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Foundations Week 6 > Wednesday EBDM > Flashcards

Wednesday EBDM Flashcards

0
Q

Probability of disease is close to zero: test or treat?

A

Neither

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1
Q

What are diagnostic tests used for?

A
  • establish a diagnosis in sick patient
  • screen for disease in healthy patient
  • provide prognosis
  • monitor ongoing therapy
  • the results are usually dichotomous
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2
Q

Probability of disease is moderate: test or treat?

A

test first

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3
Q

Probability of disease is closer to 100%? Test or treat?

A

treat first

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4
Q

What are two basic features of test?

A
  • reliability and precision

- accuracy

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5
Q

What is accuracy?

A
  • ability to hit the target

- truth

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6
Q

What is precision?

A

repeatability

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7
Q

How will you know if you are accurate/close to truth?

A

gold standard test

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8
Q

A sick person that is correctly diagnosed as sick?

A

true positive

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9
Q

A healthy person wrongly identified as sick?

A

false positive

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10
Q

A healthy person correctly identified as healthy?

A

true negative

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11
Q

A sick person wrongly identified as healthy?

A

false negative

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12
Q

P( A | B )

A

probability of A given that B is true

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13
Q

P ( Julie’s pregnancy test is + | Julie is pregnant )

TP, FP, TN, FN?

A

True positive

(probability that the test is positive if she really is pregnant

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14
Q

P ( Julie’s pregnancy test is - | Julie is pregnant )

TP, FP, TN, FN?

A

FN

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15
Q

P ( Julie’s pregnancy test is + | Julie is not pregnant )

TP, FP, TN, FN?

A

FP

16
Q

P ( Julie’s pregnancy test is - | Julie is not pregnant )

TP, FP, TN, FN?

A

TN

18
Q

Why should Diagnostic tests must be compared to Gold Standard tests?

A
  • they define disease

however, they are often time consuming, dangerous, painful, costly

19
Q

A gold standard test should…

A

be 100% sensitive and 100% specific

19
Q

If a test is very sensitive (99%), what does this mean about how many FN are present?

A

very few FN
very seNsitive tests have very few false Negatives
N for negative

20
Q

Sensitivity

A

probability that a test will be positive if a disease is really there
P ( T+ | D+)
sensitivity = number of TPs / (number of TPs + FNs)

22
Q

SnOut

A

very Sensitive tests are used to rule Out disease
(100% sensitivity means that the test correctly recognizes all sick people, and correctly rules out disease in 100% of people who do not have it, zero FNs)

22
Q

If you have a very specific test, what does this say about FP?

A

very specific test has very few FP

23
Q

Specificity

A
  • probability that test will be negative when there is no disease
  • very good at picking out negatives
    P( T- | D- )
  • can only be calculated initially in samples of individuals who do not have the disease
  • very sPecific tests have very few false Positives
  • highly specific tests to rule in disease SpIn (specificity = ruled in)
24
Q

SpIn

A

highly specific tests are used to rule in disease

25
Q

ROC curve

Where is the sweet spot? Test performing at ideal?

A

Inflection point

26
Q

Limitations

A
  • we don’t know if our patients actually have the disease
  • sensitivity and specificity don’t fit clinical reality well
  • all we usually know is what the test result is and our estimate of how likely disease is
    P( T+ | D+ )
    P( T- | D- )
27
Q

But if you want your test to be more sensitive, go further on ROC curve, what does this do to specificity?

A

increases False Positives, decreases specificity

28
Q

How should the two by two table for true/false positive/negative be organized?

A

top: patients with disease | patients w/o disease
side: test is positive | test is negative
(upper left corner is +/+, lower right corner is -/-)

29
Q

Positive Predictive Value

A

the proportion of people with a positive test who will actually have the disease
- depends on the prevalence of the disease in the population

PPV = TP/(TP+FP)

30
Q

Negative Predictive Value

A

the proportion of people with a negative test who will not have the disease
- depends on the prevalence of the disease in the population

NPV = TN / (TN+FN)

31
Q

Receiver Operating Characteristic (ROC) Curves

A

plot used to determine the best cut-off points for ‘positive’ and ‘negative’ results

  • Sensitivity vs 1 - Specificity
  • asterisk is where the curve would be PERFECT (gold standard); this is upper left of graph
32
Q

What are three important things to note about Gold Standard Tests?

A
  • they are the reference or criterion standard for diagnosis (this test defines the presence or absence of disease)
  • all diagnostic tests should be evaluated in comparison to a Gold Standard reference test
  • unfortunately, gold standard tests are usually expensive and difficult to obtain, sometimes dangerous or risky for patients

Foundations Week 6 (12 decks)

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