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BIOE FINALS > CAUSAL INFERENCE > Flashcards

CAUSAL INFERENCE Flashcards

1
Q

statistical dependence between two variables

A

Statistical Association

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2
Q

either positive or negative

A

Statistical Association

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3
Q

process of using statistical methods to characterize the association between variables.

A

Statistical Association

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4
Q

process of ascribing causal relationships to associations between variables

A

Causal Inference

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5
Q

Types of Association

A
  • Causal
  • Noncausal
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6
Q

types of causal

A
  • Direct
  • Indirect
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7
Q

Alteration in the frequency or quality of one event is followed by a change in the other

A

causal

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8
Q

Association is a result of the relationship of both factor and disease with a third variable

A

non-causal

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9
Q

Factor that plays an essential role in producing an outcome

A

cause

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10
Q

alteration if factor A is directly related to change in factor B

A

direct causal

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11
Q

there is another factor that is associated with the chnage of outcome

A

indirect causal

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12
Q

presence of mechanism that leads from exposure to disease

A

cause

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13
Q

Identifiable relationship
between exposure and
disease

A

association

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14
Q

Process of Causal Inference:

A
  1. Determine the validity of the association
  2. Determine if observed association is causal
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15
Q

rule out chance, bias, confounding as explanation of the observed association

A

Determine the validity of the association

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16
Q

consider totality of evidence taken from a number of
sources

A

Determine if observed association is causal

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17
Q

2 types of validity:

A
  1. internal
  2. external
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18
Q
  • Validity within the study
  • Estimate of effect measure is accurate
  • Not due to systematic error
A

internal validity

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19
Q
  • Validity beyond the study
  • Estimate generalizable to bigger population
  • Not due to random error
A

external validity

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20
Q

Goal of Epidemiologic Studies

A

to estimate the value of the parameter with little error

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21
Q

Sources of errors:

A
  1. random errors
  2. systematic errors
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22
Q

because of sampling errors, chance

A

random errors

23
Q

because of biases and confounding

A

systematic errors

24
Q

difference between population value of parameter being investigated and the estimate value based on the different samples

A

random errors

25
Q

distortion in the estimation of the magnitude of association between E and D (over or under estimation)

A

systematic errors

26
Q
  • deviation from the truth
  • due to bias
A

systematic errors

27
Q

3 types of systematic errors due to biases:

A
  1. selection
  2. information
  3. confounding
28
Q

biases from non-representative sample

A

selection

29
Q

biases from inaccurate info collected from sample

A

information

30
Q

information bias types:

A

misclassification

31
Q

2 kinds of misclassification:

A

non-differential
differential

32
Q

occurs when errors in similar proportion in grps being compared

A

non differential

33
Q
  • occurs when rate of errors differ in grps being compared
  • under and over estimation
A

differential

34
Q

mixing the effect of exposure on the disease with that of 3rd factor

A

confounding

35
Q

similar to non-causal

A

confounding

36
Q

variable in confounding

A

confounder

37
Q
  • associate with exposure and outcome
  • risk factor in development of disease
A

confounder

38
Q

should be ruled out when determiniing the validity if causal relationship

A

confounder

39
Q

can lead to over and under estimation

A

confounding

40
Q

sources of misclassification in information bias

A
  1. instrument
  2. subjects
  3. observers
41
Q

Methods to Control Confounding:

A
  1. DESIGN STAGE
  2. ANALYSIS STAGE
42
Q

aim is random distribution of confounders between study groups

A

Randomization

43
Q

restrict entry to study of individuals with
confounding factor

A

Restriction

44
Q

aim for equal distribution of confounders

A

Matching

45
Q

confounders are distributed evenly within each stratum

A

Stratified analysis

46
Q

a lot of ststistical tests applied to come up good analysis

A

multivariate analysis

47
Q

Bradford Hill’s criteria for Causal Inference:

A
  • Strength of association
  • Temporality
  • Consistency
  • Theoretical Plausibility
  • Coherence
  • Specificity in the Causes
  • Dose-Response Relationship
  • Experimental Evidence
  • Analogy
48
Q

higher risk ratio, higher it will be causal

A

strength of association

49
Q
  • temporal relationship
  • exposure precedes disease
A

temporality

50
Q

consistent finding across different designs/pop/investigators

A

consistency

51
Q

not contradict the natural history of disease

A

theoretical plausibility

52
Q

exposure leads to single effect

A

coherence

53
Q

higher dose, higher outcome

A

dose exposure relationship

BIOE FINALS (4 decks)

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