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Glossary: “a lexicon of the technical, obscure, or foreign words of a work or field.” 1

Each issue of Evidence-Based Mental Health will include a glossary introducing the technical and obscure words that are used in different fields. This first glossary will cover, in detail, terms used in diagnosis. Subsequent issues will cover the areas of treatment (in more detail than presented below), aetiology, design, and reviews. We will use the article by Mintz et al (see page 22),2 whose results the table below summarises.

Blinded comparison of diagnoses made using the Questionnaire for Eating Disorder Diagnoses (Q-EDD) with clinical interviews following the format of the Structured Clinical Interview for Axis I DSM-IV Disorders (SCID−the diagnostic standard)

## THE FOLLOWING TERMS ARE USED IN COMPARING A NEW TEST AGAINST A DIAGNOSTIC (GOLD) STANDARD:

Prevalence: the proportion of people in the sample who have the disorder: [(A + C) / N] = 33 / 136= 24.3%.

Sensitivity: the proportion of people who have the disorder (according to the diagnostic [gold] standard) who are detected by the test: [A / (A + C)] = 32 / 33 = 97.0%

Specificity: the proportion of people who do not have the disorder (according to the diagnostic [gold] standard) who are determined by the test to not have the disorder: [D / B + D] = 101 / 103 = 98.1%.

Positive predictive value: the proportion of people who score positive on the test who actually have the disorder: [A / (A + B)] = 32 / 34 = 94.1%. This value is dependent on the prevalence.

Negative predictive value: the proportion of people who score negative on the test who actually do not have the disorder: [D / (C + D)] = 101 / 102 = 99.0%. This also is dependent on the prevalence.

Likelihood ratio for a positive test result: the likelihood that a positive test comes from a person with the disorder rather than one without the disorder: [A / (A + C)] / [B / (B + D)], or sensitivity / (1 − specificity) = (32 / 33) / (2 / 103) = 49.94.

Likelihood ratio for a negative test result: the likelihood that a negative test comes from a person with the disorder rather than one without the disorder: [C / (A + C)] / [D / (B + D)], or (1 − sensitivity) / specificity = (1 / 33) / (101 / 103) = 0.031.

## THE FOLLOWING TERMS ARE USED WHEN 2 OR MORE RATERS ARE COMPARED WITH EACH OTHER, AND NONE IS CONSIDERED TO BE A “GOLD STANDARD:

Crude agreement: the proportion of cases for which all raters agree. If there are only 2 raters, it is (A + D) / N.

Kappa: an index of agreement “corrected” for the fact that raters will agree with each other a certain proportion of the time just by chance. If po is the observed proportion of times they agree, and pe the proportion of times they would be expected to agree by chance, then:

## THE FOLLOWING INDICES ARE USED WHEN EVALUATING A CONTINUOUS OUTCOME MEASURE:

Reliability: the stability of the test results, whether measured over time (test-retest reliability) or across different raters (inter-rater reliability). It is usually measured with an intraclass correlation coefficient or a Pearson's correlation.

Validity: whether the test measures what it purports to measure. Because validity can be determined in many different ways, there is no one index of validity.

## Terms used in therapeutics

### WHEN THE EXPERIMENTAL TREATMENT REDUCES THE RISK OF A BAD EVENT:

RRR (relative risk reduction): the proportional reduction in rates of bad events between experimental (experimental event rate [EER]) and control (control event rate [CER]) participants in a trial, calculated as |EER - CER|/CER.

ARR (absolute risk reduction): the absolute arithmetic difference in event rates, |EER - CER|.

NNT (number needed to treat): the number of patients who need to be treated to achieve one additional favourable outcome, calculated as 1/ARR.

### WHEN THE EXPERIMENTAL TREATMENT INCREASES THE PROBABILITY OF A GOOD EVENT:

RBI (relative benefit increase): the increase in the rates of good events, comparing experimental and control patients in a trial, also calculated as |EER - CER|/ CER.

ABI (absolute benefit increase): the absolute arithmetic difference in event rates, |EER - CER|

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