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ebaTools

ebaTools provides functions for evidence-based assessment of diagnostic tests. The package includes tools for calculating diagnostic accuracy statistics, performing Bayesian updating, interpreting likelihood ratios, and visualizing results with a Fagan nomogram.

Installation

CRAN

install.packages("ebaTools")

Development version

# install.packages("remotes")
remotes::install_github("rmcgill777/ebaTools")

Example

library(ebaTools)

# Calculate diagnostic accuracy statistics
stats <- eba_stats(
  tp = 45,
  fp = 12,
  fn = 5,
  tn = 138
)

stats
#> $table
#>           Condition
#> Test       Positive Negative
#>   Positive       45       12
#>   Negative        5      138
#> 
#> $sensitivity
#> [1] 0.9
#> 
#> $specificity
#> [1] 0.92
#> 
#> $ppv
#> [1] 0.7894737
#> 
#> $npv
#> [1] 0.965035
#> 
#> $lr_positive
#> [1] 11.25
#> 
#> $lr_negative
#> [1] 0.1086957
#> 
#> $log_lr_positive
#> [1] 2.420368
#> 
#> $log_lr_negative
#> [1] -2.219203
#> 
#> $dor
#> [1] 103.5
#> 
#> $prevalence
#> [1] 0.25
#> 
#> $auc
#> [1] 0.91
#> 
#> attr(,"class")
#> [1] "eba_stats"

# Update pretest probability using likelihood ratios
results <- bayes_update(
  pretest_probability = 0.30,
  lr_positive = 6,
  lr_negative = 0.25
)

results
#> $pretest_probability
#> [1] 0.3
#> 
#> $post_positive
#> [1] 0.72
#> 
#> $post_negative
#> [1] 0.09677419
#> 
#> attr(,"class")
#> [1] "bayes_update"

# Interpret the positive post-test probability
eba_interpretation(
  post_probability = results$post_positive
)
#> $post_probability
#> [1] 0.72
#> 
#> $wait_threshold
#> [1] 0.1
#> 
#> $treat_threshold
#> [1] 0.7
#> 
#> $interpretation
#> [1] "High probability: Condition likely present. Consider initiating treatment."
#> 
#> attr(,"class")
#> [1] "eba_interpretation"

# Create a Fagan nomogram
fagan_nomogram(
  pretest_probability = 0.30,
  lr_positive = 6,
  lr_negative = 0.25
)

Main Functions

Function Description
eba_stats() Calculate diagnostic accuracy statistics from a 2 × 2 contingency table.
bayes_update() Update disease probability using Bayes’ theorem and a likelihood ratio.
eba_interpretation() Provide an evidence-based interpretation of likelihood ratios.
fagan_nomogram() Visualize Bayesian updating with a Fagan nomogram.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.