Stats- sensitivity/ specificity (slides 1-51)

what is An important part of building evidence-based practice

the ability to differentiate between people who have the disease and those who do not

how do figure out if people have the disease or not

by using the most accurate and precise measure or test to promote quality outcomes

The accuracy of a screening test or a test used to

confirm a diagnosis 

how is the accuracy of a screening test or a test evaluated 

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

gold standard

the most accurate means of currently diagnosing a particular disease

what does the gold standard serve as a basis for 

comparison with newly developed diagnostic or screening tests

test variable

the screening variable (test result-positive or negative*)

state variable

the disease state (have/don’t have) or the gold standard

how many possible outcomes of a screening test for a disease is there

4

sensitivity

true positive (accurately identifies the presence of a disease

false positive

indicates a disease is present when it is not

specificity

true negative (accurately indicates that the disease is not present)

false negative

indicates that a disease is not present when it is

a highly sensitive test is very good at 

identifying the patient with a disease

If a test is highly sensitive it has a

low percentage of false negatives

Low sensitivity test is limited in

identifying the patient with a disease

If a test has low sensitivity it has a

high percentage of false negatives

If a sensitive test has negative results

the patient is less likely to have the disease

Highly specific test is very good a

identifying patients without a disease

If a test is very specific

it has a low percentage of false positives

Low specificity test is limited in

identifying patients without a disease

If a test has low specificity it has

high percentage of false positives

If a specific test has positive results

the patient is more likely to have the disease

Calculating Sensitivity & Specificity variables: A

# people who have the disease and the test is +

• top left corner of box

Calculating Sensitivity & Specificity variables: B

# people who do not have the disease and the test is +

• top right corner 

Calculating Sensitivity & Specificity variables: C

# people who have the disease and the test is –

• Bottom left corner 

Calculating Sensitivity & Specificity variables: D

# people who do not have the disease and the test is -

• Bottom right corner 

Sensitivity

probability of having the disease (true positive rate)

sensitivity equation 

a/(a+c)

Specificity

probability of the absence of disease (true negative)

• d/(b+d)

False positive

probability of no disease but having a positive test (false positive rate)

false positive equation

b/(b + d)

false negative

probability of having disease but having negative test (false negative rate)

false negative equation

c/(c + a)

likelihood ratios

Additional calculations that can help researchers to determine the accuracy of diagnostic or screening tests

what are likelihood ratios based on

the sensitivity and specific results

what is the likelihood ratios calculated for

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

Positive LR

The ratio of the true positive results to false positive results

positive LR equation

Sensitivity ÷ (1 – Specificity)

negative LR

The ratio of true negative results to false negative results

negative LR equation

Negative LR = (1 – Sensitivity) ÷ Specificity

likelihood ratios greater than 1.0

represents an increase in the likelihood of the disease

likelihood ratios less than 1.0

represents a decrease in the likelihood of the disease

The very high likelihood ratios (or >10)

rule in the disease/indicate that the patient has the disease

The very low likelihood ratios (or < 0.1)

virtually rule out the chance that the patient has the disease

Receiver Operating Characteristic (ROC) Curve

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

ROC curve variables

x-axis - false positive rate (1 – specificity)

y-axis – true positive rate (sensitivity)

C- statistic 

C statistic

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

in ROC curve what does the blue line represents

actual data

in ROC curve what does the black line represents

the reference line that represents a 50/50 chance of accurately predicting candiduria

in ROC curve the greater the distance the blue line is from the black line

the more accurate the test

Positive Predictive Value (PPV)

tells you what the probability is that a subject actually has the disease given a positive test result

PPV/ positive predictive value equation

true positives (A)/ Total number who tested positive (A+B)

what is PPV dependent upon

not just the sensitivity and specificity of the test but also on the prevalence of the illness in the population you are screening

prevalence

amount of illness (number of cases) present in the population divided by the total population

prevalence equation 

A+C/A+B+C+D

• amount of illness/ total population

If you are looking for a disease that is rare, a positive test

is more likely to be a false positive than in a population where 90% of the population actually has the disease

Negative Predictive Value (NPV)

if the subject screens negatively, NPV tells you the probability that the patient really does not have the disease

negative predictive value (NPV) equation

true negative (D)/ all subjects who tested negative (C+D)

efficiency

measure of the agreement between the screening test and the actual clinical diagnosis

how to do efficiency

Add all the true positives and the true negatives and determine what proportion of your sample that is

Efficiency equation

A+D/A+B+C+D x 100

epidemiology

The study of the distribution of a disease

• Essentially this is a science that studies ‘the why’

  behind disease pattern and progression

basic types of epidemiological studies

– Cohort study

– Case control study

– Cross-sectional study

relative risk

incidence rate in the exposed sample divided by the incidence rate of those not expose

risk ration equation

relative risk less than 1

means the group that was exposed had fewer cases develop than the group that was not exposed

what is exposure

may be a protective factor

relative risk=1

means there is no association between the exposure and the illness

relative risk greater than 1

means the group that was exposed has a higher incidence rate than the group that was not

relative risk higher than 1 exposure 

may be a risk factor 

p

value of the associated chi-square indicates whether or not your RR value is statistically significant

When you are using a RR without a p value

see the confidence interval or confidence limits

• This means that the researcher is 95% sure that the

  actual RR in the population is between the two

  numbers

what are attack rates used to determine

the origin of an outbreak – in particular with foodborne pathogens

attack rate equations

number of sick/ number of exposed (exposed and sick+exposed and not sick)