Chapter 2 Flashcards

1
Q

Validity

A

does the test accurately id whether a pt has a disease?

  • gold standard test
  • 2x2 table format
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2
Q

Sensitivity

A

probability that a person with disease has a positive test

- the true positive rate

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3
Q

Specificity

A

probability that a non-diseased person has a negative test

- the true negative rate

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4
Q

Positive Predictive Value

A

probability that a person w/ a + test has disease

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5
Q

Negative Predictive Value

A

probability that a person w/ a - test does not have disease

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6
Q

Prevalence of Disease

A

proportion of pts in the disease present column

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7
Q

Likelihood Ratio

A

probability of obtaining a given test result in a diseased patent divided by the probability of obtaining a given test result in a non-diseased patient
- tells us how much a test result changes the pre-test disease probability (prevalence) to the post-test disease probability

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8
Q

higher likelihood ratio for positive test

A

indicates that a positive test is more likely to be coming from a diseased person than from a non-diseased person, increasing confidence that a person w/ a + result has disease

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9
Q

lower likelihood ratio for negative test

A

negative test is much more likely to be coming from a non-diseased person than from a diseased person, increasing our confidence that a person w/ a - test does not have disease

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10
Q

Bayes Theorem

A

one way to use likelihood ratios to revise probabilities for disease

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11
Q

Ways to use Likelihood Ratios

A

Bayes Theorem
Fagan Nomogram
Natural Frequencies

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12
Q

Natural Frequencies

A

represent the joint frequency of 2 events, such as # of pts w/ disease and the # who have a + test result

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13
Q

Kappa Score

A

(reproducibility)

measures amount of agreement that occurs beyond chance (higher = better)

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14
Q

Precision

A

(reproducibility)

being able to apply the same test to the same unchanged person and obtain the same results

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15
Q

coefficient of variation

A

statistical test used to characterized precision

- the standard deviation divided by the mean value (lower = greater precision)

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16
Q

Primary Prevention

A

interventions designed to prevent disease

17
Q

Secondary Prevention

A

screening tests designed to find disease or disease processes at an early, asymptomatic stage

18
Q

Evidence Pyramid

A
  1. Systematic Reviews
  2. Randomized Control Trials - strongest health promotion recommendations are based on these results
  3. Cohort Studies
  4. Case-Control Studies
  5. Case Series, Case Reports
  6. Editorials, Expert Opinion
19
Q

Selection Bias

A
  • occurs when comparison groups have systematic differences in their baseline characteristics what can affect the outcome of the study
  • creates problems interpreting observed differences in outcomes n/c they could result from the interventions or the baseline differences between groups
  • randomly allocating subjects to the intervention is the best approach to minimize this
20
Q

Performance Bias

A
  • occurs when there are systematic differences in the care received between comparison groups (other than intervention)
  • creates problems in interpreting outcome differences
  • Minimize by: blinding subjects and providers to the intervention
21
Q

Detection Bias

A
  • Occurs: systematic difference in efforts to diagnoses ot ascertain outcome
  • Minimize by: blinding outcomes assessors (ensuring that they are unaware of the intervention received by h\the subject)
22
Q

Attrition Bias

A
  • Occurs: systematic differences in the comparison groups in the number of subjects who do not complete the study
  • failing to account for these difference can lead to incorrectly estimating the effectiveness of an intervention
  • Minimize by: using an intervention-to-treat analysis, where all analyses consider all subjects who are assigned to a comparison group, regardless of whether they received to completed the intervention
23
Q

statistics used to characterize the performance of a treatment of prevention intervention:

A
  • relative risks
  • relative risk difference (reduction or increase, reflecting benefit or harm)
  • absolute risk difference (reduction or increase, reflecting benefit or harm)
  • numbers needed to treat
  • numbers needed to harm
24
Q

Experimental Event Rate

A

probability that an intervention subject had the outcome

25
Q

Control Event Rate

A

probability that a control subject had the outcome

26
Q

Relative Risk

A

probability of an outcome in the intervention group compared to the probability of an outcome in the control group

27
Q

Relative Risk Difference

A

proportion of baseline risk is reduced/increased by the therapy

28
Q

Absolute Risk Difference

A

difference in outcome rates between the comparisons groups

29
Q

Reciprocal of the absolute risk difference

A

number of subjects who need to be treated over a specific period of time to prevent one outcome
- if intervention increases risk of a bad outcome this statistic becomes the number needed to harm

30
Q

guideline recommendations: US Preventative Services Task Force Approach

A

assigns 1-5 ratings and a level of certainty regarding net benefits

31
Q

guideline recommendations: Grading of Recommendations, Assessment, Development, and Evaluation (GRADE)

A

rates quality of the evidence and grades the strength of recommendations in clinical guidelines
- primary goals (1) clearly separate quality of evidence and strength of recommendations [A,B,C] (2) provide clear, pragmatic interpretations of strong v. weak recommendations [1,2]