The goal of goldilocks is to implement the Goldilocks Bayesian adaptive design proposed by Broglio et al. (2014) for time-to-event endpoint trials, both one- and two-arm, with an underlying piecewise exponential hazard model.
The method can be used for a confirmatory trial to select a trial’s sample size based on accumulating data. During accrual, frequent sample size selection analyses are made and predictive probabilities are used to determine whether the current sample size is sufficient or whether continuing accrual would be futile. The algorithm explicitly accounts for complete follow-up of all patients before the primary analysis is conducted. Final analysis tests include the log-rank test, Cox proportional hazards regression Wald test, and a Bayesian test that compares the absolute difference in cumulative incidence functions at a fixed time point.
Broglio et al. (2014) refer to this as a Goldilocks trial design, as it is constantly asking the question, “Is the sample size too big, too small, or just right?”
Other software and R packages are available to implement this algorithm. However, when designing studies it is generally required that many thousands of trials are simulated to adequately characterize the operating characteristics, e.g. type I error and power. Hence, a computationally efficient and fast algorithm is helpful. The goldilocks package takes advantage of many tools to achieve this:
Log-rank tests are implemented via the fastlogranktest package, which uses a lightweight C++ implementation
Piecewise exponential simulation is implemented via the PWEALL package, which uses a lightweight Fortran implementation
Simulation of multiple trials can be performed in parallel using the pbmcapply package
Broglio KR, Connor JT, Berry SM. Not too big, not too small: a Goldilocks approach to sample size selection. Journal of Biopharmaceutical Statistics, 2014; 24(3): 685–705.
You can install the development version of goldilocks GitHub with: