Event-driven sample size for OS, PFS, and other time-to-event primary endpoints.
Formula
Log-rank approximation — see formula reference.
Clinical survival module
power_compute("survival_pmu", "a_priori", hazard_ratio = 0.65,
event_rate = 0.5, alpha = 0.05, power = 0.8)
#> ggpower result
#> Test: Clinical: Survival endpoint (log-rank / Cox framework)
#> Analysis: a_priori
#>
#> Input parameters
#> tails: two
#> hazard_ratio: 0.65
#> event_rate: 0.5
#> allocation_ratio: 1
#> total_sample_size: 339
#> alpha: 0.05
#> target_power: 0.8
#>
#>
#> Output parameters
#> expected_events: 169.5
#> z_statistic: 2.804228
#> actual_power: 0.80074
#>
#>
#> Notes
#> - Schoenfeld/Freedman log-rank approximation for equal follow-up.
#> - A priori sample sizes are rounded up to integer values and actual power is recomputed.Post hoc
power_compute("survival_pmu", "post_hoc", hazard_ratio = 0.7,
total_n = 300, event_rate = 0.45, alpha = 0.05)
#> ggpower result
#> Test: Clinical: Survival endpoint (log-rank / Cox framework)
#> Analysis: post_hoc
#>
#> Input parameters
#> tails: two
#> hazard_ratio: 0.7
#> event_rate: 0.45
#> allocation_ratio: 1
#> total_sample_size: 300
#> alpha: 0.05
#>
#>
#> Output parameters
#> expected_events: 135
#> z_statistic: 2.072094
#> power: 0.5446676
#>
#>
#> Notes
#> - Schoenfeld/Freedman log-rank approximation for equal follow-up.