basket

Basket designs are prospective clinical trials that are devised with the hypothesis that the presence of selected molecular features determine a patient’s subsequent response to a particular “targeted” treatment strategy. Basket trials are designed to enroll multiple clinical subpopulations to which it is assumed that the therapy in question offers beneficial efficacy in the presence of the targeted molecular profile. The treatment, however, may not offer acceptable efficacy to all subpopulations enrolled. Moreover, for rare disease settings, such as oncology wherein these trials have become popular, marginal measures of statistical evidence are difficult to interpret for sparsely enrolled subpopulations. Consequently, basket trials pose challenges to the traditional paradigm for trial design, which assumes inter-patient exchangeability. The R-package basket facilitates the analysis of basket trials by implementing multi-source exchangeability models. By evaluating all possible pairwise exchangeability relationships, this hierarchical modeling framework facilitates Bayesian posterior shrinkage among a collection of discrete and pre-specified subpopulations.

Installation

You can install the released version of basket from CRAN with:

install.packages("basket")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("kaneplusplus/basket")

Example

The “Vemurafenib in multiple nonmelanoma cancers with BRAF V600 mutations” study enrolled patients into predetermined baskets that were determined by organ site with primary end point defined by Response Evaluation Criteria in Solid Tumors (RECIST), version 1.1 or the criteria of the International Myeloma Working Group (IMWG). Statistical evidence for preliminary clinical efficacy was obtained through estimation of the organ-specific objective response rates at 8 weeks following the initiation of treatment. This section demonstrates the implementation of through analysis of six organs comprising non–small-cell lung cancer (NSCLC), cholangiocarcinoma (Bile Duct), Erdheim–Chester disease or Langerhans’-cell histiocytosis (ECD or LCH), anaplastic thyroid cancer (ATC), and colorectal cancer (CRC) which formed two cohorts. Patients with CRC were initially administered vemurafenib. The study was later amended to evaluate vemurafenib in combination with cetuximab for CRC which comprised a new basket. Observed outcomes are summarized below. Included in the package, the dataset is accessible in short vemu_wide as well as long formats vemu.

library(basket)
data(vemu_wide)
vemu_wide
#> # A tibble: 6 × 7
#>   baskets         enrolled evaluable responders one_or_fewer_pr… two_prior_thera…
#>   <chr>              <dbl>     <dbl>      <dbl>            <dbl>            <dbl>
#> 1 NSCLC                 20        19          8               11                4
#> 2 CRC (vemu)            10        10          0                1                2
#> 3 CRC (vemu+cetu)       27        26          1                5               11
#> 4 Bile Duct              8         8          1                2                1
#> 5 ECD or LCH            18        14          6                9                7
#> 6 ATC                    7         7          2                5                1
#> # … with 1 more variable: three_or_more_therapies <dbl>

Inspection of Table reveals heterogeneity among the studied baskets. CRC (vemu), CRC (vemu+cetu), and Bile Duct had relatively low response rates when compared to other baskets, suggesting that patients presenting the BRAF V600 mutation may not yield exchangeable information for statistical characterization of the effectiveness of the targeted therapy. Therefore, the MEM framework is implemented to measure the extent of basketwise heterogeneity and evaluate the effectiveness of the targeted therapy on the basis of its resultant multi-resolution smoothed posterior distributions. This case study reports posterior probabilities evaluating the evidence that the response probability for each organ-site exceeds the null rate of p0 = 0.25.

An analysis of the trial data can be reproduced by loading the vemu_wide data, which is included with the package. The data set includes the number of evaluable patients (column evaluable), the number of responding patients (column responders), and the associated baskets for the respective results (column baskets). The model is fit by passing these values to the basket() function along with an argument specifying the null response rate of 0.25 for evaluation of each basket. A visualization of the posterior distribution of the response rates can be created with the following and shows that the Bile Duct and CRC cohorts are similar and do not respond to treatment where ATC, ECD or LCH, and NSCLC do respond.

data(vemu_wide)
vm <- basket(vemu_wide$responders, vemu_wide$evaluable,
vemu_wide$baskets, p0 = 0.25)
plot_density(vm, type = "basket")
#> Warning: It is deprecated to specify `guide = FALSE` to remove a guide. Please
#> use `guide = "none"` instead.

Code of conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.