A diagnostic test study

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					Diagnostic Test Studies

   Tran The Trung
   Nguyen Quang Vinh
 Why we need a diagnostic test?

 We need “information” to make a decision
 “Information” is usually a result from a test
 Medical tests:
   y To screen for a risk factor (screen test)
   y To diagnosse a disease (diagnostic test)
   y To estimate a patient’s prognosis (pronostic test)
 When and in whom, a test should be done?
   y When “information” from test result have a value.
      Value of a diagnostic test

 The ideal diagnostic test:
   y Always give the right answer:
      x Positive result in everyone with the disease
      x Negative result in everyone else
   y Be quick, safe, simple, painless, reliable & inexpensive
 But few, if any, tests are ideal.
 Thus there is a need for clinically useful
  substitutes
           Is the test useful ?


   Reproducibility (Precision)
   Accuracy (compare to “gold standard”)
   Feasibility
   Effects on clinical decisions
   Effects on Outcomes
         Determining Usefulness
            of a Medical Test

Question       Possible Designs Statistics for
                                Results
1. How       Studies of:            Proportion
reproducible - intra- and inter     agreement,
is the test? observer &             kappa, coefficient
               - intra- and inter   of variance, mean
               laboratory           & distribution of
                                    differences (avoid
               variability          correlation
                                    coefficient)
        Determining Usefulness
           of a Medical Test



Question      Possible Designs       Statistics for
                                     Results
2. How        Cross-sectional, case- Sensitivity,
accurate is   control, cohort-type   specificity,
the test?     designs in which test PV+, PV-,
              result is compared     ROC curves,
              with a “gold standard” LRs
        Determining Usefulness
           of a Medical Test

Question       Possible          Statistics for Results
               Designs
3. How         Diagnostic        Proportion abnormal,
often do       yield studies,    proportion with
test results   studies of pre-   discordant results,
affect         & post test       proportion of tests
clinical       clinical          leading to changes in
decisions?     decision          clinical decisions; cost
               making            per abnormal result or
                                 per decision change
        Determining Usefulness
           of a Medical Test


Question        Possible         Statistics for Results
                Designs
4. What are     Prospective or   Mean cost, proportions
the costs,      retrospective    experiencing adverse
risks, &        studies          effects, proportions
acceptability                    willing to undergo the
of the test?                     test
            Determining Usefulness
               of a Medical Test

Question      Possible Designs              Statistics for
                                            Results
5. Does       Randomized trials, cohort     Risk ratios, odd
doing the     or case-control studies in    ratios, hazard
test          which the predictor           ratios, number
improve       variable is receiving the     needed to treat,
clinical      test & the outcome            rates and ratios
outcome,      includes morbidity,           of desirable
or having     mortality, or costs related   and
adverse       either to the disease or to   undesirable
effects?      its treatment                 outcomes
       Common Issues for
     Studies of Medical Tests
 Spectrum of Disease Severity and Test Results:
  y Difference between Sample and Population?
  y Almost tests do well on very sick and very well
    people.
  y The most difficulty is distinguishing Healthy & early,
    presymtomatic disease.
 Subjects should have a spectrum of disease
 that reflects the clinical use of the test.
        Common Issues for
      Studies of Medical Tests
 Sources of Variation:
  y Between patients
  y Observers’ skill
  y Equipments
=> Should sample several different institutions to
 obtain a generalizable result.
        Common Issues for
      Studies of Medical Tests
 Importance of Blinding: (if possible)
  y Minimize observer bias
  y Ex. Ultrasound to diagnose appendicitis
  (It is different to clinical practice)
     Studies of Diagnostic tests

 Studies of Test Reproducibility
 Studies of The Accuracy of Tests
 Studies of The Effect of Test Results on Clinical
  Decisions
 Studies of Feasibility, Costs, and Risks of Tests
 Studies of The Effect of Testing on Outcomes
  Studies of Test Reproducibility

 The test is to test the precision
   y Intra-observer variability
   y Inter-observer variability
 Design:
   y Cross-sectional design
   y Categorical variables: Kappa
   y Continuous variables: coefficient of variance
 Compare to it-self (“gold standard” is not
  required)
Studies of the Accuracy of Tests

 Does the test give the right answer?
 “Tests” in clinical practice:
  y Symptoms
  y Signs
  y Laboratory tests
  y Imagine tests
  To find the right answer.
  “Gold standard” is required
       How accurate is the test?

 Validating tests against a gold standard:
 New tests should be validated by comparison
  against an established gold standard in an
  appropriate subjects
 Diagnostic tests are seldom 100% accurate
  (false positives and false negatives will occur)
   Validating tests against a gold
              standard
 A test is valid if:
   y It detects most people with disorder (high Sen)
   y It excludes most people without disorder (high Sp)
   y a positive test usually indicates that the disorder is
     present (high PV+)

 The best measure of the usefulness of a test is
  the LR: how much more likely a positive test is
  to be found in someone with, as opposed to
  without, the disorder
A Pitfall of Diagnostic test


 A test can separate the very sick from the very
 healthy does not mean that it will be useful in
 distinguish patients with mild cases of the
 disease from others with similar symptoms
                  Sampling

 The spectrum of patients should be
  representative of patients in real practice.
 Example: Which is better? What is the limits?
  y Chest X-ray to diagnose aortic aneurism (AA). Sample
    are 100 patients with and 100 without AA that
    ascertained by CT scan or MRI.
  y FNA to diagnose thyroid cancer. 100 patients with
    nodule > 3cm and had indication to thyroidectomy
    (biopsy was the gold standard).
            “Gold standard”

 “Gold standard” test: often confirm the presence
  or absence of the disease : D(+) or D(-).
 Properties of “Gold standard”:
  y   Ruling in the disease (often doing well)
  y   Ruling out the disease (maybe not doing well)
  y   Feasible & ethical ? (ex. Biopsy of breast mass)
  y   Widely acceptable.
             The test result

 Categorical variable:
  y Result: Positive or Negative
  y Ex. FNA cytology
 Continuous variable:
  y Next step is: find out “cut-off point” by ROC curve
  y Ex. almost biochemical test: pro-BNP, TR-Ab,..
    Analysis of Diagnostic Tests


How accurate is the test?
 Sensitivity & Specificity
 Likelihood ratio: LR (+), LR (-)
 Posterior probability (Post-test probability) /
  Positive, Negative Predictive value (PPV, NPV);
  given Prior probability (Pre-test probability)
  Sensitivity and Specificity




        a
Sens                   Disease D
       ac            “Gold standard”
             Test
             Result    +          -
Spec 
        d       +      a          b
       bd      -      c          d
Positive & Negative Predictive Value

 PV (+): positive
  predictive value      Test        Disease D
 PV (-): negative      Result      +       -
  predictive value        +          a      b
            a             -          c      d
  PV () 
           ab
                                             a /(a  c)
                     LikelihoodRatio( LR ) 
            d                                b /(b  d )
  PV () 
           cd
                       Posterior odds


When combined with information on the prior
 probability of a disease*, LRs can be used to
 determine the predictive value of a particular test
 result:
Posterior odds = Prior odds x Likelihood ratio


*expressing the prior probability [p] of a disease as the prior odds [p/(1-p)] of
that disease. Conversely, if the odds of a disease are x/y, the probability of the
disease is x / (x + y)
     Choice of a cut-off point
      for continuous results

Consider the implications of the two possible
  errors:
 If false-positive results must be avoided (such as
  the test result being used to determine whether
  a patient undergoes dangerous surgery), then
  the cutoff point might be set to maximize the
  test's specificity
 If false-negative results must be avoided (as
  with screening for neonatal phenylketonuria),
  then the cutoff should be set to ensure a high
  test sensitivity
      Choice of a cut-off point
       for continuous results

 Using receiver operator characteristic (ROC)
  curves:
  y Selects several cut-off points, and determines the
    sensitivity and specificity at each point
  y Then, graphs sensitivity (true-positive rate) as a
    function of 1-specificity (false-positive rate)

 Usually, the best cut-off point is where the ROC
  curve "turns the corner”
           RECEIVER OPERATING
        CHARACTERISTIC (ROC) curve

 ROC curves (Receiver
  Operator Characteristic)
 Ex. SGPT and Hepatitis         Sensitivity

SGPT      D+    D-    Sum    1

< 50      10    190   200
50-99     15    135   150
100-149   25    65    90
150-199   30    30    60
200-249   35    15    50
250-299   120   10    130
>300      65    5     70                                   1
                                           1-Specificity
Sum       300   450   750

				
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