The goal of goldilocks is to implement the Goldilocks
Bayesian adaptive design proposed by Broglio et al. (2014), for both
one- and two-arm trials. Outcomes are generated from an underlying
piecewise-exponential event-time model. Final analyses may retain the
time-to-event outcome or reduce complete follow-up to event status at a
fixed endpoint time.
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. Time-to-event final analyses include the log-rank test, Cox proportional hazards regression Wald test, and Bayesian piecewise-exponential inference. Fixed-time binary final analyses include a frequentist risk-difference Wald test and Bayesian beta-binomial inference.
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?”
All designs use the piecewise-exponential event-time model for
simulation and predictive imputation. The method argument
selects the final analysis:
"logrank": log-rank test for a two-arm time-to-event
endpoint
"cox": Cox model Wald test for a two-arm
time-to-event endpoint
"bayes-surv": Bayesian piecewise-exponential
analysis for one- or two-arm time-to-event endpoints
"riskdiff": frequentist Wald test for a two-arm
fixed-time binary event-risk difference
"bayes-bin": Bayesian beta-binomial analysis for
one- or two-arm fixed-time binary endpoints
See the package vignettes for worked two-arm, single-arm, piecewise survival, and Bayesian binary examples.
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 a lightweight C++
implementation originally from the fastlogranktest
package. Since fastlogranktest has been deprecated and
removed from CRAN, a copy of the C++ source code has been ported
directly into goldilocks
Piecewise exponential simulation is implemented via the PWEALL
package, which uses a lightweight Fortran implementation
Simulation of multiple trials can be performed in parallel on Unix-like platforms or Windows using R’s parallel backends
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.
The current source release is goldilocks 0.6.0.
You can install the released version of goldilocks from
CRAN with:
install.packages("goldilocks")You can install the current source version from GitHub with:
# install.packages("devtools")
devtools::install_github("graemeleehickey/goldilocks")