Nothing
power_best_binomial()sim_power_best_binomial()power_best_binomial() produces a valid result using simulations.wcs_power_best_binomial() to avoid search probabilities
lower than the difference.sim_power_nbinom() to explicitly indicate the direction of the alternative
hypothesis as it could be wrongly inferred from the rr alone.multz() and multq() to follow R convention in distributions. A new
parameter lower.tail is addedss_best_normal() returns now an integer valuess_ni_ve() now computes the total number of events using Freedman's (1982)
log-rank sample size formula instead of gsDesign::nBinomial1Sample(),
which better reproduces Table 1 of Fleming et al. (2021) and removes the
gsDesign package dependencyve_exp/ve_ac parameters to ss_ni_ve(), allowing the experimental
vaccine's assumed efficacy to differ from the active comparator's, which
reproduces Table 2 of Fleming et al. (2021)true_diff parameter to sim_power_ni_normal() so simulations are
no longer restricted to a true difference of 0 between groups, allowing
sensitivity evaluation when the experimental group is truly slightly worse
or better than controlprophr() to accept vector inputs for p0/hr without an "condition
has length > 1" errorsim_power_best_bin_rank() selects the group with
the highest total rank as best (roxygen said "lowest"); power_best_normal()
returns a probability, not an integer sample size; sim_power_ni_normal()'s
and sim_power_equivalence_normal()'s t_level examples/docs clarifiedsim_power_nbinom()'s
examplepower_events_rate() function to compute the exact binomial
probability of observing at least a given number of events, across
combinations of sample sizes and risks.power_events_rate() alongside
power_single_rate().sim_best_binomial() function to select the best using ranks rather
than the custom approach of which == max().sim_.ni_fleming has been renamed to ni_ve.multz function was modified to correctly handle the case when there is
only one group.probhr function to estimate the probability of an event in the
experimental group based on the control group probability and the hazard
ratio, assuming proportional hazards.ni_fleming function.ni_fleming.power_best_binomial and power_best_normal were updated to match their
rank-based equivalents.dif represents the difference between the most promising group and
the rest.prob is now the probability in the most promising
group (previously it referred to the other groups), and dif defines how
much lower the other groups are.power_best_norm_ranks and power_best_bin_rank.power_best_norm_rank to empirically estimate power for normal
distributions based on ranks.power_test_binomial where, in the absence of ties,
the sample function randomly selected a number instead of sampling from
a range.power_best_normal.power_best_binomial now returns a data frame with power and 95%
confidence interval.power_best_normal now returns a data frame with power and 95%
confidence interval.power_ni_normal now includes the number of simulations in the output data
frame.power_best_bin_rank, which selects the best group based on ranks,
instead of assuming the highest value as in power_best_binomial.power_ni_normal function.ggplot_prob_lowest_power graph.power_equivalence_normal function.power_best_binomial function and related helpers.power_best_normal function and related helpers.NEWS.md file to track package changes.power_single_rate function.Any scripts or data that you put into this service are public.
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