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Red Block > EBM Em > Flashcards

EBM Em Flashcards

1
Q

sensitivity and specificity judge the _______ precision of a test

A

sensitivity and specificity judge the TECHNICAL precision of a test

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2
Q

sensitivity and specificity are ______ to changes in prevalence

A

sensitivity and specificity are INSENSITIVE to changes in prevalence

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3
Q

PPV and NPV judge the _______ of a test

A

PPV and NPV judge the CLINICAL PERFORMANCE of a test

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4
Q

PPV and NPV are ______ to changes in prevalence

A

PPV and NPV are SENSITIVE to changes in prevalence

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5
Q

sensitivity eqn

A

TP/(TP+FN)

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6
Q

specificity eqn

A

TN/(TN+FP)

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7
Q

sensitivity

A

% pts w/ disease who have + test

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8
Q

specificity

A

% pts w/o disease who have - test

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9
Q

positive likelihood ratio

A

compares the likelihood that someone with the disease in question has a positive test as compared with someone who doesn’t have the disease

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10
Q

negative likelihood ratio

A

compares the likelihood that someone without the disease in question will have a negative test as compared with someone who does have the disease

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11
Q

positive likelihood ratio eqn

A

sensitivity / (1-specificity)

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12
Q

negative likelihood ratio eqn

A

(1-sensitivity) / specificity

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13
Q

In practice, likelihood ratios are used in two ways:

A
  1. To calculate post-test odds of a disease, given pre-test odds and the LR
  2. To give a general interpretation of a test’s quality
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14
Q

likelihood ratio >10

A

good test to rule-in disease w/ positive result

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15
Q

likelihood ratio <0.1

A

good test to rule-out disease w/ a negative result

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16
Q

allocation concealment vs. blinding (masking)

A

Allocation concealment occurs DURING the process of SELECTING patients for a study and involves the person enrolling patients.

Blinding occurs DURING the CONDUCT OF THE STUDY ITSELF and usually involves the patient, the outcome assessor, and sometimes the investigator performing the intervention.

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17
Q

P-value of .05

A

5% risk that the difference is due to chance, or a 95% likelihood that the difference we see represents a real difference.

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18
Q

P-value

A

probability that difference between two averages, rates, etc. is due to chance

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19
Q

number needed to treat

A

how many people we need to treat instead of not treat, or the number that need to be treated with one therapy instead of another, for one ADDITIONAL person to benefit

calculated based only on results that are presented as rates (%’s)

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20
Q

NNT eqn

A

NNT=100/(% tx group - % cntrl group)

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21
Q

Relative risk

A

helps us understand the difference between two rates

  • risk of harm with one drug as compared with another.
  • it also can be the risk of benefit with one drug as compared with another.
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22
Q

relative risk = 1

A

no difference in risk of harm or benefit

23
Q

relative risk = 1.3

A

likelihood of something happening is 30% higher in one group

24
Q

relative risk = .7

A

likelihood of something happening is 30% lower in one group vs. another

25
Q

OR > 1

A

the control is better than the intervention

26
Q

OR < 1

A

the intervention is better than the control

27
Q

OR = 1

A

outcome is the same in both groups

28
Q

confidence interval

A

level of uncertainty around the measure of effects

if crosses 1, implies no difference between arms of the study

29
Q

power

A

ability to find a difference between the groups if a difference truly exists

depends on the number of patients in the study but also the variability in the data and the magnitude of the effect

*only worry about if no difference between treatments

30
Q

hierarchy of evidence

A
  1. controlled trials
  2. case-control studies
  3. case series
  4. expert consensus or opinion
  5. pathophysiologic reasoning
31
Q

summary reviews

A
  • traditional type
  • cover full breadth of topic
  • useful for background Qs
32
Q

synthesis reviews

A

“systematic reviews”

  • define 1 or 2 specific questions
  • useful for foreground Qs
33
Q

Statistical heterogeneity

A

occurs when the difference among study results is greater than predicted by chance alone

34
Q

evidence of heterogeneity

A
  1. chi squared test (p=value <0.5)

2. degree of inconsistency (I^2)

35
Q

95% confidence interval

A

we can be fairly (95%) certain that the true value will fall within this range

36
Q

positive predictive value

A

probability that an individual who tests positive actually has the disease

(a/a+b)

37
Q

negative predictive value

A

probability that an individual who tests negative actually does not have the disease

(d/d+c)

38
Q

team-based learning approach main goal

A

use and apply course concepts

39
Q

concealed allocation

A

preventing the ENROLLING investigator from knowing what tx the pt will recieve

40
Q

blinding

A

preventing the TREATING investigator from knowing what tx the pt will recieve

preventing pts from knowing which tx they are receiving

41
Q

intention-to-treat analysis

A

patients analyzed in the group to which they were initially assigned regardless of whether they actually received the treatment

42
Q

degree of effect is reflected by a low NNT or a low P-value?

A

NNT

43
Q

Publication bias

A

Likelihood that negative results, i.e., studies that do not show a difference or benefit of a treatment, are less likely to be published.

Since publication bias favors publication of research showing a benefit, a meta-analysis combining on published studies could inflate the real benefit of an intervention.

44
Q

funnel plot

A

Compares the effect size in different studies with some measure of the variability of the data from each study.

A “funnel” formed by the data that is balanced on both sides of the mean shows there was no publication bias

45
Q

basic criteria

A

Is the test sufficiently sensitive and specific? We have many tests with low sensitivity and specificity.

46
Q

Minimally useful

A

The test changes diagnosis. As a result of a positive test, we now have a label to put on a patient. That doesn’t mean that we’ve done anything other than categorize their set of signs and symptoms.

47
Q

More useful

A

The test changes treatment.

A test that results in changes in treatment is a good start. However, tests don’t always lead to changes.

48
Q

Very useful

A

The test changes outcomes. Even tests that change diagnosis and change treatment decisions may not benefit patients.

49
Q

Maximum benefit

A

The test is worthwhile to patients and/or society.

Exp. Screening newborns for congenital hypothyroidism, phenylketonuria, and other diseases (but not all) results in early treatment that permanently benefits affected children, which not only benefits them but benefits society.

50
Q

usefulness of information

A

(relevance*validity)/work

51
Q

Bayes’ theorem

A

pre-test probability

based on prevalence

52
Q

pretest probability increases as we move from ______ through ______

A

pretest probability increases as we move from SCREENING through PROGNOSIS

53
Q

testing leading to diagnosis is only helpful if

A

it leads to a change in treatment

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