Simulate from ER model at specified exposure values

Simulate from ER model at specified exposure values

Usage

sim_er_new_exp(
  ermod,
  exposure_to_sim_vec = NULL,
  data_cov = NULL,
  n_draws_sim = NULL,
  seed_sample_draws = NULL,
  output_type = c("draws", "median_qi"),
  qi_width = 0.95
)

sim_er_curve(
  ermod,
  exposure_range = NULL,
  num_exposures = 51,
  data_cov = NULL,
  n_draws_sim = NULL,
  seed_sample_draws = NULL,
  output_type = c("draws", "median_qi"),
  qi_width = 0.95
)

Arguments

ermod

An object of class ermod

exposure_to_sim_vec

Vector of exposure values to simulate.

data_cov

Data frame containing covariates to use for simulation, see details below.

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".

exposure_range

Range of exposure values to simulate. If NULL (default), it is set to the range of the exposure variable in the original data for model development.

num_exposures

Number of exposure values to simulate.

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.

Details

Simulation dataset will be all combinations of covariates in data_cov and exposure values in exposure_to_sim_vec, so the run time can become very long if data_cov has many rows.

data_cov has to be supplied if ermod is a model with covariates. It is recommended that data_cov contains subject identifiers such as ID for post-processing.

Exposure values in data_cov will be ignored.

sim_er_curve() is a wrapper function for sim_er_new_exp() that use a range of exposure values to simulate the expected responses. Particularly useful for plotting the exposure-response curve.

See also

calc_ersim_med_qi() for calculating median and quantile interval from ersim object (generated with output_type = "draws").

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_new_exp_med_qi <- sim_er_new_exp(
  ermod_bin,
  exposure_to_sim_vec = seq(2, 6, by = 0.2),
  data_cov = dplyr::tibble(BHBA1C_5 = 4:10),
  n_draws_sim = 500, # This is set to make the example run faster
  output_type = "median_qi"
)

ersim_new_exp_med_qi
#> # A tibble: 147 × 12
#>    AUCss_1000 BHBA1C_5  .row .epred .epred.lower .epred.upper .linpred
#>         <dbl>    <int> <int>  <dbl>        <dbl>        <dbl>    <dbl>
#>  1        2          4     1 0.0231       0.0107       0.0465   -3.74 
#>  2        2          5     2 0.0411       0.0226       0.0710   -3.15 
#>  3        2          6     3 0.0708       0.0457       0.107    -2.57 
#>  4        2          7     4 0.120        0.0891       0.157    -1.99 
#>  5        2          8     5 0.198        0.160        0.237    -1.40 
#>  6        2          9     6 0.305        0.255        0.363    -0.824
#>  7        2         10     7 0.440        0.358        0.526    -0.242
#>  8        2.2        4     8 0.0252       0.0118       0.0507   -3.66 
#>  9        2.2        5     9 0.0445       0.0247       0.0768   -3.07 
#> 10        2.2        6    10 0.0771       0.0498       0.117    -2.48 
#> # ℹ 137 more rows
#> # ℹ 5 more variables: .linpred.lower <dbl>, .linpred.upper <dbl>, .width <dbl>,
#> #   .point <chr>, .interval <chr>
# }