example-combo2_trial: Two-drug combination example using BLRM Trial

In OncoBayes2: Bayesian Logistic Regression for Oncology Dose-Escalation Trials

Two-drug combination example using BLRM Trial

Description

Example using blrm_trial to guide the built-in two-drug combination study example.

Details

blrm_trial is used to collect and store all relevant design information for the example. Subsequent use of the update.blrm_trial command allows convenient model fitting via blrm_exnex. The summary.blrm_trial method allows exploration of the design and modeling results.

To run this example, use example_model("combo2_trial"). See example_model.

See Also

Other blrm_trial combo2 example: blrm_trial(), dose_info_combo2, drug_info_combo2

Examples

## Setting up dummy sampling for fast execution of example
## Please use 4 chains and 100x more warmup & iter in practice
.user_mc_options <- options(
  OncoBayes2.MC.warmup = 10, OncoBayes2.MC.iter = 20, OncoBayes2.MC.chains = 1,
  OncoBayes2.MC.save_warmup = FALSE
)

library(tibble)
library(dplyr)
library(tidyr)

# Combo2 example using blrm_trial functionality

# construct initial blrm_trial object from built-in example datasets
combo2_trial_setup <- blrm_trial(
  data = hist_combo2,
  dose_info = dose_info_combo2,
  drug_info = drug_info_combo2,
  simplified_prior = FALSE
)

# summary of dimensionality of data structures
dims <- summary(combo2_trial_setup, "dimensionality")

# Fit the initial model with the historical data and fully specified prior


combo2_trial_start <- update(
   combo2_trial_setup,
   ## bivariate normal prior for drug A and drug B of intercept and
   ## log-slope
   prior_EX_mu_comp =
     replicate(2,
               mixmvnorm(c(1,
                           logit(0.2), 0,
                           diag(c(2^2, 1))))
             , FALSE),
   prior_EX_tau_comp =
     replicate(2,
               mixmvnorm(c(1,
                           log(0.25), log(0.125),
                           diag(c(log(4)/1.96, log(4)/1.96)^2)))
             , FALSE),
   prior_EX_mu_inter = mixmvnorm(c(1, 0, 1.121^2)),
   prior_EX_tau_inter = mixmvnorm(c(1, log(0.125), (log(4) / 1.96)^2)),
   prior_is_EXNEX_comp = c(FALSE, FALSE),
   prior_is_EXNEX_inter = FALSE,
   prior_EX_prob_comp = matrix(1,
     nrow = dims$num_groups,
     ncol = 2
   ),
   prior_EX_prob_inter = matrix(1,
     nrow = nlevels(dose_info_combo2$group_id),
     ncol = 1
   ),
   prior_tau_dist = 1
 )

# print summary of prior specification
prior_summary(combo2_trial_start)

# summarize inference at observed dose levels
summary(combo2_trial_start, "data_prediction")

# summarize inference at specified dose levels
summary(combo2_trial_start, "dose_prediction")


# Update again with new data

# using update() with data argument supplied
# dem <- update(combo2_trial_start, data = codata_combo2)

# alternate way using update() with add_data argument for
# new observations only (those collected after the trial
# design stage).
new_data <- filter(codata_combo2, cohort_time > 0)

combo2_trial <- update(combo2_trial_start, add_data = new_data)

summary(combo2_trial, "data") # cohort_time is tracked
summary(combo2_trial, "data_prediction")
summary(combo2_trial, "dose_prediction")

rm(dims, new_data)

## Recover user set sampling defaults
options(.user_mc_options)

OncoBayes2 documentation built on April 11, 2025, 5:42 p.m.