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BIOEPI > CAUSAL INFERENCE > Flashcards

CAUSAL INFERENCE Flashcards

1
Q

Making judgment about causality; has process to follow

A

Causal Inference

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2
Q

Process of using different statistical methods to characterize the association between variables.

A

Statistical Association
(Statistical dependence between two variables
There is an identifiable relationship bet 2 variables
Association/ relationship is either positive or negative)

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3
Q

Process of ascribing causal relationships to associations between variables
An example of an association

A

Causal Inference

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4
Q

Factor that plays an essential role in producing an outcome
Event, condition, and characteristics
Presence of this factor should result to an outcome

A

Cause
Cause = Exposure, Outcome = disease
Ex: great intake of sugar = diabetes

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5
Q

Identifiable relationship between exposure (factor) and disease (outcome)
Cannot tell yet which one is the factor and which one is the outcome
Relationship could be co-existence: bidirectional: Cannot say yet that the exposure is the cause of the disease or that the factor is the cause of the outcome

A

Association
Cannot say yet that the exposure is the cause of the disease or that the factor is the cause of the outcome
Ex: poor lack of education, intelligence success in life

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6
Q

Presence of mechanism that leads from exposure to disease
Relationship is cause-effect: one direction: unidirectional: causal
There is really a cause that leads to the effect

A

Cause
Cause must precede the effect; cause first then effect
Ex: Mycobacterium tuberculosis —> TB, x —> y, infectious agent —> disease

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7
Q

Types of Association: (Causal vs Non-causal)
direct: Alteration in the frequency or quality of one event is followed by a change in the other
Direct relationship; one increase/ decreased the other increases/ decreases too

A

Causal

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8
Q

Types of Association: (Causal vs Non-causal)
indirect: Association is a result of the relationship of both factor and disease with a third variable
There is a relationship between the 2 variable because of the presence of a third/ confounder

A

Non Causal
Third variable: confounder: only variable that makes the 2 variables related
Associated to exposure and will be the risk factor for the outcome of interest
Confounder should be eliminated to be a causal association/ direct

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9
Q

Process/ Steps of Causal Inference : (Step 1 or Step 2)
Rule out chance, bias, confounding as explanation of the observed association
If ruled out = association is valid
If not ruled out, the association is not valid
Chance: external validity; random errors
Bias and confounding: internal validity; systematic errors

A

Step 1

Determine the validity of the association

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10
Q

Process/ Steps of Causal Inference : (Step 1 or Step 2)

Consider totality of evidence taken from a number of sources

A

Step 2

Determine if observed association is causal

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11
Q

Causal Inference: Step 1: Validity of the Association
(Internal vs External)
Bias and confounding
Estimate of the effect measure is accurate. Association should not be due to systematic error

A

Internal Validity

Validity within the study

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12
Q

Causal Inference: Step 1: Validity of the Association
(Internal vs External)
Chance
Estimate generalizable to a bigger population. Not due to random error

A

External Validity

Validity beyond the study

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13
Q

TRUE OR FALSE:

The goal of epidemiologic studies is to estimate the value of the parameter (population) with little error

A

TRUE

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14
Q

Sources of errors:
sampling errors; chance
Difference between population value of parameter being investigated and the estimate value based on the different samples
Inference will be inaccurate due to chance

A

Random Errors

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15
Q

Generalization about the group on the basis of data from the sample of the group

A

Chance

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16
Q 
Sources of errors: 
distortion in the estimation of the magnitude of association between Exposure and Disease (over or under estimation)
Deviation from the truth 
Bias: Selection and Information 
Confounding
A

Systematic errors

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17
Q

Types of Bias:
non-representative sample; not valid
Sample respondents are not representative of the population or general respondents

A

Selection Bias

18
Q

Types of Bias:
inaccurate information collected from sample
Misclassification: Differential (non-random) and Non-differential (random)

A

Information Bias

19
Q

Sources of Bias:
anything used to collect the data
Ex: weighing scale that was not calibrated = inaccurate info
Questionnaires that have vague instructions

A

Instrument

20
Q

Sources of Bias:
respondents can give inaccurate information
Due to old age, not want to disclose information

A

Subjects

21
Q

interactive responses; responses are modified because of the knowledge that they are being studied or observed

A

Hawthorne effect

22
Q

deals with instrument or there is a random fluctuations in some of the biological factors of interest

A

Biologic variability

23
Q

Sources of Bias:
researchers should not have an influence or prior knowledge before collecting the data; researchers are the one that modifies the answers of the respondents or results

A

Observers

24
Q

strategy to avoid observers’ bias wherein researchers are not involved in data collection

A

Blinding

25
Q

Mixing the effect of the exposure on the disease with that of a 3rd factor

Associated with the exposure: bidirectional
Risk factor of the disease/ outcome: unidirectional —>

A

Confounding

Presence of Confounder can lead to over or under estimation of the association

26
Q

Methods to Control Confounding: Design Stage
aim is random distribution of confounders between study groups
Random assignments of individuals in the study
Can only be done in experimental studies

A

Randomization

27
Q

Methods to Control Confounding: Design Stage
restrict entry to study of individuals with confounding factor
Limiting the participants to one category of the confounding variable

A

Restriction

28
Q

Methods to Control Confounding: Design Stage
aim for equal distribution of confounders
Mostly used in case-control studies
Confounders are identically distributed among each of the study groups

A

Matching

29
Q

Methods to Control Confounding: Analysis stage

confounders are distributed evenly within each stratum

A

Stratified analysis

30
Q

Methods to Control Confounding: Analysis stage

analysis of data that takes into account a number of variables simultaneously.

A

Multivariate analysis

31
Q

tool that can be used to assess if observed association is causal

A

Bradford Hill’s 9 Criteria for Causal Inference
HILL’S CRITERIA
-Biostatistician and epidemiologist

32
Q

Bradford Hill’s 9 Criteria for Causal Inference:
stronger the relationship between the 2 variables the less likely the relationship is due to the confounder
Can check the risks and odds ratio

A

Strength of association

33
Q

Bradford Hill’s 9 Criteria for Causal Inference:
exposure must occur first before the outcome
Important and required for establishing causation
If there is no temporality, there is no causal relationship

A

Temporality or time element

34
Q

Bradford Hill’s 9 Criteria for Causal Inference:
relationship produces the same results even with different people, circumstances, and methods
Outcome is consistent

A

Consistency

35
Q

Bradford Hill’s 9 Criteria for Causal Inference:
knowledge: rational and theoretical basis for a relationship supported by known biological and other factors
There is already an established or existing knowledge, theories, and studies about the factor, and is really the cause of the outcome

A

Theoretical plausibility

36
Q

Bradford Hill’s 9 Criteria for Causal Inference:
facts stick together as a coherent whole
Cause and effect interpretation fits with what is known regarding the diseases’ natural history, transmission or patterns

A

Coherence or Biological plausibility

37
Q

Bradford Hill’s 9 Criteria for Causal Inference:

in an ideal situation, the effect or outcome should only have one cause for it to be a causal relationship

A

Specificity in the causes

38
Q

Bradford Hill’s 9 Criteria for Causal Inference:

direct relationship between the dose and response or exposure and outcome

A

Dose-response relationship

39
Q

Bradford Hill’s 9 Criteria for Causal Inference:
strongest and most direct epidemiologic evidence to make judgement about the existence of the cause-effect relationship
Conducted an experiment to see if the factor is really the cause of the outcome
Strongest support for causality if it is done well

A

Experimental evidence

40
Q

Bradford Hill’s 9 Criteria for Causal Inference:
weakest and sometimes not considered criteria
Commonly accepted phenomenon in one area can be applied to another area

A

Analogy

BIOEPI (13 decks)

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