hist_combo3: Dataset: historical and concurrent data on a three-way...

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

Dataset: historical and concurrent data on a three-way combination

Description

This dataset involves a hypothetical dose-escalation study of combination therapy with three treatment components. From two previous studies HistAgent1 and HistAgent2, historical data is available on each of the treatments as single-agents, as well as two of the two-way combinations. However, due to a difference in treatment schedule between the Combo study and the historical studies, a stratification (through stratum_id) is made between the groups to allow differential discounting of the alternate-dose data.

Usage

hist_combo3

Format

A data frame with 18 rows and 7 variables:

group_id

study

drug_A

dose of Drug A

drug_B

dose of Drug B

drug_C

dose of Drug C

num_patients

number of patients

num_toxicities

number of DLTs

stratum_id

stratum for group_id's used for differential discounting

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
)

## example combo3

library(abind)

dref <- c(500, 500, 1000)
num_comp <- 3
num_inter <- choose(3, 2) + 1
num_strata <- nlevels(hist_combo3$stratum_id)
num_groups <- nlevels(hist_combo3$group_id)

blrmfit <- blrm_exnex(
  cbind(num_toxicities, num_patients - num_toxicities) ~
    1 + I(log(drug_A / dref[1])) |
      1 + I(log(drug_B / dref[2])) |
      1 + I(log(drug_C / dref[3])) |
      0
      + I(drug_A / dref[1] * drug_B / dref[2])
        + I(drug_A / dref[1] * drug_C / dref[3])
        + I(drug_B / dref[2] * drug_C / dref[3])
        + I(drug_A / dref[1] * drug_B / dref[2] * drug_C / dref[3]) |
      stratum_id / group_id,
  data = hist_combo3,
  prior_EX_mu_comp = replicate(num_comp, mixmvnorm(c(1, logit(1/3), 0, diag(c(2^2, 1)))), FALSE),
  prior_EX_tau_comp = list(replicate(num_comp,
                                     mixmvnorm(c(1, log(c(0.25, 0.125)),
                                               diag(c(log(4)/1.96, log(4)/1.96)^2))), FALSE),
                           replicate(num_comp,
                                     mixmvnorm(c(1, log(2 * c(0.25, 0.125)),
                                               diag(c(log(4)/1.96, log(4)/1.96)^2))), FALSE)),
  prior_EX_mu_inter = mixmvnorm(c(1, rep.int(0, num_inter),
                                     diag((rep.int(sqrt(2) / 2, num_inter))^2))),
  prior_EX_tau_inter = replicate(num_strata,
                                 mixmvnorm(c(1, rep.int(log(0.25), num_inter),
                                             diag((rep.int(log(2) / 1.96, num_inter))^2))), FALSE),
  prior_EX_prob_comp = matrix(0.9, nrow = num_groups, ncol = num_comp),
  prior_EX_prob_inter = matrix(1.0, nrow = num_groups, ncol = num_inter),
  prior_is_EXNEX_comp = rep(TRUE, num_comp),
  prior_is_EXNEX_inter = rep(FALSE, num_inter),
  prior_tau_dist = 1,
  prior_PD = FALSE
)
## Recover user set sampling defaults
options(.user_mc_options)

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