The Centre for Evidence-Based Medicine develops, promotes and disseminates better evidence for healthcare.

February 11, 2019

*Kathy Taylor*

At the Centre for Evidence Based Medicine (CEBM), we often conduct systematic reviews of diagnostic accuracy, a topic with great relevance to primary care and other fields. In this blog post, I’ll look at a common problem with extracting data from diagnostic accuracy studies.

First some background. To pool diagnostic accuracy data in a meta-analysis, for each study and every reported combination of index test, reference test, measure and threshold reported, the number of true positives (TP), false positives (FP), true negatives (TN) and false negatives (FN) of the diagnostic test are required (Table).

Disease | No disease | ||

Test positive | TP | FP | TP+FP |

Test negative | FN | TN | FN+TN |

TP+FN | FP+TN | Total |

TP is the number with the disease who are correctly identified

TN is the number without the disease who are correctly identified

FP is the number without the disease who are incorrectly identified (test positive)

FN is the number with the disease who are incorrectly identified (test negative)

Although most studies don’t report this table, fortunately it may be derived from the reported statistics. Let’s suppose that the sensitivity, specificity and prevalence have been reported, and it is also clear how many were in the study. The sensitivity is the proportion of diseased patients (1st column in Table) that correctly test disease-positive. The specificity is the proportion of ‘healthy’ patients (2nd column) that correctly test disease-negative. Prevalence is the proportion of all those in the study who have the disease.

A bit of maths (see below if you’re interested) shows us that:

TP=Sensitivity x Prevalence x Total

FN=Prevalence x Total x (1-Sensitivity)

TN=Specificity x Total x (1-Prevalence)

FP=Total x (1-Prevalence) x (1-Specificity)

The first calculation (TP) is to multiply the reported sensitivity by the reported prevalence and then by the total number of people. Be careful – statistics such as prevalence can be reported as percentages or as decimal fractions. We’re assuming that sensitivity, specificity and prevalence are all reported as fractions.

Let me now show you an example from a **review **that I worked on. It’s a **study** of the detection of heart failure in a population with prevalence 34% using brain natriuretic peptide (BNP). Measuring BNP with the Biosite Triage device at a threshold of 69 pg/mL, compared to clinical assessment, the sensitivity is reported at 97% and specificity at 44%.

This study reports percentage inputs, so we first need to convert these to decimal fraction inputs by dividing the percentages by 100 (e.g. sensitivity of 97% becomes 0.97). Then applying the equations:

TP = 0.97 x 0.34 x 205 = 67.6

FN = 0.34 x 205 x (1 – 0.97) = 2.1

TN = 0.44 x 205 x (1 – 0.34) = 59.5

FP = 205 x (1 – 0.34) x (1 – 0.44) = 75.8

Note that the equations will not necessarily produce whole numbers because reported statistics are rounded up or down. Rounding up or down to the nearest integer and checking that the total is still 205 we see TP+FN+TN+FP = 68+2+59+76 = 205.

Here’s a tip…

The diagnostic accuracy 2×2 classification table can be calculated using the reported sensitivity, specificity, prevalence and study size.

In the next post I’ll explain what you might do if the prevalence is not reported.

*(You can skip this if you are only interested in carrying out the calculations)*

Sensitivity = TP = Proportion with disease who are correctly identified (**equation 1**)

TP+FN

Specificity = TN = Proportion with no disease who are correctly identified (**equation 2**)

TN+FP

Prevalence = TP+FN = Proportion with the disease (**equation 3**)

Total

Total = TP + FN + FP + TN = All patients (**equation 4**)

To calculate TP | Rearrange equation 1 | TP=Sensitivity x (TP+FN) | |

Rearrange equation 3 | TP+FN=Prevalence x Total | (equation 5) | |

Substitute for TP+FN | TP=Sensitivity x Prevalence x Total | ||

To calculate FN | Rearrange equation 3 | FN=Prevalence x Total – TP | |

Substitute for TP | FN=Prevalence x Total – Sensitivity x Prevalence x Total | ||

Tidy up with brackets | FN=Prevalence x Total x (1-Sensitivity) | ||

To calculate TN | Rearrange equation 2 | TN=Specificity x (TN+FP) | (equation 6) |

Rearrange equation 4 | TN+FP=Total –(TP+FN) | ||

Substitute for TP+FN (equation 5) | TN+FP=Total–Prevalence x Total | (equation 7) | |

Substitute for TN+FP in equation 6 | TN=Specificity x (Total –Prevalence x Total | ||

Tidy up with brackets | TN=Specificity x Total x (1–Prevalence) | ||

To calculate FP | Rearrange equation 7 | FP=Total –Prevalence x Total – TN | |

Substitute for TN | FP=Total x (1– Prevalence) – Specificity x Total x (1–Prevalence) | ||

Tidy up with brackets | FP=Total x (1–Prevalence) x (1– Specificity) |

*Dr Kathy Taylor teaches data extraction in Meta-analysis. This is a short course that is also available as part of our MSc in Evidence-Based Health Care, MSc in EBHC Medical Statistics, and MSc in EBHC Systematic Reviews.*

*Follow updates on this blog and related news on Twitter @dataextips*