OncoBayes2 (version 0.9-3)

example-single-agent: Single Agent Example

Description

Example using a single experimental drug.

Details

The single agent example is described in the reference Neuenschwander, B. et al (2008). The data are described in the help page for hist_SA. In this case, the data come from only one study, with the treatment being only single agent. Hence the model specified does not involve a hierarchical prior for the intercept and log-slope parameters. The model described in Neuenschwander, et al (2008) is adapted as follows: $$\text{logit}\, \pi(d) = \log\, \alpha + \beta \, \log\, \Bigl(\frac{d}{d^*}\Bigr),$$ where \(d^* = 250\), and the prior for \(\boldsymbol\theta = (\log\, \alpha, \log\, \beta)\) is $$\boldsymbol\theta \sim \text{N}(\boldsymbol m, \boldsymbol S),$$ and \(\boldsymbol m = (\text{logit}\, 0.5, \log\, 1)\) and \(\boldsymbol S = \text{diag}(2^2, 1^2)\) are constants.

The above model is non-hierarchical. To disable the hierarchical model structure of the blrm_exnex framework, the user can specify the option prior_tau_dist=NULL. This will internally set all the heterogeniety parameters (\(\tau^2_\alpha\) and \(\tau^2_\beta\)) to zero.

References

Neuenschwander, B., Branson, M., & Gsponer, T. (2008). Critical aspects of the Bayesian approach to phase I cancer trials. Statistics in medicine, 27(13), 2420-2439.

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 from Neuenschwander, B., et al. (2009). Stats in Medicine

dref <- 50

## Since there is no prior information the hierarchical model
## is not used in this example by setting tau to (almost) 0.
blrmfit <- blrm_exnex(
  cbind(num_toxicities, num_patients - num_toxicities) ~
      1 + log(drug_A / dref) |
      0 |
      group_id,
  data = hist_SA,
  prior_EX_mu_comp = mixmvnorm(c(1, logit(1 / 2), log(1), diag(c(2^2, 1)))),
  ## Setting prior_tau_dist=NULL disables the hierarchical prior which is
  ## not required in this example as we analyze a single trial.
  prior_tau_dist = NULL,
  prior_PD = FALSE
)
## Recover user set sampling defaults
options(.user_mc_options)