Stratified Adaptive Designs for Two-Stage Phase II Studies

Phase II Clinical Trial Design for Multinomial Endpoints
Provide multinomial design methods under intersection-union test (IUT) and union-intersection test (UIT) scheme for Phase II trial. The design types include : Minimax (minimize the maximum sample size), Optimal (minimize the expected sample size), Admissible (minimize the Bayesian risk) and Maxpower (maximize the exact power level).

Randomized Phase II Oncology Trials with Bernoulli Outcomes
c(person(given = "Michael", family = "Grayling",
email = "michael.grayling@newcastle.ac.uk",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-0680-6668")))
Provides functions to assist with the design of randomized
comparative phase II oncology trials that assume their primary outcome
variable is Bernoulli distributed. Specifically, support is provided to (a)
perform a sample size calculation when using one of several published
designs, (b) evaluate the operating characteristics of a given design (both
analytically and via simulation), and (c) produce informative plots.
dplyr, ggplot2, Rcpp, scales, stats, tibble, tidyr
Michael Grayling [aut, cre] (<https://orcid.org/0000-0002-0680-6668>)

Sensitivity Analysis for p-Hacking in Meta-Analyses
c(
person("Mika", "Braginsky", email = "mika.br@gmail.com", role = c("aut", "cre")),
person("Maya", "Mathur", email = "mmathur@stanford.edu", role = c("aut")))
Fits right-truncated meta-analysis (RTMA), a bias correction for
the joint effects of p-hacking (i.e., manipulation of results within studies
to obtain significant, positive estimates) and traditional publication bias
(i.e., the selective publication of studies with significant, positive
results) in meta-analyses [see Mathur MB (2022). "Sensitivity analysis for
p-hacking in meta-analyses." <doi:10.31219/osf.io/ezjsx>.]. Unlike publication
bias alone, p-hacking that favors significant, positive results (termed
"affirmative") can distort the distribution of affirmative results. To
bias-correct results from affirmative studies would require strong assumptions
on the exact nature of p-hacking. In contrast, joint p-hacking and publication
bias do not distort the distribution of published nonaffirmative results when
there is stringent p-hacking (e.g., investigators who hack always eventually
obtain an affirmative result) or when there is stringent publication bias
(e.g., nonaffirmative results from hacked studies are never published). This
means that any published nonaffirmative results are from unhacked studies.
Under these assumptions, RTMA involves analyzing only the published
nonaffirmative results to essentially impute the full underlying distribution
of all results prior to selection due to p-hacking and/or publication bias.
The package also provides diagnostic plots described in Mathur (2022).
dplyr, ggplot2, methods, purrr, rlang, stats, stats4,
truncnorm, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), Rdpack,
rstan (>= 2.18.1), rstantools (>= 2.2.0)
Mika Braginsky [aut, cre],
Maya Mathur [aut]