dreamer_mcmc {dreamer}

Bayesian Model Averaging of Dose Response Models

Description

This function performs Bayesian model averaging with a selection of dose response models. See model for all possible models.

Usage

dreamer_mcmc(
  data,
  ...,
  n_adapt = 1000,
  n_burn = 1000,
  n_iter = 10000,
  n_chains = 4,
  silent = FALSE,
  convergence_warn = TRUE
)

Arguments

data

a dataframe with column names of "dose" and "response" for individual patient data. Optional columns "n" and "sample_var" can be specified if aggregate data is supplied, but it is recommended that patient-level data be supplied where possible for continuous models, as the posterior weights differ if aggregated data is used. For aggregated continuous data, "response" should be the average of "n" subjects with a sample variance of "sample_var". For aggregated binary data, "response" should be the number of successes, "n" should be the total number of subjects (the "sample_var" column is irrelevant in binary cases and is ignored).

...

model definitions created using the model creation functions in model. If arguments are named, the names are retained in the return values.

n_adapt

the number of MCMC iterations to tune the MCMC algorithm.

n_burn

the number of burn-in MCMC samples.

n_iter

the number of MCMC samples to collect after tuning and burn-in.

n_chains

the number of separate, independent, MCMC chains to run.

silent

logical indicating if MCMC progress bars should be suppressed.

convergence_warn

logical (default TRUE) indicating if the Gelman-Rubin diagnostics should be run to detect convergence issues. Warnings are thrown if the upper bound of the Gelman-Rubin statistic is greater than 1.1.

Details

The Bayesian model averaging approach uses data, multiple models, priors on each model's parameters, and a prior weight for each model. Using these inputs, each model is fit independently, and the output from the models is used to calculate posterior weights for each model. See Gould (2018) for details.

Value

A named list with S3 class "dreamer_bma" and "dreamer". The list contains the following fields:

• doses: a vector of the unique ordered doses in the data.

• times: a vector of the unique ordered times in the data.

• w_prior: a named vector with the prior probabilities of each model.

• w_post: a named vector with the posterior probabilities of each model.

• The individual MCMC fits for each model.

References

Gould, A. L. (2019). BMA-Mod: A Bayesian model averaging strategy for determining dose-response relationships in the presence of model uncertainty. Biometrical Journal, 61(5), 1141-1159.

Examples

set.seed(888)
data <- dreamer_data_linear(
  n_cohorts = c(20, 20, 20),
  dose = c(0, 3, 10),
  b1 = 1,
  b2 = 3,
  sigma = 5
)

# Bayesian model averaging
output <- dreamer_mcmc(
  data = data,
  n_adapt = 1e3,
  n_burn = 1e2,
  n_iter = 1e3,
  n_chains = 2,
  silent = TRUE,
  mod_linear = model_linear(
    mu_b1 = 0,
    sigma_b1 = 1,
    mu_b2 = 0,
    sigma_b2 = 1,
    shape = 1,
    rate = .001,
    w_prior = 1 / 2
  ),
  mod_quad = model_quad(
    mu_b1 = 0,
    sigma_b1 = 1,
    mu_b2 = 0,
    sigma_b2 = 1,
    mu_b3 = 0,
    sigma_b3 = 1,
    shape = 1,
    rate = .001,
    w_prior = 1 / 2
  )
)
# posterior weights
output$w_post
# plot posterior dose response
plot(output)

# LONGITUDINAL
library(ggplot2)
set.seed(889)
data_long <- dreamer_data_linear(
  n_cohorts = c(10, 10, 10, 10), # number of subjects in each cohort
  doses = c(.25, .5, .75, 1.5), # dose administered to each cohort
  b1 = 0, # intercept
  b2 = 2, # slope
  sigma = .5, # standard deviation,
  longitudinal = "itp",
  times = c(0, 12, 24, 52),
  t_max = 52, # maximum time
  a = .5,
  c1 = .1
)

## Not run: 
ggplot(data_long, aes(time, response, group = dose, color = factor(dose))) +
   geom_point()

## End(Not run)

output_long <- dreamer_mcmc(
   data = data_long,
   n_adapt = 1e3,
   n_burn = 1e2,
   n_iter = 1e3,
   n_chains = 2,
   silent = TRUE, # make rjags be quiet,
   mod_linear = model_linear(
      mu_b1 = 0,
      sigma_b1 = 1,
      mu_b2 = 0,
      sigma_b2 = 1,
      shape = 1,
      rate = .001,
      w_prior = 1 / 2, # prior probability of the model
      longitudinal = model_longitudinal_itp(
         mu_a = 0,
         sigma_a = 1,
         a_c1 = 0,
         b_c1 = 1,
         t_max = 52
      )
   ),
   mod_quad = model_quad(
      mu_b1 = 0,
      sigma_b1 = 1,
      mu_b2 = 0,
      sigma_b2 = 1,
      mu_b3 = 0,
      sigma_b3 = 1,
      shape = 1,
      rate = .001,
      w_prior = 1 / 2,
      longitudinal = model_longitudinal_linear(
         mu_a = 0,
         sigma_a = 1,
         t_max = 52
      )
   )
)

## Not run: 
# plot longitudinal dose-response profile
plot(output_long, data = data_long)
plot(output_long$mod_quad, data = data_long) # single model

# plot dose response at final timepoint
plot(output_long, data = data_long, times = 52)
plot(output_long$mod_quad, data = data_long, times = 52) # single model

## End(Not run)