Skip to contents

Cox Prognostic Models

Source: vignettes/biomarker-cox-prognostic.Rmd
biomarker-cox-prognostic.Rmd

Power for a single covariate Cox proportional hazards model.

Formula

z≈|log⁡(HR)|Ez \approx |\log(\text{HR})| \sqrt{E}

where EE is the expected number of events.

Post hoc 

power_compute("cox_regression", "post_hoc", hazard_ratio = 0.65,
              events = 100, alpha = 0.05)
#> ggpower result
#> Test: Biomarker: Cox proportional hazards (single covariate)
#> Analysis: post_hoc
#> 
#> Input parameters
#>   tails: two
#>   hazard_ratio: 0.65
#>   events: 100
#>   alpha: 0.05
#> 
#> 
#> Output parameters
#>   z_statistic: 4.307829
#>   power: 0.9905593
#> 
#> 
#> Notes
#> - Wald test power from expected number of events.

A priori events 

power_compute("cox_regression", "a_priori", hazard_ratio = 0.7,
              alpha = 0.05, power = 0.8)
#> ggpower result
#> Test: Biomarker: Cox proportional hazards (single covariate)
#> Analysis: a_priori
#> 
#> Input parameters
#>   tails: two
#>   hazard_ratio: 0.7
#>   events: 62
#>   alpha: 0.05
#>   target_power: 0.8
#> 
#> 
#> Output parameters
#>   z_statistic: 2.808461
#>   actual_power: 0.8019204
#> 
#> 
#> Notes
#> - Wald test power from expected number of events.
#> - A priori sample sizes are rounded up to integer values and actual power is recomputed.