OncoBayes2 (version 0.6-0)

hist_combo3: 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) 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

stratum for group_id's used for differential discounting

Examples

# NOT RUN {
## 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)

## example combo3

library(RBesT)
library(abind)

dref <- c(500, 500, 1000)
num_comp <- 3
num_inter <- choose(3,2) + 1
num_strata <- nlevels(hist_combo3$stratum)
num_groups <- nlevels(hist_combo3$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/group_id,
                      data=hist_combo3,
                      prior_EX_mu_mean_comp=matrix(c(logit(1/3), 0), nrow=num_comp, ncol=2, TRUE),
                      prior_EX_mu_sd_comp=matrix(c(2, 1), nrow=num_comp, ncol=2, TRUE),
          prior_EX_tau_mean_comp=abind(matrix(log(  c(0.25, 0.125)), nrow=num_comp, ncol=2, TRUE),
                                       matrix(log(2*c(0.25, 0.125)), nrow=num_comp, ncol=2, TRUE),
                                       along=0),
                      prior_EX_tau_sd_comp=abind(matrix(log(4)/1.96, nrow=num_comp, ncol=2, TRUE),
                                                 matrix(log(4)/1.96, nrow=num_comp, ncol=2, TRUE),
                                                 along=0),
                      prior_EX_mu_mean_inter=rep(0, num_inter),
                      prior_EX_mu_sd_inter=rep(sqrt(2)/2, num_inter),
                      prior_EX_tau_mean_inter=matrix(log(0.25)  , nrow=num_strata, ncol=num_inter),
                      prior_EX_tau_sd_inter=matrix(log(2)/1.96, nrow=num_strata, ncol=num_inter),
                      prior_EX_prob_comp=matrix(0.9, nrow=num_groups, ncol=num_comp),
                      prior_EX_prob_inter=matrix(0.9, nrow=num_groups, ncol=num_inter),
                      ## by default EXNEX is on for components and off for all interactions
                      prior_tau_dist=1,
                      prior_PD=FALSE
                      )
## Recover user set sampling defaults
options(.user_mc_options)

# }