Sensitivity, Specificity

what does the variable "b" represent

# of people who do not have the disease and the test is positive

what does the variable "c" represent

# of people who have the disease and the test is negative

what does the variable "a" represent

# of people who have the disease and the test is positive

positive LR (likelihood ratio) definition and calculation

ratio of the true positive results to false positive results Pos LR = sensitivity / (1-specificity)

negative LR definition and calculation

ratio of true negative results to false negative results neg LR = (1-sensitivity) / specificity

what an LR of less than 1.0 means

represents a decrease in the likelihood of the disease

what an LR greater than 1.0 means

represents an increase in the likelihood of the disease

purpose of likelihood ratios

to determine the likelihood that a positive test result is a true positive and a negative result is a true negative

false pos calculation

B / (B+D)

what does the variable "d" represent

# of people who do not have the disease and the test is negative

characteristics of a highly specific test

- good at identifying patients without a disease - low percentage of false positives

characteristics of a highly sensitive test

- good at identifying the patient with the disease - low percentage of false negatives

characteristics of a low specificity test

- limited in identifying patients without a disease - high percentage of false positives

characteristics of a low sensitivity test

- limited in identifying the patient with a disease - high percentage of false negatives

ROC curve values

.5-.6: fail (F) .6-.7: poor (D) .7-.8: fair (C) .8-.9: good (B) .9-1: excellent (A)

false negative calculation

C / (C+A)

when are likelihood ratios calculated?

after pretest probability (disease prevalence) and diagnostic testing (sensitivity and specificity), but before posttest probability (PPV, NPV)

ROC curve: C statistic

area under the curve. probability that the test result from a randomly selected person with the disease will be positive

ROC curve: what the blue line and black line represent

blue: actual data black: reference line that represents a 50/50 chance of accurately predicting the disease

receiver operating characteristics curves (ROC curves)

descriptive graph that plots the true positive rate against the false positive rate

how is the accuracy of a screening test determined?

evaluated in terms of its ability to correctly assess the presence or absence of a disease or condition as compared to the gold standard

how to calculate specificity

going down columns, not across (D) / (B+D) (true neg) / (true neg + false pos)

how to calculate sensitivity

going down the columns. (A) / (A+C) --> (true positive) / (true pos + false neg)

false positive

indicates a disease is present when it is not

false negative

indicates that a disease is not present when it is

how is accuracy measured in ROC curve

measured by the area under the curve (labeled as "Area" in the SPSS output)

relationship between specificity and sensitivity

more sensitive = less specific

gold standard

most accurate means of currently diagnosing a particular disease and serves as a basis for comparison with newly developed diagnostic or screening tests

what an LR less than 0.1 means

patient does not have the disease

what negative results of a sensitive test mean

patient is less likely to have the disease

what does it mean if a specific test has positive results

patient is more likely to have the disease

false negative rate

probability of having disease but having negative result

sensitivity

probability of having the disease; true positive

false positive rate

probability of no disease but having a false positive test

specificity

probability of the absence of the disease; true negative

test variable

screening variable

true positive

sensitivity - accurately identifies the presence of a disease

true negative

specificity - accurately indicates that the disease is not present

state variable

the disease state or the gold standard

ROC curve: what the distance between the lines means

the greater the distance the blue line is from the black line, the more accurate the test

what is the importance of determining the sensitivity of a screening test?

the higher the sensitivity of a screening test, the more likely the test is to be positive when a person has the disease (true positive)

what is the importance of determining the specificity of a screening test?

the higher the specificity the more likely the test is to be negative when a person does not have a disease (true negative)

what does the false positive rate mean?

the number of people who do not have the disease and the test is positive for the disease

what an LR value greater than 10 means

the patient has the disease

four possible outcomes of a screening test for a disease

true positive, false positive, true negative, false negative

ROC curve: x-axis and y-axis

x: false positive rate (1-specificity) y: true positive rate (1-specificity)