---
title: "Introduction to BayesianReasoning"
author: "Gorka Navarrete"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Introduction to BayesianReasoning}
%\VignetteEncoding{UTF-8}
%\VignetteEngine{knitr::rmarkdown}
editor_options:
chunk_output_type: console
params:
package_creation: FALSE
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
dpi = 60,
fig.height = 10,
fig.width = 14
)
```
```{r, echo = FALSE, message = FALSE, results = 'hide'}
library(BayesianReasoning)
# FROM: https://community.rstudio.com/t/internet-resources-should-fail-gracefully/49199/12
safely_show_image_Rmd <- function(remote_file, name_image) {
try_GET <- function(x, ...) {
tryCatch(
httr::GET(url = x, httr::timeout(1), ...),
error = function(e) conditionMessage(e),
warning = function(w) conditionMessage(w)
)
}
is_response <- function(x) {
class(x) == "response"
}
# First check internet connection
if (!curl::has_internet()) {
message("No internet connection.")
return(invisible(NULL))
}
# Then try for timeout problems
resp <- try_GET(remote_file)
if (!is_response(resp)) {
message(resp)
return(invisible(NULL))
}
# Then stop if status > 400
if (httr::http_error(resp)) {
httr::message_for_status(resp)
return(invisible(NULL))
}
# Output
paste0("![", name_image, "](", remote_file, ")")
}
```
# Bayesian reasoning
<!-- badges: start -->
```{r echo=FALSE, results='asis'}
# If we are creating the README params$package_creation will be TRUE
if (params$package_creation) {
cat(paste0("[", safely_show_image_Rmd(remote_file = "https://www.r-pkg.org/badges/version/BayesianReasoning", name_image = "CRAN status"), "]", "(https://cran.r-project.org/package=BayesianReasoning)"))
cat(paste0("[", safely_show_image_Rmd(remote_file = "https://codecov.io/gh/gorkang/BayesianReasoning/branch/master/graph/badge.svg", name_image = "Codecov test coverage"), "]", "(https://app.codecov.io/gh/gorkang/BayesianReasoning?branch=master)"))
cat(paste0("[", safely_show_image_Rmd(remote_file = "http://cranlogs.r-pkg.org/badges/BayesianReasoning", name_image = "downloads"), "]", "(https://cran.r-project.org/package=BayesianReasoning)"))
cat(paste0("[", safely_show_image_Rmd(remote_file = "https://img.shields.io/badge/lifecycle-experimental-orange.svg", name_image = "Lifecycle: experimental"), "]", "(https://lifecycle.r-lib.org/articles/stages.html#experimental)"))
cat(paste0("[", safely_show_image_Rmd(remote_file = "https://zenodo.org/badge/93097662.svg", name_image = "DOI"), "]", "(https://zenodo.org/badge/latestdoi/93097662)"))
}
```
<!-- badges: end -->
---
## Bayesian reasoning in medical contexts
This package includes a few functions to plot and help understand Positive and Negative Predictive Values, and their relationship with Sensitivity, Specificity and Prevalence.
+ The **Positive Predictive** Value of a medical test is the probability that a positive result will mean having the disease. Formally p(Disease|+)
+ The **Negative Predictive** Value of a medical test is the probability that a negative result will mean **not** having the disease. Formally p(Healthy|-)
The BayesianReasoning package has three main functions:
+ **PPV_heatmap()**: Plot heatmaps with PPV or NPV values for the given test and disease parameters.
+ **PPV_diagnostic_vs_screening()**: Plots the difference between the PPV of a test in a diagnostic context (very high prevalence; or a common study sample, e.g. ~50% prevalence) versus a screening context (lower prevalence).
+ **min_possible_prevalence()**: Calculates how high should the prevalence of a disease be to reach a desired PPV given certain test parameters.
---
If you want to install the package can use: `remotes::install_github("gorkang/BayesianReasoning")`. Please report any problems you find in the [Issues Github page](https://github.com/gorkang/BayesianReasoning/issues).
There is a [shiny app implementation](https://gorkang.shinyapps.io/BayesianReasoning/) with most of the main features available.
---
## PPV_heatmap()
Plot heatmaps with PPV or NPV values for a given specificity and a range of Prevalences and FP or FN (1 - Sensitivity). The basic parameters are:
* min_Prevalence: Min prevalence in y axis. "min_Prevalence out of y"
* max_Prevalence: Max prevalence in y axis. "1 out of max_Prevalence"
* Sensitivity: Sensitivity of the test
* max_FP: FP is 1 - specificity. The x axis will go from FP = 0% to max_FP
* Language: "es" for Spanish or "en" for English
```{r heatmap}
PPV_heatmap(min_Prevalence = 1,
max_Prevalence = 1000,
Sensitivity = 100, limits_Specificity = c(90, 100),
Language = "en")
```
---
### NPV
You can also plot an NPV heatmap with PPV_NPV = "NPV".
```{r NPV-heatmap}
PPV_heatmap(PPV_NPV = "NPV",
min_Prevalence = 800, max_Prevalence = 1000,
Specificity = 95, limits_Sensitivity = c(90, 100),
Language = "en")
```
---
### Area overlay
You can add different types of overlay to the plots.
For example, an area overlay showing the point PPV for a given prevalence and FP or FN:
```{r area}
PPV_heatmap(min_Prevalence = 1, max_Prevalence = 1200,
Sensitivity = 81,
limits_Specificity = c(94, 100),
label_subtitle = "Prenatal screening for Down Syndrome by Age",
overlay = "area",
overlay_labels = "40 y.o.",
overlay_position_FP = 4.8,
overlay_prevalence_1 = 1,
overlay_prevalence_2 = 68)
```
The area plot overlay can show more details about how the calculation of PPV/NPV is performed:
```{r area2}
PPV_heatmap(min_Prevalence = 1, max_Prevalence = 1200,
Sensitivity = 81,
limits_Specificity = c(94, 100),
label_subtitle = "Prenatal screening for Down Syndrome by Age",
overlay_extra_info = TRUE,
overlay = "area",
overlay_labels = "40 y.o.",
overlay_position_FP = 4.8,
overlay_prevalence_1 = 1,
overlay_prevalence_2 = 68)
```
### Line overlay
Also, you can add a line overlay highlighting a range of prevalences and FP. This is useful, for example, to show how the PPV of a test changes with age:
```{r line}
PPV_heatmap(min_Prevalence = 1, max_Prevalence = 1800,
Sensitivity = 90,
limits_Specificity = c(84, 100),
label_subtitle = "PPV of Mammogram for Breast Cancer by Age",
overlay = "line",
overlay_labels = c("80 y.o.", "70 y.o.", "60 y.o.", "50 y.o.", "40 y.o.", "30 y.o.", "20 y.o."),
overlay_position_FP = c(6.5, 7, 8, 9, 12, 14, 14),
overlay_prevalence_1 = c(1, 1, 1, 1, 1, 1, 1),
overlay_prevalence_2 = c(22, 26, 29, 44, 69, 227, 1667))
```
---
Another example. In this case, the FP is constant across age:
```{r line-2}
PPV_heatmap(min_Prevalence = 1, max_Prevalence = 2000, Sensitivity = 81,
limits_Specificity = c(94, 100),
label_subtitle = "Prenatal screening for Down Syndrome by Age",
overlay = "line",
overlay_labels = c("40 y.o.", "30 y.o.", "20 y.o."),
overlay_position_FP = c(4.8, 4.8, 4.8),
overlay_prevalence_1 = c(1, 1, 1),
overlay_prevalence_2 = c(68, 626, 1068))
```
---
## PPV_diagnostic_vs_screening()
In scientific studies developing a new test for the early detection of a medical condition, it is quite common to use a sample where 50% of participants has a medical condition and the other 50% are normal controls. This has the unintended effect of maximizing the PPV of the test.
This function shows a plot with the difference between the PPV of a diagnostic context (very high prevalence; or a common study sample, e.g. ~50% prevalence) versus that of a screening context (lower prevalence).
```{r diagnostic}
PPV_diagnostic_vs_screening(max_FP = 10,
Sensitivity = 100,
prevalence_screening_group = 1000,
prevalence_diagnostic_group = 2)
```
---
## min_possible_prevalence()
Imagine you would like to use a test in a population and want to have a 98% PPV. That is, IF a positive result comes out in the test, you would like a 98% certainty that it is a true positive.
How high should the prevalence of the disease be in that group?
```r
min_possible_prevalence(Sensitivity = 100,
FP_test = 0.1,
min_PPV_desired = 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
---
Another example, with a very good test, and lower expectations:
```r
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
---
## plot_cutoff()
Since v0.4.2 you can also plot the distributions of sick and healthy individuals and learn about how a cutoff point changes the True Positives, False Positives, True Negatives, False Negatives, Sensitivity, Specificity, PPV and NPV.
```{r cutoff}
PLOTS = plot_cutoff(prevalence = 0.2,
cutoff_point = 33,
mean_sick = 35,
mean_healthy = 20,
sd_sick = 3,
sd_healthy = 5
)
PLOTS$final_plot
```
Then, with `remove_layers_cutoff_plot()` you can remove specific layers, to help you understand some of these concepts.
```{r remove-cutoff}
# Sensitivity
remove_layers_cutoff_plot(PLOTS$final_plot, delete_what = c("FP", "TN")) + ggplot2::labs(subtitle = "Sensitivity = TP/(TP+FN)")
# Specificity
remove_layers_cutoff_plot(PLOTS$final_plot, delete_what = c("FN", "TP")) + ggplot2::labs(subtitle = "Specificity = TN/(TN+FP)")
# PPV
remove_layers_cutoff_plot(PLOTS$final_plot, delete_what = c("TN", "FN")) + ggplot2::labs(subtitle = "PPV = TP/(TP+FP)")
# NPV
remove_layers_cutoff_plot(PLOTS$final_plot, delete_what = c("TP", "FP")) + ggplot2::labs(subtitle = "NPV = TN/(TN+FN)")
```