Function reference • gsDesign2

Fixed design

Functions to calculate power/sample size in fixed designs.

fixed_design_ahr() fixed_design_fh() fixed_design_lf() fixed_design_maxcombo() fixed_design_mb() fixed_design_milestone() fixed_design_rd() fixed_design_rmst(): Fixed design under non-proportional hazards

Average hazard ratio

Functions for the average hazard ratio (AHR) method.

ahr(): Average hazard ratio under non-proportional hazards (test version)

expected_time(): Predict time at which a targeted event count is achieved

expected_event(): Expected events observed under piecewise exponential model

gs_info_ahr(): Information and effect size based on AHR approximation

gs_power_ahr(): Group sequential design power using average hazard ratio under non-proportional hazards

gs_design_ahr(): Group sequential design using average hazard ratio under non-proportional hazards

gs_update_ahr(): Group sequential design using average hazard ratio under non-proportional hazards

ahr_blinded(): Blinded estimation of average hazard ratio

Weighted logrank

Functions for the weighted logrank test (WLR) method.

wlr_weight_fh() wlr_weight_1() wlr_weight_n() wlr_weight_mb(): Weight functions for weighted log-rank test

gs_info_wlr(): Information and effect size for weighted log-rank test

gs_power_wlr(): Group sequential design power using weighted log rank test under non-proportional hazards

gs_design_wlr(): Group sequential design using weighted log-rank test under non-proportional hazards

MaxCombo

Functions for the MaxCombo method.

gs_info_combo(): Information and effect size for MaxCombo test

gs_spending_combo(): Derive spending bound for MaxCombo group sequential boundary

gs_power_combo(): Group sequential design power using MaxCombo test under non-proportional hazards

gs_design_combo(): Group sequential design using MaxCombo test under non-proportional hazards

Risk difference

Functions for risk differences.

gs_info_rd(): Information and effect size under risk difference

gs_power_rd(): Group sequential design power of binary outcome measuring in risk difference

gs_design_rd(): Group sequential design of binary outcome measuring in risk difference

Input definition

Helper functions to define inputs for study design.

define_enroll_rate(): Define enrollment rate

define_fail_rate(): Define failure rate

Summary and display tables

Functions to summarize fixed / group sequential design results.

summary(<fixed_design>) summary(<gs_design>): Summary for fixed design or group sequential design objects

as_gt(): Convert summary table of a fixed or group sequential design object to a gt object

as_rtf(): Write summary table of a fixed or group sequential design object to an RTF file

to_integer(): Rounds sample size to an even number for equal design

Boundary functions

Functions to specify the upper and lower bound in group sequential designs. They are not recommended to use alone. Instead, they should be used companied with gs_design_npe, gs_power_npe, ect..

gs_b(): Default boundary generation

gs_spending_bound(): Derive spending bound for group sequential boundary

Expected …

Functions for computing trial events.

expected_event(): Expected events observed under piecewise exponential model

expected_time(): Predict time at which a targeted event count is achieved

expected_accrual(): Piecewise constant expected accrual

Piecewise exponential

Functions for computing piecewise exponential distributions.

ppwe(): Piecewise exponential cumulative distribution function

s2pwe(): Approximate survival distribution with piecewise exponential distribution

Low-level helpers

Functions to calculate sample size or number of events under non-constant treatment effect over time.

gs_power_npe(): Group sequential bound computation with non-constant effect

gs_design_npe(): Group sequential design computation with non-constant effect and information

gs_create_arm(): Create npsurvSS arm object

pw_info(): Average hazard ratio under non-proportional hazards (test version)