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().