Sensitivity & Specificity

What would a low specificity lead to?

If a test has a LOW Specificity, it would lead to many false diagnosis (FALSE POSITIVE: test stating a truly HEALTHY patient is SICK)

What would 100% specificity mean?

If a test has a Specificity of 100%, it would identify EVERY healthy patient and have no FALSE POSITIVES

If the prevalence goes up, the PPV and NPV will....

PPV goes up NPV goes down

What is specificity?

W•Looking at a test's ability to detect people who DO NOT HAVE Disease X •Percentage of people who are correctly identified as HEALTHY •Of all people WITHOUT Disease X, how well does Test Y do at correctly identify them as disease free?

X axis

disease X

Bottom left box

false negative

Top right box

false positive

What would a 100% sensitivity test do?

identify every sick patient

How are specificity and sensitivity related?

inversely related

What would a low sensitivity test do?

lead to many missed diagnoses

Side of the chart

positive, negative

SNOUT

sensitivity--a test is good at ruling OUT disease

Top of the chart

sick, healthy

SPIN

specificity--a test is good at ruling IN a disease

Y axis

test result

Bottom right box

true negative

Top left box

true positive

How will a high prevalence affect predictive values?

•Higher prevalence will INCREASE the PPV and LOWER the NPV

What is sensitivity?

•Looking at a test's ability to detect people who DO HAVE disease X •Percentage of people who are correctly identified •Of all people with Disease X, how well does test Y do at correctly identify them

Why will a low prevalence have a low PPV

•Lower prevalence (IE: a rare disease) will have a low PPV due to an increased False Positive amount

What do the predictive values depend on?

•Predictive Values vary according to the prevalence of a disease

What do sensitivity and specificity depend on?

•Sensitivity & Specificity depends solely on the characteristics of the test

What is the sensitivity formula?

•Sensitivity= (TRUE POSITIVE / (TRUE POSITIVE + FALSE NEGATIVE)) * 100

What is the specificity formula?

•Specificity= (TRUE NEGATIVE / (TRUE NEGATIVE + FALSE POSITIVE)) * 100

What is a true positive?

•Test result correctly identifies a SICK patient with Disease X •SICK ID'd as SICK

What is a true negative?

•Test result correctly identifying a HEALTHY patient without Disease X •HEALTHY ID'd as HEALTHY

What is a false negative?

•Test result incorrectly identifying a SICK patient without Disease X •SICK MISS ID'd as HEALTHY

What is a type I error?

•Test result incorrectly identifying a SICK patient without Disease X •SICK MISS ID'd as HEALTHY •aka false negative

What is a false positive?

•Test result incorrectly saying a HEALTHY patient has Disease X •HEALTHY ID'd as SICK

What is a type II error?

•Test result incorrectly saying a HEALTHY patient has Disease X •HEALTHY ID'd as SICK •aka false positive

Negative predictive value

•What proportion of negative results are TRUE NEGATIVES •NPV= (TRUE NEGATIVE / (TRUE NEGATIVE + FALSE NEGATIVE)) * 100

Positive predictive value

•What proportion of positive results are TRUE POSITIVES •PPV= (TRUE POSITIVE / (TRUE POSITIVE + FALSE POSITIVE)) * 100