Diagnostic Tests

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Diagnostic Tests

• Dr. Shah Navas.P
• Associate Professor in Orthopedics,
• Faculty, CERTC, MC Trivandrum

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Research Focus

• Application of Bayesian methods
• to epidemiologic problems
• in diagnostic test evaluation and
• disease prevalence estimation.
• primary practical advantages of the Bayesian
  framework are that
• it can always be used for small samples, it
  allows great flexibility
• it allows direct probability interpretation of
  outcome results
• practical and statistical significance can be
  readily evaluated by scientists and policymakers.

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Epidemiological Studies
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The "Epidemiological Triad"

• Snieszko (1974)
• Host-
• Pathogen
• Environment),

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The Decision Making

• based on a number of factors
• factual knowledge
• experience and intuition
• clinical diagnostic tests and
• combinations of all of these
• which increases the probability of correct
  diagnosis
• the uncertainty associated with diagnosis
• the outcome of action taken as a result.

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Diagnostic Testing

• objective methods which reduce the uncertainly
  factor in diagnosis.
• often interpreted using a dichotomous outcome
• normal/abnormal,
• diseased/healthy,
• treat/don't treat)
• but can cause considerable difficulty in
  interpretation
• when it is continuous range
• (e.g. serum antibody levels or cell counts).
• In such cases, the selection of an appropriate
  cut-off point
• to separate 'positive' and 'negative' results
• introduces a level of uncertainty.
• In most diagnostic tests false positives and
  false negatives occur.

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True Status
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Results
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Diagnostic test accuracy studies (DTA)

• Aim at measuring the ability
• of a new, simpler, cheaper, faster, less invasive
  test,
• called index test,
• to detect the presence orabsence of a specific
  disease or condition.
• It is defined using a referencestandard the
  term gold standard
• the term gold standard is discouraged because
  it improperly implies perfection.

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Selection of Diagnostic Tests

• If the intention is to rule out a disease,
  reliable negative results are required for which
  a test with high sensitivity (i.e. few false
  negatives) is used.
• If it is desired to confirm a diagnosis or find
  evidence of disease (i.e. to "rule in" the
  disease)
• a test with reliable positive results (i.e. high
  specificity).
• As a general rule of thumb, a test with at least
• 95 sensitivity and 75 specificity
• should be used to rule out a disease and
• one with at least 95 specificity and 75
  sensitivity
• used to rule in a disease (Pfeiffer, 1998).

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Accuracy, Bias, Precision
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2 x2 Table
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2 x2 Table

• "a" the true positives
• "b" the false positives
• "c" the false negatives
• "d" the true negatives

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Epidemiological Values

• The various epidemiological values can also be
  calculated as follows
• Sensitivity a/(ac)
• Specificity d/(bd)
• PPV a/(ab)
• NPV d/(cd)
• Apparent prevalence ab/(abcd)
• True prevalence a/(abcd)

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Sensitivity

• it is the probability that it will produce a true
  positive result when used on an infected
  population (as compared to a reference or "gold
  standard").
• the sensitivity of a test can be determined by
  calculating
• TPTPFN a/(ac)

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Specificity

• is the probability that a test will produce a
  true negative result
• when used on a non-infected population (as
  determined by a reference or "gold standard").
• the specificity of a test can be determined by
  calculating
• TNTNFP d/(bd)

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ROC Curves
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Positive Predictive Value

• it is the probability that a person is infected
• when a positive test result is observed.
• In practice, predictive values should only be
• calculated from cohort studies or studies
• the number of people in that population who are
  infected with the disease
• predictive values are inherently dependent upon
• the prevalence of infection.
• the positive predictive value of a test can be
  determined by calculating
• TPTPFP

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Negative Predictive Value

• It is the probability that a person is not
  infected
• when a negative test result is observed
• This measure of accuracy should only be used
• if prevalence is available from the data.
• the negative predictive value of a test can be
  determined by calculating
• TNTNFN

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Diagnostic Likelihood Ratios (DLR)

• not yet commonly reported in literature
• they can be a valuable tool for comparing the
  accuracy of several tests to the gold standard
• they are not dependent upon the prevalence of
  disease
• The positive DLR represents the odds ratio that
• will be observed in an infected population
• compared to the odds that the same result
  observed among a non-infected population.
• the positive DLR of a test can be determined by
• TP/ TPFN FP/ FPTN

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Negative Diagnostic Likelihood Ratios

• The negative DLR represents the odds ratio
• that a negative test result will be observed in
  an infected population
• compared to the odds that the same result will be
  observed among a non- infected population.
• the negative DLR for a test can be determined by
  calculating
• FN/ TPFN TN/ FPTN Or
• false negative ratetrue negative rate

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Results

• the calculations are
• Sensitivity 74/76 97
• Specificity 127/182 70
• PPV 74/129 57
• for the particular prevalence of 29
• NPV 127/129 98
• for the particular prevalence of 29
• Apparent prevalence
• 129/258 50
• True prevalence
• 74/258 29

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Kappa Tests

• calculation of epidemiological values with the
  observed agreement given by the formula
• OA (a d)/(a b c d).
• compared to the expected agreement which would be
  obtained by chance
• EA (a b)/n x (a c)/n (c d)/n x
  (b d)/n
• Kappa is the agreement greater than that expected
  by chance divided by the potential excess.
• (OA - EA)/(1-EA)

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Kappa Tests