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STATISTICS - STEP 2 > FA 2 > Flashcards

FA 2 Flashcards

1
Q

epidimiology - RRR AR ARR

A

Relative risk reduction Attributable risk

Absolute risk reduction

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2
Q

Odds ratio

A

Odds that the group with the disease (cases) was exposed to a risk factor divided by odds that the group without disease (controls) was exposed

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3
Q

Odds ratio equation (and explanation)

A

OR=(a/c)/(b/d)=ad/bc = X

the risk of the disease is X times higher for exposed then non exposed in population

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4
Q

Relative risk

A

Risk of developing disease in the exposed group divided by risk in the unexposed group

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5
Q

epidemiology - RR equation

A

(a/(a+b))/(c/(c+b))

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6
Q

if prevelance is low –> RR? OR?

A

RR=OR

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7
Q

RR: greater/lower/=1

A

=1: no association between exposure and disease

greater: exposure increases the occurrence disease
lower: decreases

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8
Q

Relative risk reduction (RRR)

A

The proportion risk reduction attributable to the intervention as compared to a control
(how much the risk is reduced by the intervention)

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9
Q

RRR - ex.

A

2% of patients who received flu shot develop the flu, while 8% of unvaccinated patients develop flu then RR=2/8 = 0.25
RRR=1-RR = 0.75

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10
Q

Attributable risk (AR) - definition example

A

The difference in risk between exposed and unexposed groups
or,
The proportion of disease that are ATTRIBUTABLE to the exposure
- If risk for lung cancer is 21% in smokers and 1 in nonsmokers, then 20% of the lung cancer risk in smokers is attributed to smoking

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11
Q

Attributable risk (AR) - example

A

If risk for lung cancer is 21% in smokers and 1 in nonsmokers, then 20% of the lung cancer risk in smokers is attributed to smoking

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12
Q

Relative risk example

A

21% smokers develop lung ca and 1% of non smoker

RR=21/1 = 21

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13
Q

Absolute risk reduction - definition

A

The difference risk (not the proportion) attributable to the intervention as compared to control)

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14
Q

Absolute risk reduction example

A

8% placebo flu - 2% vaccine flu = 6% = 0,6

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15
Q

Number needed to treat (NNT)

A

Number of patients who need to be to be treated for 1 patient to benefit
1/ARR

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16
Q

Number needed to harm

A

Number of patients who need to be exposed to a risk factor for 1 patient to be harmed
1/AR

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17
Q

of 200 patients, 50 have lung Ca. Of these, 45 are smokers. Of remaining 150 patients (no Ca) ,60 are smokers –> Odds Ratio

A

(45/5)/(60/90) = (45x90)/(5x60) = 13.5

The risk of Lung Ca is 13.5 times higher for smokers than in nonsmokers in this population

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18
Q

Accuracy

A

The trueness of test measurements (validity)
The absence of SYSTEMIC ERROR or BIAS in a test
(How close is the measured value to the true value)

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19
Q

Systemic error - accuracy

A

Systemic error decreases accuracy in a test

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20
Q

Presicion

A

-the consistency and reproducibility of a test (reliability)
-the absence of random variation on a test
(How close the values are each to other)

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21
Q

Precision - standard deviation

A

Increased precision –> decreased standard deviation

22
Q

Precision - statistical power

A

Increased precision –> increased statistical power (1-β)

23
Q

random variation - precision

A

Random variation decreases precision of a test

24
Q

interrated vs test-retest precision

A
  1. interrated –> similar results when the test is administrated by a different rater or examiner
  2. Test-retest –> similar results when the sybect is tested as a second or third time
25
Q

Bias And Study Errors types

A
  1. Recruiting participants
  2. Performing study
  3. Interpreting results
26
Q

Bias And Study Errors - recruiting participants

A

Selection bias

27
Q

Bias And Study Errors - performing study

A
  1. Recall bias
  2. Measurement bias
  3. Procedure bias
  4. Observer-expectancy bias
28
Q

Bias And Study Errors - Interpreting results

A
  1. Confounding bias
  2. Lead time bias
  3. length time bias
29
Q

Selection bias (type and definition)

A

Type: Recruiting participant bias

Error in assigning subjects to study group resulting in an unrepresentative sample. Most commonly a sampling bias

30
Q

Selection bias examples

A
  1. Berkson bias (from hospitals)
  2. Healthy worker effects
  3. Non-response bias
31
Q

Strategy to reduce selection bias

A
  1. Randomization

2. Ensure the choice of the right comparison/reference group

32
Q

Recall bias (type and definition)

A

Type: performing study

Awareness of disorder alters recall by subjects. Common in retrospective studies

33
Q

Recall bias example

A

Patients with disease recall exposure after learning of similar cases

34
Q

Recall bias strategy to reduce

A

Decrease time from exposure to follow up

35
Q

Measurement bias (type and definition)

A

Type: performing bias

Information is gathered in a way at distorts it

36
Q

Measurement bias example

A

Miscalibrated scale consistently overstates weights of subjects

37
Q

Measurement bias strategy to reduce

A

Use objective, standardized and previously tested method of data collection that are planned ahead of time

38
Q

Procedures bias (type and definition)

A

Performing study

Subjects in different group are not treated in the same way

39
Q

Procedure bias example

A

Patients in treatment group spend more time in highly specialized hospitals units

40
Q

Procedure bias strategy to reduce

A

Blinding and use of placebo reduce influence of participant and researchers on procedures and interpretation of outcomes as neither are aware of group allocation

41
Q

Observer-expectancy bias (type and definition)

A

Type: Performing study
Researchers belief in the efficacy of the treatment changes the outcome of that treatment (aka Pygmalion effect, self-fulfilling prophesy)

42
Q

Observer-expectancy bias example

A

If observer expects treatment groups to show signs of recovery, then he is more likely to document positive outcomes

43
Q

Observer-expectancy bias strategy of reduction

A

Blinding and use of placebo reduce influence of participant and researchers on procedures and interpretation of outcomes as neither are aware of group allocation

44
Q

Bias And Study Errors - Interpreting results

A
  1. Confounding bias
  2. Lead time bias
  3. length time bias
45
Q

Comfounding bias (types and definition)

A

Type: Interpreting bias
When factor is related to both exposure and outcomes, but not to the causal pathway –> factor distorts or confuses effects on outcome

46
Q

Confounding bias example

A

Pulmonary disease is more common in coal workers than the general population. However, people who work in coal mines also smoke more frequently than the general population

47
Q

Confounding bias strategy of reduction

A
  1. Multiple/repeated studies
  2. Crossover studies (subjects act as their own controls –> persons in group 1 receive the drug and group 2 placebo. Later they swich)
  3. Matching (patient with similar characteristics in both treatment and control groups)
  4. Restriction
  5. Randomization
48
Q

Lead time bias def/example

A

Early detection makes it seems as though survival has increased, but the natural history of the disease has not changed

49
Q

Lead time bias strategy of reduction

A

Measure “back end” survival (adjust survival to the severity of disease at the time of diagnosis)

50
Q

Length time bias - type and definition

A

type: interpreting results
screening test detects diseases with long latency period, while those with shorter latency period become symptomatic earlier

51
Q

Length time bias - example

A

a slowly progressive cancer is more likely detected by a screening test than a rapidly progressive test

52
Q

Length time bias - strategy to reduce it

A

a randomized controlled trial assigning subjects to the sceening program or to no screening

STATISTICS - STEP 2 (6 decks)

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