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Biomarker Diagnostic Accuracy

Source: vignettes/biomarker-diagnostic-accuracy.Rmd
biomarker-diagnostic-accuracy.Rmd

Power for validating sensitivity and specificity of a diagnostic classifier.

Formula

Separate binomial tests; reported power is min⁡(Powersens,Powerspec)\min(\text{Power}_{\text{sens}}, \text{Power}_{\text{spec}}).

Post hoc 

power_compute("diagnostic_acc", "post_hoc", sensitivity = 0.85, specificity = 0.85,
              n_pos = 50, n_neg = 50, alpha = 0.05)
#> ggpower result
#> Test: Biomarker: Diagnostic accuracy (sensitivity and specificity)
#> Analysis: post_hoc
#> 
#> Input parameters
#>   tails: two
#>   sensitivity_h1: 0.85
#>   specificity_h1: 0.85
#>   n_positive: 50
#>   n_negative: 50
#>   alpha: 0.05
#> 
#> 
#> Output parameters
#>   z_sensitivity: 16.83251
#>   z_specificity: 16.83251
#>   power_sensitivity: 1
#>   power_specificity: 1
#>   power: 1
#>   total_sample_size: 100
#> 
#> 
#> Notes
#> - Joint power uses the minimum of sensitivity and specificity power (Bonferroni-style).

A priori 

power_compute("diagnostic_acc", "a_priori", sensitivity = 0.9, specificity = 0.9,
              alpha = 0.05, power = 0.8, allocation_ratio = 1)
#> ggpower result
#> Test: Biomarker: Diagnostic accuracy (sensitivity and specificity)
#> Analysis: a_priori
#> 
#> Input parameters
#>   tails: two
#>   sensitivity_h1: 0.9
#>   specificity_h1: 0.9
#>   n_positive: 2
#>   n_negative: 2
#>   alpha: 0.05
#>   target_power: 0.8
#> 
#> 
#> Output parameters
#>   z_sensitivity: 4.242641
#>   z_specificity: 4.242641
#>   power_sensitivity: 0.9887753
#>   power_specificity: 0.9887753
#>   total_sample_size: 4
#>   actual_power: 0.9887753
#> 
#> 
#> Notes
#> - Joint power uses the minimum of sensitivity and specificity power (Bonferroni-style).
#> - A priori sample sizes are rounded up to integer values and actual power is recomputed.