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gs_design_rd()
when
info_scale = "h0_h1_info"
(#402, thanks to @LittleBeannie).gs_spending_combo()
to enable HSD
spending function (#444, thanks to @LittleBeannie).fixed_design_maxcombo()
regarding the
upper bounds (#445, thanks to @elong0527).gs_design_wlr()
when the design is
driven by information fraction only (#446, thanks to @LittleBeannie).pw_info()
when there are many piecewise
HRs (#460, thanks to @LittleBeannie).The gs_update_ahr()
function (test version) is
updated to
info_scale
as the original design (#470, @LittleBeannie).Rounding of integer design is updated (#488, #484, #486, thanks to @LittleBeannie).
Integer design (i.e., integer sample size and events) is updated to ensure exact integer sample size and events (#452, #460, thanks to @LittleBeannie and @yihui).
Change the information fraction displayed at the summary-gt table from under H1 to H0 for logrank tests (#439, thanks to @LittleBeannie).
Add the sample size as the output of ahr()
and
pw_info()
(#427, #433, thanks to @LittleBeannie).
col_decimals
and
analysis_decimals
to summary.gs_design()
(#403, #431, @jdblischak).lower
is equivalent to
gs_b
(#413, thanks to @jdblischak )summary()
, as_gt()
as_rtf()
, and to_integer()
functions are
refactored (#448, #449, #450, #465, #461, thanks to @yihui).full_alpha
argument from
as_rtf.gs_design()
(#458, thanks to @yihui).gs_b()
(#415, @jdblischak)gs_power_ahr()
are added (#420,
@LittleBeannie).summary()
are added (#422,
#426, thanks to @yuliasidi, @jdblischak and @LittleBeannie).ahr_blinded()
are added (#435,
thanks to @DMuriuki).to_integer()
are added (#476,
thanks to @LittleBeannie).gs_update_ahr()
function is now available for
efficacy and futility boundary update based on blinded estimation of
treatment effect (#370).gs_design_wlr()
depending on npsurvSS (#344, #356).gs_design_ahr()
to incorporate information fraction
driven design when number of analyses >= 4 (#358).as_gt()
is added (#337).as_rtf()
method is now available for
fixed_design
and gs_design
objects for
generating RTF table outputs (#278).gs_power_wlr()
and to_integer()
now check
and convert integer sample size more rigorously (#322).gs_design_*()
now handle exceptions explicitly when all
hazard ratio is set to 1 throughout the study (#301).fixed_design_rd()
will not generate warnings due to the
previous default value change of h1_spending
(#296).gs_power_ahr()
now runs twice as fast by using
data.table and other performance optimizations (#295), enhanced by
similar improvements in gs_info_ahr()
and
pw_info()
(#300).to_integer()
and summary()
are updated
(#292).define_enroll_rate()
and
define_fail_rate()
documentation by adding detailed
descriptions and improving code examples (#302).Imports
(#307, #325).library()
calls
(#332).fixed_design()
into a group of
fixed_design_*()
functions for enhanced modularity
(#263).gs_design_rd()
and gs_power_rd()
now have
updated options of weighting for stratified design (#276).ppwe()
now accepts two arguments duration
and rate
instead of a data frame fail_rate
(#254).gridpts()
, h1()
,
and hupdate()
(#253).define_enroll_rate()
and
define_fail_rate()
as new input constructor functions to
replace the tibble inputs (#238).pw_info()
which calculates the
statistical information under the piecewise model (#262).expected_event()
to improve computational
performance (@jdblischak, #250).inst/
to tests/testthat/
as developer tests (#269).gs_design_rd()
(#220)..cpp
and header
files (#224).summary()
(#231).fixed_design()
function in the application
of stratified design when using the Lachin and Foulkes method
(#211).fixed_design()
function in the application
of rmst
(#212).info_scale
argument options from
c(0, 1, 2)
to
c("h0_h1_info", "h0_info", "h1_info")
to be more
informative and make the default value ("h0_h1_info"
) clear
(#203).README.md
(#198).README.md
to show the
monthly downloads (#216).gs_power_ahr()
(#202).Suggests
to
Imports
.Suggests
..gitattributes
for GitHub Linguist to
keep the repository’s language statistics accurate.gridpts()
, h1()
,
hupdate()
, and gs_create_arm()
to avoid the
use of :::
in code examples.data-raw/
which is not included in the
package.First submission to CRAN in March 2023.
check_fail_rate()
when only 1 number in
fail_rate
is > 0 (#132).gs_power_ahr()
when study duration is > 48 months
(#141).fixed_design()
for event-based design (#143).gs_design_combo()
when test only applies to part of the
analysis (#148).gs_info_rd()
for variance calculation (#153).summary()
for capitalized first letter in the summary
header (#164).GitHub release in December 2022.
vignette("style")
. See the detailed mapping between the old
API and new API in #84.fixed_design()
to implement different methods for
power/sample size calculation.info_scale
arguments to gs_design_*()
and gs_power_*()
.expected_accrual()
for stratified population.gs_spending_bound()
when IA is close to FA (#40).gs_power_bound()
when applied in the MaxCombo test
(#62).gs_design_npe()
for type I error (#59).GitHub release in August 2022.
Merck/gsdmvn
.GitHub release in May 2022.
GitHub release in May 2021.
s2pwe()
.AHR()
when using stratified
population.GitHub release in December 2019.
eEvents_df()
explaining the methods
thoroughly.eEvents_df()
to simplify output under option
simple = FALSE
.GitHub release in December 2019.
docs/
directory to correct the reference
materials on the website.eAccrual()
.GitHub release in November 2019.
simfix()
, simfix2simPWSurv()
,
pMaxCombo()
).GitHub release in November 2019.
AHR()
and simfix()
more
compatible with each other.GitHub release in October 2019.
AHR()
to output trial duration, expected events
and average hazard ratio in a tibble.pMaxCombo()
to compute
p-value for MaxCombo test; pMaxComboVignette demonstrates this
capability.GitHub release in September 2019.
eEvents_df()
.AHR()
.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.