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Simulated exposure-response data

Source: R/data.R
erplots_data.Rd

A simulated dataset with multiple exposure columns and response columns spanning all three response types, designed to demonstrate every layer of the erplots mini-language without depending on any companion model-fitting package for the data itself.

Usage

erplots_data

Format

A tibble with 4,000 rows (one per simulated subject) and 15 columns:

subject_id

Integer subject identifier, 1:4000.

dose_mg

Numeric assigned dose in mg: one of 0, 10, 30, 100, 300.

dose_group

Ordered factor version of dose_mg ("Placebo" < "10 mg" < "30 mg" < "100 mg" < "300 mg"), about 800 subjects per level. A natural stratify_by/grouping column.

study_id

Factor, "Study 1"-"Study 4" (400/800/1200/1600 subjects respectively). A purely administrative label, independent of dose/exposure/response by construction – see Details.

bodyweight_kg

Numeric bodyweight covariate.

age_years

Numeric age covariate.

sex

Factor covariate, "F"/"M".

renal_function

Factor covariate, "Normal"/"Mild"/"Moderate".

auc_ss

Numeric exposure: steady-state AUC (cumulative exposure). 0 for placebo subjects.

cmax_ss

Numeric exposure: steady-state peak concentration.

cmin_ss

Numeric exposure: steady-state trough concentration.

biomarker_change

Continuous response, Emax-shaped in auc_ss.

responder

Binary (0/1) response, Emax-shaped on the logit scale in cmax_ss.

adverse_event

Binary (0/1) response, log-linear (plain logistic regression, no saturation) in auc_ss.

symptom_score

Continuous response, linear in cmin_ss.

n_events

Integer count response, log-linear Poisson rate in auc_ss.

Source

Simulated; see data-raw/erplots_data.R for the full generating code.

Details

The three exposure columns (auc_ss, cmax_ss, cmin_ss) come from a simplified, internally-consistent PK-flavoured simulation (individual clearance driven by bodyweight_kg/renal_function, with between-subject variability) rather than a literal pharmacokinetic model – good enough to produce a plausible, correlated exposure triple, not a validated PK simulator.

Each response column is paired with the exposure column and mechanism that makes it a natural fit for one modelling scenario:

ResponseExposureScenario
biomarker_changeauc_ssEmax (continuous)
respondercmax_ssEmax (binary)
adverse_eventauc_sslogistic regression
symptom_scorecmin_sslinear regression
n_eventsauc_ssPoisson regression

At 4,000 rows, a raw-point data-layer overlay (er_style_data_overlay()) visibly overplots – see the relevant example below, which uses er_style_data_hex() instead.

study_id ("Study 1"-"Study 4", unevenly sized: 400/800/1200/1600 subjects) is included purely as a convenient filtering column: it's independent of dose, exposure, and response by construction, so subsetting to a single study (e.g. dplyr::filter(erplots_data, study_id == "Study 1")) gives a much smaller sample that still spans the full dose range – useful for illustrating how the same plot looks with less data (e.g. whether a raw-point overlay is legible again once N drops, or whether er_style_data_hex()'s bins become too sparse to be useful).

Examples

erplots_data
#> # A tibble: 4,000 × 16
#>    subject_id dose_mg dose_group study_id bodyweight_kg age_years sex  
#>         <int>   <dbl> <ord>      <fct>            <dbl>     <dbl> <fct>
#>  1          1       0 Placebo    Study 4           47.9        34 F    
#>  2          2       0 Placebo    Study 1           51.5        47 F    
#>  3          3       0 Placebo    Study 3           74.2        61 F    
#>  4          4       0 Placebo    Study 2           76.5        38 F    
#>  5          5       0 Placebo    Study 3           40          49 M    
#>  6          6       0 Placebo    Study 2           57.5        25 M    
#>  7          7       0 Placebo    Study 4           70.5        36 F    
#>  8          8       0 Placebo    Study 3           69          48 F    
#>  9          9       0 Placebo    Study 3           56.7        62 M    
#> 10         10       0 Placebo    Study 1           81.9        62 M    
#> # ℹ 3,990 more rows
#> # ℹ 9 more variables: renal_function <fct>, auc_ss <dbl>, cmax_ss <dbl>,
#> #   cmin_ss <dbl>, biomarker_change <dbl>, responder <int>,
#> #   adverse_event <int>, symptom_score <dbl>, n_events <int>

# Logistic regression: adverse_event ~ auc_ss
if (requireNamespace("erglm", quietly = TRUE)) {
  mod <- erglm::erglm_model(adverse_event ~ auc_ss, data = erplots_data, family = binomial())
  erplots_data |>
    er_plot(auc_ss, adverse_event) |>
    er_plot_add_model(mod) |>
    er_plot_add_summary(model = mod) |>
    plot()
}


# Linear regression: symptom_score ~ cmin_ss
if (requireNamespace("erglm", quietly = TRUE)) {
  mod <- erglm::erglm_model(symptom_score ~ cmin_ss, data = erplots_data, family = gaussian())
  erplots_data |>
    er_plot(cmin_ss, symptom_score) |>
    er_plot_add_model(mod) |>
    plot()
}


# Linear regression: symptom_score ~ cmin_ss, with a hex-binned data layer
if (requireNamespace("erglm", quietly = TRUE) && requireNamespace("hexbin", quietly = TRUE)) {
  mod <- erglm::erglm_model(symptom_score ~ cmin_ss, data = erplots_data, family = gaussian())
  erplots_data |>
    er_plot(cmin_ss, symptom_score) |>
    er_plot_add_model(mod) |>
    er_plot_add_data(style = er_style_data_hex) |>
    plot()
}


# Filtering to one study (n = 400) for a smaller-sample illustration: a
# Poisson regression n_events ~ auc_ss with scatter plot data layer
if (requireNamespace("erglm", quietly = TRUE)) {
  small_data <- erplots_data[erplots_data$study_id == "Study 1", ]
  mod <- erglm::erglm_model(n_events ~ auc_ss, data = small_data, family = poisson())
  small_data |>
    er_plot(auc_ss, n_events, response_type = "count") |>
    er_plot_add_model(mod) |>
    er_plot_add_data() |>
    plot()
}