BayesDIP

R package: Bayesian Decreasingly Informative Priors for Early Termination Phase II Trials

https://github.com/chenw10/bayesdip

Repository

R package: Bayesian Decreasingly Informative Priors for Early Termination Phase II Trials

README.Rmd

---
title: "README"
output: github_document
---



```{r setup, include = FALSE}
knitr::opts_chunk$set(
  echo = TRUE,
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# BayesDIP

Provide early termination phase II trial designs with a decreasingly informative prior (DIP) 
or a regular Bayesian prior chosen by the user. The program can determine the minimum 
planned sample size necessary to achieve the user-specified admissible designs. The program 
can also perform power and expected sample size calculations for the tests in early 
termination Phase II trials.[1]

## Installation

You  can install from CRAN with:
```{r eval = FALSE}
install.packages("BayesDIP")
```

Or try the development version from [GitHub] with:
```{r eval = FALSE}
# install.packages("devtools")
devtools::install_github("chenw10/BayesDIP")
```

## Example
```{r example}
library(BayesDIP)

# Calculate the minimum planned sample size within the range 10<=N<=100,
# under an admissible design which is set as 80% power and 5% type I error here.

# One sample Bernoulli model with the response rate for the new treatment is 0.5, 
# the null response rate is 0.3, and the target improvement to achieve is 0.
# The alternative hypothesis: p1 > p0 + d
# Simulate 10 replicate trials using this design with efficacy boundary 0.98 
# and futility boundary 0.05.

### Designs with traditional Bayesian prior Beta(1,1)
### Designs and operating characteristics based on 100 simulations:
OneSampleBernoulli.Design(list(2,1,1), nmin = 10, nmax=100, p0 = 0.3, p1 = 0.5, d = 0,
                          ps = 0.98, pf = 0.02, power = 0.80, t1error=0.05, alternative = "greater",
                          seed = 202210, sim = 100)


### Designs with DIP
### Designs and operating characteristics based on 10 simulations:
OneSampleBernoulli.Design(list(1,0,0), nmin = 10, nmax=100, p0 = 0.3, p1 = 0.5, d = 0,
                          ps = 0.98, pf = 0.02, power = 0.80, t1error=0.05, alternative = "greater",
                          seed = 202210, sim = 100)


# Calculate the power, type I error and the expected sample size given a planned sample size

# One sample Bernoulli model with the response rate for the new treatment is 0.5, 
# the null response rate is 0.3, and the target improvement to achieve is 0.05.
# The alternative hypothesis: p1 > p0 + d
# Simulate 100 replicate trials for a given planned sample size 100 using this design
# with efficacy boundary 0.98 and futility boundary 0.05.  

## with traditional Bayesian prior Beta(1,1)
## Operating characteristics based on 100 simulations:
OneSampleBernoulli(list(2,1,1), N = 100, p0 = 0.3, p1 = 0.5, d = 0.05,
                   ps = 0.98, pf = 0.05, alternative = "greater",
                   seed = 202210, sim = 100)


## with DIP
## Operating characteristics based on 100 simulations:
OneSampleBernoulli(list(1,0,0), N = 100, p0 = 0.3, p1 = 0.5, d = 0.05,
                   ps = 0.98, pf = 0.05, alternative = "greater",
                   seed = 202210, sim = 100)





```

## Reference

[1] Wang C, Sabo RT, Mukhopadhyay ND, and Perera RA. Early termination in single-parameter model phase II clinical trial designs using decreasingly informative priors. \textit{International Journal of Clinical Trials}, 9(2): April - June 2022. https://doi.org/10.18203/2349-3259.ijct20221110

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