https://github.com/openpharma/mtdesign
Two-Stage Designs Optimal Under the Alternative Hypothesis for Phase II [Cancer] Clinical Trials
https://github.com/openpharma/mtdesign
Last synced: 6 months ago
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Two-Stage Designs Optimal Under the Alternative Hypothesis for Phase II [Cancer] Clinical Trials
- Host: GitHub
- URL: https://github.com/openpharma/mtdesign
- Owner: openpharma
- License: gpl-3.0
- Created: 2022-09-28T12:29:33.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-11-05T11:20:52.000Z (7 months ago)
- Last Synced: 2024-12-06T14:42:17.766Z (6 months ago)
- Language: R
- Homepage: https://openpharma.github.io/mtdesign
- Size: 6.95 MB
- Stars: 4
- Watchers: 2
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE.md
Awesome Lists containing this project
README
---
output: github_document
bibliography: "mtdesign.bib"
---
```{r, include = FALSE}
# /usr/local/lib/R/site-library/BH/include/boost
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# mtdesign
[](https://CRAN.R-project.org/package=mtdesign)
[](https://github.com/openpharma/mtdesign/blob/_xml_coverage_reports/data/main/coverage.xml)
## Introduction
The package `mtdesign` provides implementations of both Simon [-@SIMON] and Mander & Thompson [-@MANDER]. Other implementations of Simon's methods are available - for example, the `ph2simon` function in the `clinfun` package [@CLINFUN], but these do not provide easy access to non-optimal solutions in the way that `mtdesign` does. I am not aware of any other R-based implementations of Mander & Thompson's extension to Simon.
## Installation
Once available on CRAN, you can install `mtdesign` in the usual way:
`install.packages("mtdesign")`
You can install the development version of `mtdesign` from [GitHub](https://github.com/openpharma/mtdesign) with:
`devtools::install_github("openpharma/mtdesign")`
## Set up vignette environment
```{r}
# By policy, on CRAN, use only two cores, no matter how many are available.
if (requireNamespace("parallel", quietly = TRUE)) {
maxCores <- parallel::detectCores()
maxCores <- ifelse(identical(Sys.getenv("NOT_CRAN"), "true"), maxCores, min(maxCores, 2))
} else {
maxCores <- 1
}
```
## Example
Suppose that treatments with a response rate of less than 5% are of no interest but those with a response rate of at least 25% are worthy of further development. A Simon's 2-stage design to seek an efficacy signal with a significance level of 5% and a power of 80% is required.
```{r example}
library(mtdesign)
library(knitr)
library(dplyr)
simonDesign <- obtainDesign(p0 = 0.05, p1 = 0.25, alpha = 0.05, beta = 0.2, mander = FALSE, parallel = FALSE)
simonDesign %>%
select(-Alpha, -Beta, -p0, -p1, -PETAlt, -AveSizeAlt) %>%
kable(digits = c(0, 0, 0, 0, 3, 3, 2, 1, NA))
```
The table shows that the optimal design for these requirements is 0/9 2/17. The expected sample size is 12.0 and the probability of early termination is 63%. The significance level actually achieved is 4.7% and the power level achieved is 100% - 18.8% = 81.2%.
The power curves for both designs are easily plotted.
```{r}
powerPlot(simonDesign)
```
Obtaining the equivalent Mander & Thompson designs requires only a small change to the calls.
```{r}
manderDesign <- obtainDesign(
p0 = 0.05,
p1 = 0.25,
alpha = 0.05,
beta = 0.2,
cores = maxCores
)
manderDesign %>%
select(-Alpha, -Beta, -p0, -p1) %>%
kable(digits = c(0, 0, 0, 0, 3, 3, 2, 2, 2, 1, NA))
powerPlot(manderDesign)
```
### Constrained designs
Suppose a trial, for whatever reason, is restricted to using 8 participants in each stage. As shown above, the optimal Simon's two stage design is 0/9 2/17. That's close to n~1~ = 8, n = 16. Is there a (slightly) sub-optimal design that has n~1~ = 8, n = 16?
```{r}
x <- createGrid(p0 = 0.05, p1 = 0.25, alpha = 0.05, beta = 0.2, mander = FALSE)
y <- x %>% filter(nStage1 == 8, nTotal == 16)
z <- y %>% obtainDesign(cores = maxCores)
if (nrow(z) == 0) {
print("No acceptable designs were found.")
} else {
select(-Alpha, -Beta, -p0, -p1, -PETAlt, -AveSizeAlt) %>%
z() %>%
select(-Alpha, -Beta, -p0, -p1, -PETAlt, -AveSizeAlt) %>%
kable(digits = c(0, 0, 0, 0, 3, 3, 2, 1, NA))
}
```
No, there isn't. How close can we get?
```{r}
z1 <- y %>% augmentGrid()
bestSize <- z1 %>%
filter(Type1 < Alpha) %>%
slice_min(Type2)
bestSize %>%
select(-Alpha, -Beta, -p0, -p1, -PETAlt, -AveSizeAlt) %>%
kable(
caption = "Best sub-optimal design with required significance level",
digits = c(0, 0, 0, 0, 3, 3, 2, 1, NA)
)
bestPower <- z1 %>%
filter(Type2 < Beta) %>%
slice_min(Type1)
bestPower %>%
select(-Alpha, -Beta, -p0, -p1, -PETAlt, -AveSizeAlt) %>%
kable(
caption = "Best sub-optimal design with required power",
digits = c(0, 0, 0, 0, 3, 3, 2, 1, NA)
)
```
So the choice lies between a design which achieves the required significance level but has a power of only 77.1% or one which has the required power but which has a significance level of 15.1%. Both designs accept the null hypothesis when no responders are seen in the first group of eight participants. They differ in the critical value at the end of stage 2: 1 to maintain the power, 2 to maintain the significance level.
The power curve for each of these designs can be compared with that for the globally optimal design.
```{r}
plotData1 <- simonDesign %>%
filter(Criterion == "optimal") %>%
bind_rows(list(bestSize, bestPower))
powerPlot(plotData1)
```
## Package structure
The `mtdesign` package consists of three main functions:
* `createGrid` creates the grid (of nStage1, rFutility, nTotal and rTotal for Simon's design or nStage1, rFutility, rSuccess, nTotal and rTotal for a Mander & Thompson design) over which the brute force search for the required design(s) is conducted
* `augmentGrid`takes a grid created by `createGrid` and adds columns for probability of early termination, Type 1 error, Type 2 error and expected sample size to it.
* `obtainDesign` takes an augmented grid and identifies the optimal and minimax designs
## Error and warning messages and logging
The `mtdesign` package supports logging via the `futile.logger` package [@LOGGER]. Most functions simply report Entry and Exit at the `DEBUG` level.
The `augmentGrid` function reports steps of the parallelisation process at the `TRACE` level.
## Parallelisation
There is no known closed form solution to obtaining solutions to either Simon's original equations nor Mander & Thompson's extensions. The `mtdesign` package uses a brute force approach to evaluate the operating characteristics of all reasonable potential designs. The grids can be quickly become large, particularly for Mander & Thompson designs. For example, `createGrid(0.2, 0.4, alpha=0.1, beta=0.1)` creates a grid of almost 11 million candidate designs. `mtdesign` uses paralellisation to attempt to speed up the evaluation of candidate designs.
The `augmentGrid` function allows users some control over the parallelisation process:
* The `parallel` parameter defaults to `TRUE` and defines whether or not paralellisation is to be used.
* The `cores` parameter specifies how many cores are to be used. The default value, `NA` tells `mtdesign` to use all available (as defined by `parallel::detectCores()`), cores.
* The `minChunkSize` determines the smallest grid of candidate designs that will trigger paralellisation. The default value is `100000`.
The `parallel` package is required for parallelisation. If parallelisation is both needed (ie the grid size exceeds `minChunkSize`) and requested but the `parallel` package has not been installed, an error message is thrown and augmentation of the grid stops. If paralellisation is not requested and the grid contains one million or more rows, a warning is produced.
## Troubleshooting
If, when installing or using the `mtdesign` package, you get an error regarding a syntax error in an`.hpp` file, similar to the following
```R
.../BH/include/boost/math/tools/fraction.hpp:84:48: error: ‘long double’ is not a class, struct, or union type using value_type = typename T::value_type;
```
the issue is most likely a mismatch between the g++ compiler being used and the headers supplied by the `BH` package. There are only two solutions that I know of:
* Upgrade g++
* Downgrade the version of the `BH` package you are using. The appropriate package version depends on the version of the g++ compiler you are using.
## References