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BayesERtools BayesERtools website

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BayesERtools provides a suite of tools that facilitate exposure-response analysis using Bayesian methods.

Installation

You can install the BayesERtools with:

install.packages('BayesERtools')
# devtools::install_github("genentech/BayesERtools") # development version

Supported model types

Binary endpoint
Continuous endpoint
Linear (logit) Emax (logit) Linear Emax
backend rstanarm rstanemax rstanarm rstanemax
reference 🔗 🔗 🔗 🔗
develop model ✅ ✅ ✅ ✅
simulate & plot ER ✅ ✅ ✅ ✅
exposure metrics selection ✅ ✅ ✅ ✅
covariate selection ✅ ❌ ✅ ❌
covariate forest plot ✅ ❌ ✅ ❌
✅ Available, 🟡 In plan/under development, ❌ Not in a current plan

Quick guide

Here is a quick demo on how to use this package for E-R analysis. See Basic workflow for more thorough walk through.

# Load package and data
library(dplyr)
library(BayesERtools)
ggplot2::theme_set(ggplot2::theme_bw(base_size = 12))

data(d_sim_binom_cov)

# Hyperglycemia Grade 2+ (hgly2) data
df_er_ae_hgly2 <-
  d_sim_binom_cov |>
  filter(AETYPE == "hgly2") |>
  # Re-scale AUCss, baseline age
  mutate(
    AUCss_1000 = AUCss / 1000, BAGE_10 = BAGE / 10,
    Dose = paste(Dose_mg, "mg")
  )

var_resp <- "AEFLAG"

Simple univariable model for binary endpoint

set.seed(1234)
ermod_bin <- dev_ermod_bin(
  data = df_er_ae_hgly2,
  var_resp = var_resp,
  var_exposure = "AUCss_1000"
)
ermod_bin
#> 
#> ── Binary ER model ─────────────────────────────────────────────────────────────
#> ℹ Use `plot_er()` to visualize ER curve
#> 
#> ── Developed model ──
#> 
#> stan_glm
#>  family:       binomial [logit]
#>  formula:      AEFLAG ~ AUCss_1000
#>  observations: 500
#>  predictors:   2
#> ------
#>             Median MAD_SD
#> (Intercept) -2.04   0.23 
#> AUCss_1000   0.41   0.08 
#> ------
#> * For help interpreting the printed output see ?print.stanreg
#> * For info on the priors used see ?prior_summary.stanreg

# Using `*` instead of `+` so that scale can be
# applied for both panels (main plot and boxplot)
plot_er_gof(ermod_bin, var_group = "Dose", show_coef_exp = TRUE) *
  xgxr::xgx_scale_x_log10(guide = ggplot2::guide_axis(minor.ticks = TRUE))
#> Warning in ggplot2::annotate("label", x = pos_ci_annot[1], y = pos_ci_annot[2],
#> : Ignoring unknown parameters: `label.size`
#> Warning: annotation$theme is not a valid theme.
#> Please use `theme()` to construct themes.

Covariate selection

BGLUC (baseline glucose) is selected while other two covariates are not.

set.seed(1234)
ermod_bin_cov_sel <-
  dev_ermod_bin_cov_sel(
    data = df_er_ae_hgly2,
    var_resp = var_resp,
    var_exposure = "AUCss_1000",
    var_cov_candidate = c("BAGE_10", "RACE", "BGLUC")
  )
#> 
#> ── Step 1: Full reference model fit ──
#> 
#> ── Step 2: Variable selection ──
#> 
#> ℹ The variables selected were: AUCss_1000, BGLUC
#> 
#> ── Step 3: Final model fit ──
#> 
#> ── Cov mod dev complete ──
#> 
ermod_bin_cov_sel
#> ── Binary ER model & covariate selection ───────────────────────────────────────
#> ℹ Use `plot_submod_performance()` to see variable selection performance
#> ℹ Use `plot_er()` with `marginal = TRUE` to visualize marginal ER curve
#> 
#> ── Selected model ──
#> 
#> stan_glm
#>  family:       binomial [logit]
#>  formula:      AEFLAG ~ AUCss_1000 + BGLUC
#>  observations: 500
#>  predictors:   3
#> ------
#>             Median MAD_SD
#> (Intercept) -7.59   0.90 
#> AUCss_1000   0.46   0.08 
#> BGLUC        0.87   0.13 
#> ------
#> * For help interpreting the printed output see ?print.stanreg
#> * For info on the priors used see ?prior_summary.stanreg
plot_submod_performance(ermod_bin_cov_sel)

coveffsim <- sim_coveff(ermod_bin_cov_sel)
plot_coveff(coveffsim)
#> Warning in geom_errorbar(..., orientation = orientation): Ignoring unknown
#> parameters: `height`

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.