Type:	Package
Title:	Plot Positive and Negative Predictive Values for Medical Tests
Version:	0.4.2
Date:	2023-11-13
Maintainer:	Gorka Navarrete <gorkang@gmail.com>
Description:	Functions to plot and help understand positive and negative predictive values (PPV and NPV), and their relationship with sensitivity, specificity, and prevalence. See Akobeng, A.K. (2007) <doi:10.1111/j.1651-2227.2006.00180.x> for a theoretical overview of the technical concepts and Navarrete et al. (2015) for a practical explanation about the importance of their understanding <doi:10.3389/fpsyg.2015.01327>.
License:	CC0
URL:	https://github.com/gorkang/BayesianReasoning
BugReports:	https://github.com/gorkang/BayesianReasoning/issues
Depends:	R (≥ 3.5.0)
Imports:	cli, dplyr, ggforce (≥ 0.4.0), ggplot2, ggtext, gt, magrittr, png, reshape2, scales, stats, tibble, tidyr
Suggests:	curl, httr, knitr, patchwork, purrr, rmarkdown, testthat (≥ 3.0.0), vdiffr, webshot2
VignetteBuilder:	knitr
Encoding:	UTF-8
RoxygenNote:	7.2.3
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2023-11-14 08:40:39 UTC; emrys
Author:	Gorka Navarrete [aut, cre]
Repository:	CRAN
Date/Publication:	2023-11-14 11:33:20 UTC

BayesianReasoning: Plot Positive and Negative Predictive Values for Medical Tests

Description

Functions to plot and help understand positive and negative predictive values (PPV and NPV), and their relationship with sensitivity, specificity, and prevalence. See Akobeng, A.K. (2007) doi:10.1111/j.1651-2227.2006.00180.x for a theoretical overview of the technical concepts and Navarrete et al. (2015) for a practical explanation about the importance of their understanding doi:10.3389/fpsyg.2015.01327.

Author(s)

Maintainer: Gorka Navarrete gorkang@gmail.com (ORCID)

See Also

Useful links:

• https://github.com/gorkang/BayesianReasoning

• Report bugs at https://github.com/gorkang/BayesianReasoning/issues

Pipe operator

Description

Pipe operator

Usage

lhs %>% rhs

Plot PPV values for a diagnostic and a screening group

Description

Plot PPV associated to different levels of FP and a specific Sensitivity, for two different Prevalence groups.

Usage

PPV_diagnostic_vs_screening(
  max_FP = 10,
  Sensitivity = 100,
  prevalence_screening_group = 100,
  prevalence_diagnostic_group = 2,
  labels_prevalence = c("Screening", "Diagnostic"),
  folder = ""
)

Arguments

Value

Shows a plot or, if given a folder argument, saves a .png version of the plot

Examples

# Example 1
PPV_diagnostic_vs_screening(
  max_FP = 10, Sensitivity = 100,
  prevalence_screening_group = 1500,
  prevalence_diagnostic_group = 3
)

# Example 2. QWith custom labels
PPV_diagnostic_vs_screening(
  max_FP = 10, Sensitivity = 100,
  prevalence_screening_group = 1667,
  prevalence_diagnostic_group = 44,
  labels_prevalence = c("20 y.o.", "50 y.o.")
)

Plot PPV and NPV heatmaps

Description

Plot heatmaps showing the PPV for a given Sensitivity and a range of Prevalences and False Positive values or NPV values for a given Specificity and a range of Prevalences and True Positive values

Usage

PPV_heatmap(
  min_Prevalence = 1,
  max_Prevalence = 1000,
  Sensitivity = NULL,
  Specificity = NULL,
  limits_Sensitivity = NULL,
  limits_Specificity = NULL,
  one_out_of = FALSE,
  overlay = "no",
  overlay_labels = "",
  overlay_extra_info = FALSE,
  overlay_position_FP = NULL,
  overlay_position_FN = NULL,
  overlay_prevalence_1 = NULL,
  overlay_prevalence_2 = NULL,
  uncertainty_prevalence = "high",
  label_title = "",
  label_subtitle = "",
  Language = "en",
  folder = "",
  PPV_NPV = "PPV",
  steps_matrix = 100,
  DEBUG = FALSE,
  ...
)

Arguments

Value

Shows a plot or, if given a folder argument, saves a .png version of the plot

Examples

PPV_heatmap(
  min_Prevalence = 1,
  max_Prevalence = 1000,
  Sensitivity = 100,
  Specificity = 98,
  Language = "en"
)

Show minimum possible prevalence given the test characteristics

Description

Given a FP and a desired PPV, what is the Minimum Prevalence of a Condition

Usage

min_possible_prevalence(Sensitivity = 95, FP_test = 1, min_PPV_desired = 90)

Arguments

Value

A description showing the minimum necessary prevalence.

Examples

# Example 1
min_possible_prevalence(Sensitivity = 99.9, FP_test = .1, min_PPV_desired = 70)
"To reach a PPV of 70 when using a test with 99.9 % Sensitivity and 0.1 % False Positive Rate,
you need a prevalence of at least 1 out of 429"

# Example 2
min_possible_prevalence(100, 0.1, 98)
"To reach a PPV of 98 when using a test with 100 % Sensitivity and 0.1 % False Positive Rate,
you need a prevalence of at least 1 out of 21"

plot_cutoff Create a cutoff plot, showing the healthy and sick distributions, and the consequences of different cutoff points

Description

plot_cutoff Create a cutoff plot, showing the healthy and sick distributions, and the consequences of different cutoff points

Usage

plot_cutoff(
  prevalence = 0.1,
  cutoff_point = 30,
  mean_sick = 35,
  mean_healthy = 20,
  sd_sick = 3,
  sd_healthy = 5,
  n_people = 1e+05,
  add_table = FALSE,
  output_filename = NULL
)

Arguments

Value

A list with plots and table

Examples

## Not run: 
plot_cutoff(prevalence = 0.2)

## End(Not run)

remove_layers_cutoff_plot Remove layers from a cutoff plot. This is useful to show how different things are calculated (e.g. Sensitivity)

Description

remove_layers_cutoff_plot Remove layers from a cutoff plot. This is useful to show how different things are calculated (e.g. Sensitivity)

Usage

remove_layers_cutoff_plot(cutoff_plot, delete_what, silent = TRUE)

Arguments

Value

a cutoff plot without the elements deleted

Examples

## Not run: 
PLOT = plot_cutoff(prevalence = 0.2)
remove_layers_cutoff_plot(PLOT$final_plot, delete_what = c("FN", "TP")) + 
ggplot2::labs(subtitle = "Specificity = TN/(TN+FP)")

## End(Not run)