     200  746.   68.2   15.8  64.3  88.0  5.73   47.7
#>  4     4 hgly2       0     200 1984.  287.    37.2  65.5 114.   5.26   23.4
#>  5     5 hgly2       0     200 2045.  275.    53.1  67.5  64.6  4.49   43.1
#>  6     6 hgly2       0     200  632.   47.3   12.0  67.0  73.0  6.41   43.6
#>  7     7 hgly2       0     200 2274.  136.    33.6  73.7  63.6  7.22   35.9
#>  8     8 hgly2       0     200 1347.  130.    21.7  59.9  99.7  5.34   36.2
#>  9     9 hgly2       0     200 1101.   60.7   11.2  65.8  82.3  5.47   36.0
#> 10    10 hgly2       0     200 1822.  179.    49.3  65.5  62.3  5.70   33.7
#> # ℹ 490 more rows
#> # ℹ 16 more variables: RACE <chr>, VISC <fct>, AUCss_1000 <dbl>, BAGE_10 <dbl>,
#> #   BWT_10 <dbl>, BHBA1C_5 <dbl>, .row <int>, .epred <dbl>, .epred.lower <dbl>,
#> #   .epred.upper <dbl>, .linpred <dbl>, .linpred.lower <dbl>,
#> #   .linpred.upper <dbl>, .width <dbl>, .point <chr>, .interval <chr>
# }

Simulate from ER model

Usage

Arguments

Value

See also

Examples