This PPT will enable you to get a comprehensive understanding related to the topic, with examples. Important topic through research point of view. Simple language used, with a slide on distinguish, for better recap of the content.

1. Evaluating a diagnostic test
Mrs. Angela Braver
Professor
HOD: Nursing Research ,
Community health Nursing

2. Correctly classifying individuals by
disease status
• Tests are used in medical diagnosis , screening and
research to classify subjects in to disease and non
disease group.
• Ideally all subjects who have the disease should be
classified as having the disease and vice versa

3. Diagnostic test and screening test
• A diagnostic test is used to determine the presence or
absences of a disease when a subject shows signs or
symptoms of a disease.
• A screening test identifies asymptomatic individuals
who may have the disease.
• The diagnostic test is performed after a positive
screening test to establish a definitive diagnosis

4. What is a diagnostic test?
• A test or instrument that provides some evidence for
the presences or absences of a pathology.
• A diagnostic test is a procedure which gives a rapid,
convenient and/or inexpensive indication of whether
a patient has a certain disease.
• Aids evidence based management of pathologies in
medical practice
• Retinoscopy is a diagnostic test – it provides evidence
for the presence or absences of refractive errors.

5. Attributes of a diagnostic test
• Accuracy
• Precision
• Sensitivity
• Specificity
• Positive and negative predictive values
• Receiver operating characteristics (ROC Curves)
• Likelihood ratios

6. The concept of a “Gold Standard”
• In diagnostic testing, physicians often talk about
something called “the gold standard”.
• What this actually refers to is the best possible
assessment of the disease status.
• For example, an autopsy would be probably be the
best possible assessment of thyroid enlargement, but
might not be acceptable to the patient! In practical
terms, an ultrasound is available to your clinical
group, and is an accurate “gold standard” for
identifying thyroid enlargement.

7. Measure of diagnostic accuracy
• Comparison of the disease status: gold standard test
and index test
Indextest(T)
DISEASE
D+ D-
T+ a
(Truepositive)
b
(Falsepositive)
T- c
(Falsenegative)
d
(Truenegative)

8. Sensitivity
• Proportion of people with the disease, who have
positive test result for the disease
• A sensitive test will rarely miss people with the
disease.
• Sensitivity = a
___
a+c

9. Sensitivity
• It is a conditional probability and it is denoted by P
(T+/D+)
• It is the ability of a tool to detect the disease under
consideration when it is truly present, that is, the chance
or probability of detection of the disease by the tool
when actually the disease is present.
• It is also known as true positive rate
• Mathematically expressed as: a
___
a+c
For e.g: if the value of sensitivity of the tool is 0.85 or 85% then it can be said that
in 85% of subjects with the disease the diagnostic tool gives correct disease.

10. Specificity
• It is a conditional probability and it is denoted by P
(T-/D-)
• It is the chance or probability of a test giving negative
result by the tool when actually the disease is absent
• It is also known as true negative rate.
• Mathematically expressed as: d
__
b+d
For eg: if the value of specificity of the tool is 0.80 or
80% then it can be said that in 80% of subjects without
the disease the diagnostic tool gives correct result

11. Lets understand more on Specificity
and Sensitivity
• Higher the values of sensitivity and specificity better
is the diagnostic value of the tool (true positive and
true negative)
• The criterion validity of the tool is evaluated by
taking into account both sensitivity and specificity.
• The diagnostic tool maybe with high sensitivity but
low specificity and vice versa

12. How high the values of sensitivity and
specificity are acceptable?
Depends on the following:
• Disease under study
• Implications of incorrect diagnosis
• Ease and cost of testing as compared to GS.
• Highly sensitive tool can be used to rule out the
disease in question because there is a high chance that
the tool gives positive result when disease is present
and if the tool gives negative result there is a high
chance that the disease is absent.

13. • Accuracy: how close is the value measured by a test to the gold
standard value
• Precision: how reproducible is the value when multiples
measurements are taken using this test
• Accuracy and precision need not be related to each other at all

14. Accuracy
• Accuracy is the ability of the tool to give correct
diagnosis, that is the chance or probability of a test
giving correctly positive or negative results by the tool
and it denoted as P (T+ OR T-)
• It measures the overall accuracy of the tool
• It is mathematically expressed as
P(T + or T-)= a+d
______
a+b+c+d
• For e.g. if the value of the accuracy of the tool is 0.95 or 95% then it can be
said that in 95% of subjects the diagnostic tool gives correct diagnosis and
in5% of the subjects the tool gives incorrect diagnosis

15. Positive predictive value (PPV)2
• It is a conditional probability and it is denoted by P
(D+/T+)
• It is used to assess the predictive value of a diagnostic
tool and it measures the chance or probability of
disease actually present when the tool give positive
result
• Positive predictive value= a
___
a+b

16. Negative predictive value (NPV)2
• It is a conditional probability and it is denoted
by P (D-/T-)
• It measures the chance or probability of
disease actually absent when the tool give
negative result
• Negative predictive value= d
___
c+d

17. To recap
• All diagnostic tests have four possible outcomes:
• the test is positive, and the patient has the disease
(true positives);
• the test is positive, and the patient does not have the
disease (false positives);
• the test is negative, and the patient has the disease
(false negatives);
• the test is negative, and the patient does not have the
disease (true negatives).

18. Example on (PPV)2 and (NPV)2
• If the PPV, and NPV, for the tool for a disease are 0.85
or 85% and 0.80 or 80% respectively then 85 % of
subjects who tested positive by the tool are actually
having the disease and 80% who tested negative by the
tool are actually not having the disease respectively.
• PPV and NPV are highly dependent on the prevalence
of the disease in the defined population where the tool
is tested and thus one needs to be careful before
interpreting it. If the prevalence of the disease is high
then the PPV is also high

19. Likelihood ratios (LR)2
A LR is the ratio of :
probability of a particular test result among subjects
with a disease of interest
______________________________________
to probability of that test result among subjects without
the disease of interest.

20. • Higher the value of LR + higher the likelihood of
the disease , that is larger the value of LR+ better
the relationship between the tool (diagnostic tool)
giving positive result and the individual having
the disease by GS.
• The smaller the value of LR-, better the
relationship between the tool (diagnostic tool)
giving negative result and the individual not
having the disease by GS.
• The tool with high value of LR+ can be used for
ruling in the disease and while the tool with low
LR- can be used for ruling out the disease

21. Example
• Prostate specific antigen (PSA)
• This test has a sensitivity of 86% meaning it is good at
detecting prostrate cancer, but a specificity of only 33%
which means there are many false positive results.
• RT-PCR
• 98.5% Sensitivity
• 70% specificity
• 85.5% accuracy
• 79.7% PPV
• 97.6% NPV

22. REWIND
Sensitivity Specificity Accuracy PPV NPV
True positive True negative probability of a
test giving
correctly
positive or
negative results
by the tool
probability of
disease actually
present when
the tool give
positive result
probability of
disease actually
absent when
the tool give
negative result
T+/D+ T-/D- T+ or T_ D+ or T+ D- or T-
a/a+c d/b+d a+d/
a+b+c+d
a/a+b d/c+d

23. • In the context of testing of hypothesis, there
are basically 2 types of errors we can make:
• Type I error
• Type II error

24. Type I error
• A type I error , also known as an error of the first kind,
occurs when the null hypothesis (H0) is true , but it is
rejected.
• A type I error may be compared with a so called false
positive
• A type I error occurs when we believe in a falsehood
• In other words, it is when we wrongly assume that there
is an effect when no such effect really exists
• Eg- like a false positive result in a Covid RTPCR test
• It is denoted by the Greek letter α (alpha)
• It is related to the significance level of a test
• If type I error is fixed at 5% it means that there are
about 5 chances in 100 that we will reject HO when HO
is true.

25. Type I error -example
• Assume that we are testing to see if there is a statistically
significant difference between the average marks obtained by
female and transgender students in the class.
• Assume that in reality there is no such significant difference.
• The null hypothesis will be “ average marks of female and
average marks of transgender persons are not statistically
different”
• Rejecting this null hypothesis would mean that we conclude
that there is a significant difference in the average marks of
female and average marks of transgender persons

26. Type II error
• A type II error , also known as an error of the second kind,
occurs when the null hypothesis (H0) is false , but erroneously
fails to be rejected.
• Type II error means accepting the null hypothesis which
should have been rejected.
• Failing to reject the null hypothesis would mean that we
conclude that there is no significant difference , when a
difference actually exists.
• A type I error may be compared with a so called false negative
• A type I error occurs when we fail to believe in the truth
• A type II error occurs when one rejects the alternative
hypothesis, when the alternate hypothesis is true.
• The rate of the type II error is denoted by the Greek letter
β(beta)
• It is related to the power of the test

27. Type II error -example
• Assume that we are testing to see if there is a
statistically significant difference between the
average BMI of active persons and sedentary persons.
• Assume that in reality there exists such a significant
difference.
• Failing to reject the null hypothesis would mean that
we conclude that there is no significant between the
average BMI of active persons and sedentary persons.

28. Nullhypothesis(HOistrue) Nullhypothesis
(HOisFalse)
Rejectnullhypothesis TypeI error
FalsePositive
Correctoutcome
Truepositive
Failtorejecthypothesis Correctoutcome
TrueNegative
TypeIIerror
Falsenegative

29. R
E
S
E
R
C
H
REALITY
Null hypothesis is
true
The alternate
hypothesis is true
The null
hypothesis is
true
Accurate Type II Error
β
Alternate thesis
is true
Type I Error
α
Accurate
Our
Decision
REALITY
INNOCENT GUILTY
Guilty
verdict
Type I error Correct
Not guilty
verdict
Correct Type II error
Accurate

30. Let us distinguish
Type I Error
• A type I error is when a
statistics calls for the rejection
of a null hypothesis which is
factually true.
• We may reject Ho when Ho is
true, is know as type I error
• A type error is called a false
positive
• It is denoted as alpha
• Null hypothesis and type I
error
Type II Error
• A type II error is when a
statistic does not give enough
evidence to reject a null
hypothesis even when the null
hypothesis should factually be
rejected.
• We may accept Ho when infact
Ho is not true is know as type
II error
• A type II error is false negative
• It is denoted by beta
• Alternative hypothesis and
type II errors.

31. Reducing errors
• Reducing type I errors:
• The chances of reducing the type I errors are
by increasing the level of confidence.
• Reducing type II errors:
• Increasing the sample size
• Increase the level of significance
• Increase the precision and accuracy of the test

32. Thank you!