A showcase of Bayesian clinical trial designs, implemented in 'RStan' and R. Some designs are implemented in R for the first time (e.g. 'EffTox' by Thall and Cook, 2004, < https://biostatistics.mdanderson.org/softwaredownload/SingleSoftware.aspx?Software_Id=2>). Given the emphasis on flexibility in Bayesian analysis, the implementations in a common language and style may serve as a cookbook to encourage the use of Bayesian methods in trials. Compiled 'RStan' models are provided in addition to helper classes and functions to perform simulations and inference on observed trial outcomes. There is a preponderance of early phase trial designs because this is where Bayesian methods are used most. If there is a published Bayesian design you want implemented in 'Stan', get in touch.

`trialr`

is a collection of Bayesian clinical trial designs implemented
in Stan and R.

Many notable Bayesian designs for clinical trials have been published. However, one of the factors that has constrained their adoption is availability of software. We present here some of the most popular, implemented and demonstrated in a consistent style, leveraging the powerful Stan environment.

It should be stressed that Bayesian trialists are not generally without code. Often authors make available code with their design publication. There are also some fantastic packages that aid the use of certain designs. However, challenges to use still persist. The disparate methods are naturally presented in a style that appeals to the particular author. Features implemented in one package for one design may be missing in another. Sometimes the technology chosen may only be available on one particular operating system, or the chosen technology may have fallen into disuse.

`trialr`

seeks to address these problems. Models are specified in
Stan, a state-of-the-art environment for Bayesian
analysis. It uses Hamiltonian Monte Carlo to take samples from the
posterior distributions. This method is more efficient than Gibbs
sampling, for instance, and reliable inference can be performed on a few
thousand posterior samples. R, Stan and `trialr`

are each available on
Mac, Linux, and Windows, so all of the examples presented here should
work on each operating system. Furthermore, Stan offers a very simple
method to split the sampling across *n* cores, taking full advantage of
the modern multicore processor in your computer (probably).

The designs implemented in `trialr`

are introduced briefly below, and
developed more fully in vignettes. We focus on real-life usage,
including:

- fitting models to observed data using your prior;
- processing posterior samples to produce useful inferences;
- and visualising inferences using modern
`ggplot`

graphics.

In all examples, we will need to load `trialr`

library(trialr)

The Continual Reassessment Method (CRM) was first published by
@OQuigley1990. It assumes a smooth mathematical form for the
dose-toxicity curve to conduct a dose-finding trial seeking a maximum
tolerable dose. There are many variations to suit different clinical
scenarios and the design has enjoyed *relatively* common use (although
nowhere near as common as the ubiquitous and inferior 3+3 design).

We will demonstrate the method using a notional trial example. In a scenario of five potential doses, let us assume that we seek the dose with probability of toxicity closest to 25% where our prior guesses of the rates of toxicity can be represented:

target <- 0.25skeleton <- c(0.05, 0.15, 0.25, 0.4, 0.6)

Let us assume that we have already treated 2 patients each at doses 2, 3 and 4, having only seen toxicity at dose-level 4. What dose should we give to the next patient or cohort? We can fit the data to the popular empiric model

mod1 <- stan_crm(outcome_str = '2NN 3NN 4TT', skeleton = skeleton,target = target, model = 'empiric', beta_sd = sqrt(1.34),seed = 123)

The fitted model contains lots of useful of information:

mod1#> Patient Dose Toxicity#> 1 1 2 0#> 2 2 2 0#> 3 3 3 0#> 4 4 3 0#> 5 5 4 1#> 6 6 4 1#>#> DoseLevel Skeleton N Tox ProbTox ProbMTD#> 1 1 0.05 0 0 0.1081169 0.21400#> 2 2 0.15 2 0 0.2159618 0.27175#> 3 3 0.25 2 0 0.3098591 0.26575#> 4 4 0.40 2 2 0.4444842 0.20900#> 5 5 0.60 0 0 0.6235105 0.03950#>#> The model targets a toxicity level of 0.25.#> The dose with estimated toxicity probability closest to target is 2.#> The dose most likely to be the MTD is 2.

library(ggplot2)plot_df = data.frame(DoseLevel = 1:length(skeleton),ProbTox = mod1$prob_tox)ggplot(plot_df, aes(x = DoseLevel, y = ProbTox)) +geom_point() + geom_line() + ylim(0, 1) +geom_hline(yintercept = target, col = 'orange', linetype = 'dashed') +labs(title = 'Posterior dose-toxicity curve under empiric CRM model')

Several variants of the CRM are implemented in ‘trialr’. Further visualisation techniques are demonstrated in the Visualisation in CRM vignette.

EffTox by @Thall2004 is a dose-finding design that uses binary efficacy and toxicity outcomes to select a dose with a high utility score. We present it briefly here but there is a much more thorough examination in the EffTox vignette.

For demonstration, We fit the model parameterisation introduced by @Thall2014 to the following notional outcomes:

Patient | Dose-level | Toxicity | Efficacy |
---|---|---|---|

1 | 1 | 0 | 0 |

2 | 1 | 0 | 0 |

3 | 1 | 0 | 1 |

4 | 2 | 0 | 1 |

5 | 2 | 0 | 1 |

6 | 2 | 1 | 1 |

outcomes <- '1NNE 2EEB'mod <- stan_efftox_demo(outcomes, seed = 123)#> trying deprecated constructor; please alert package maintainer

mod#> Patient Dose Toxicity Efficacy#> 1 1 1 0 0#> 2 2 1 0 0#> 3 3 1 0 1#> 4 4 2 0 1#> 5 5 2 0 1#> 6 6 2 1 1#>#> DoseLevel ProbEff ProbTox ProbAccEff ProbAccTox Utility#> 1 1 0.4045039 0.08990953 0.33175 0.92700 -0.3397885#> 2 2 0.7917219 0.09875146 0.94575 0.92250 0.4237935#> 3 3 0.9313427 0.21522248 0.98475 0.72900 0.5249445#> 4 4 0.9572788 0.30606939 0.98475 0.62925 0.4380717#> 5 5 0.9657038 0.36255571 0.98350 0.57725 0.3685257#> Acceptable#> 1 TRUE#> 2 TRUE#> 3 TRUE#> 4 FALSE#> 5 FALSE#>#> The model recommends selecting dose-level 3.

In this instance, after evaluation of our six patients, the dose advocated for the next group is dose-level 3. This is contained in the fitted object:

mod$recommended_dose#> [1] 3

This is not surprising because dose 3 has the highest utility score:

mod$utility#> [1] -0.3397885 0.4237935 0.5249445 0.4380717 0.3685257

Sometimes, doses other than the maximal-utility dose will be recommended because of the dose-admissibility rules. See the papers for details.

Functions are provided to create useful plots. For instance, it is illuminating to plot the posterior means of the probabilities of efficacy and toxicity at each of the doses on the trade-off contours. The five doses are shown in red. Doses closer to the lower-right corner have higher utility.

efftox_contour_plot(mod$dat, prob_eff = mod$prob_eff, prob_tox = mod$prob_tox)title('EffTox utility contours')

This example continues in the EffTox vignette.

There are many publications related to EffTox but the two most important are @Thall2004 and @Thall2014.

Sticking with Peter Thall’s huge contribution to Bayesian clinical trials, @Thall2003 described a method for analysing treatment effects of a single intervention in several sub-types of a single disease.

We demonstrate the method for partially-pooling response rates to a single drug in various subtypes of sarcoma. The following convenience function returns the necessary data:

dat <- thallhierarchicalbinary_parameters_demo()dat#> $m#> [1] 10#>#> $x#> [1] 0 0 1 3 5 0 1 2 0 0#>#> $n#> [1] 0 2 1 7 5 0 2 3 1 0#>#> $target_resp#> [1] 0.3#>#> $mu_mean#> [1] -1.3863#>#> $mu_sd#> [1] 3.162278#>#> $tau_alpha#> [1] 2#>#> $tau_beta#> [1] 20

Fitting the data to the model:

samp <- rstan::sampling(stanmodels$ThallHierarchicalBinary, data = dat,seed = 123)#> trying deprecated constructor; please alert package maintainer

library(dplyr)library(tidyr)as.data.frame(samp, 'p') %>%gather(Cohort, ProbResponse) %>%ggplot(aes(x = Cohort, y = ProbResponse, group = Cohort)) +geom_boxplot() + geom_hline(yintercept = 0.3, col = 'orange', linetype = 'dashed') +labs(title = 'Partially-pooled analysis of response rate in 10 sarcoma subtypes')

The hierarchical model for binary responses is developed in its own vignette.

@Thall2008 introduced an extension of EffTox that allows dose-finding by
efficacy and toxicity outcomes and adjusts for covariate information.
Brock, et al. simplified the method by removing the dose-finding
components to leave a design that studies associated co-primary and
toxicity outcomes in an arbitrary number of cohorts determined by the
basline covariates. They refered to the simplifed design as BEBOP, for
*Bayesian Evaluation of Bivariate binary Outcomes with Predictive
variables*.

The investigators implement the design is a phase II trial of pembrolizumab in non-small-cell lung cancer. A distinct feature of the trial is the availability of predictive baseline covariates, the most notwworthy of which is the PD-L1 tumour proportion score, shown by @Garon2015 to be a predictive biomarker.

This example is demonstrated in the BEBOP vignette.

You can install trialr from github with:

# install.packages("devtools")devtools::install_github("brockk/trialr")

If the latest CRAN build is what you seek then instead run:

install.packages("trialr")

It should go without saying that the CRAN release will be older than the github version.

If there is a published Bayesian design you want implemented in Stan, get in touch. Contact @brockk on github.

Garon, Edward B, Naiyer a Rizvi, Rina Hui, Natasha Leighl, Ani S Balmanoukian, Joseph Paul Eder, Amita Patnaik, et al. 2015. “Pembrolizumab for the treatment of non-small-cell lung cancer.” The New England Journal of Medicine 372 (21): 2018–28. doi:10.1056/NEJMoa1501824.

O’Quigley, J, M Pepe, and L Fisher. 1990. “Continual reassessment method: a practical design for phase 1 clinical trials in cancer.” Biometrics 46 (1): 33–48. doi:10.2307/2531628.

Thall, Peter F., Hoang Q. Nguyen, and Elihu H. Estey. 2008. “Patient-specific dose finding based on bivariate outcomes and covariates.” Biometrics 64 (4): 1126–36. doi:10.1111/j.1541-0420.2008.01009.x.

Thall, Peter F., J. Kyle Wathen, B. Nebiyou Bekele, Richard E. Champlin, Laurence H. Baker, and Robert S. Benjamin. 2003. “Hierarchical Bayesian approaches to phase II trials in diseases with multiple subtypes.” Statistics in Medicine 22 (5): 763–80. doi:10.1002/sim.1399.

Thall, PF, and JD Cook. 2004. “Dose-Finding Based on Efficacy-Toxicity Trade-Offs.” Biometrics 60 (3): 684–93.

Thall, PF, RC Herrick, HQ Nguyen, JJ Venier, and JC Norris. 2014. “Effective sample size for computing prior hyperparameters in Bayesian phase I-II dose-finding.” Clinical Trials 11 (6): 657–66. doi:10.1177/1740774514547397.

Updated to use rstan 2.18.1, which in-turn has been updated to use C++14.

This release updates some Stan code that was generating C++ code that would compile on Linux, Mac & Windows, but not Solaris.

This release adds the Continual Reassessment Method (CRM) for dose-finding. Four model variants are currently given:

- empiric likelihood with normal prior on slope;
- logistic likelihood with constant intercept and normal prior on slope;
- logistic likelihood with constant intercept and gamma prior on slope;
- logistic likelihood with normal priors on the intercept and slope.

This release also adds general model-fitting functions stan_crm and stan_efftox. It also adds S3 classes crm_fit and efftox_fit with the expected generic methods.

Unit tests have been added.

First release with implementations of:

- Thall & Cook's EffTox dose-finding clinical trial design;
- Thall et al.'s hierarchical Bayesian phase II design for diseases with multiple subtypes
- Brock et al.'s BEBOP design for efficacy and toxicity outcomes in phase II where predictive information