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Introduction to causal Introduction to causal association and bias in association and bias in epidemiological studyepidemiological study

Shashi KantAIIMS

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Statistical association and causal relationship

Association refers to the statistical dependence between two variables

Presence of an association, however, in no way implies that the observed relationship is one of cause and effect

Judging causality is neither simple nor straightforward

Requires judgment based on totality of evidence

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Statistical association and causal relationship contd….

Whether the association is real or spurious?If controls are selected in such a way that they

tended to be non-exposed then the association is spurious

If real, whether it is causal?Exposure to disease;Exposure to factor X, where factor X is also

independently associated with disease (confounding)

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Statistical association and causal relationship contd….2

If causal, whether it is direct or indirect I.e. intermediate step(s) are involved

Why is this distinction important?- If the relationship is causal then reduction

in exposure would lead to reduction in disease

- If non-causal, then exposure reduction will not result in any decline in disease risk

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Causal association Causal association is suggested when a

change in the frequency or quality of an exposure results in a corresponding change in frequency of the disease or outcome of interest

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Type of causal association Necessary and sufficient. Rare situation e.g.

toxicity at a particular threshold Necessary but not sufficient e.g. Tubercle

bacillus Sufficient but not necessary e.g. Leukemia

due to radiation exposure or due to benzene Neither sufficient nor necessary e.g. causal

relationship in chronic diseases

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Determination of causal association (I) For an individual study: whether the

observed association between exposure and disease is VALID, and

(II) From number of studies: whether the totality of evidence from different sources support the judgment of causality

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VALID observation That alternative explanations for the

observation are unlikely Alternative explanations include:

ChanceBias Confounding

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Bias Any systematic error in the design,

conduct or analysis of a study that results in a mistaken estimate of an exposure’s effect on the risk of a disease

(Schlesselman JJ. In: Case-control studies: design, conduct and analysis. OUP, NY, 1982

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Bias – what to do?It is a major issue in virtually any type of

study design At design and conduct stage: reduce or

eliminate bias

Analysis stage: recognize it and take into account while interpreting the findings

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Bias – selection bias Systematic error in the way in which cases

and controls, or exposed and non-exposed individuals were selected

Example: study evaluating exposure to oral contraceptive and subsequent development of thromboembolism

Cases from hospitalized individuals. Physicians more likely to admit if woman was on OC

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Selection bias - example

Variable Cases (cancer) Controls (no cancer)

No. of autopsies 816 816Evidence of TB 54 (6.6%) 133 (16.3%)Concluded: TB had protective effect against

cancer (Pearl R. Am J Hyge 1929;9:97-107Few years later Carlson & Bell in J Cancer Res

13:126-35 found no such association

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Bias – Measurement or ascertainment or information bias

Systematic error in eliciting the informationExample: Ascertaining the role of moderate

alcohol consumption and MI Interviewer assumed it had beneficial effect –

inflated estimate of drinking among controls Interviewer assumed it had deleterious effect

– inflated estimate of drinking among casescalled interviewer bias

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Other instances of Measurement or information bias Surrogate interview bias: when high

case fatality and short survival period e.g. pancreatic cancer

Surrogate is usually spouse or a child. Problem: Lack of accurate information

especially relating to stigma, andPosthumous elevation of life

style or work category

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Other instances of Measurement or information bias Non-response bias Abstracting bias Interviewer bias – example given Recall bias Rumination bias Wish bias Misclassification bias

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Interviewer bias Systematic difference in soliciting, recording

or interpreting information from different study groups

More common in eliciting exposure history in case-control study because outcome is already known

Also in assessment of outcome in prospective cohort study (exposure being known)

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Limitations of Human Recall!

PhysicalExamination

Patient’s report of circumcision

Yes NoYes 37 (66.1) 47 (34.6)No 19 (33.9) 89 (65.4)

Total 56 (100) 136 (100)

Lilienfield Am, Graham S. J Natl. Cancer Instt, 1958; 21: 713-720

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Recall bias Association between exposure to

anesthetic gas and miscarriage among hospital personnel in Sweden

Comparison of interview schedule with hospital record about exposure

Cases had 100% concordance while controls had 70% concordance

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Cases ControlsTrue Incidence (% ) 15 15

Recall rate (% ) 60 10

Reported incidence (% ) 9.0 1.5

Rumination bias (Wynder)

Hypothetical

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Wish Bias To absolve themselves of certain

exposure related to life style e.g. smoking, drinking

To over-emphasize exposure related to work place if contemplating litigation

(Wynder et al. J clin epidemiol 1991,43:619-21)

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Misclassification Bias Subjects erroneously categorized with

respect to either exposure or disease status It could be due to poor sensitivity/specificity

of diagnostic test, or incomplete or inaccurate data/record

Could be of two types i.e. differential misclassification or non-differential misclassification

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Non-differential misclassification Inaccuracies in data collection is inevitable It results from the degree of inaccuracy in

ascertaining the information from any study group

Misclassification is not related to exposure status or disease status

The proportion of inaccuracy is same in both the group

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Non-differential misclassification Let us assume that the actual number of

diseased exposed group was 20/200 and among non-exposed was 10/200. The odds ratio is therefore 2.0

Now a misclassification occurs and 5 of the diseased are classified as non-diseased. The odds ratio would then become 1.0

Message: It leads to dilution of risk ratio and tends to move towards null

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Differential misclassification The proportion of misclassification is different

in different groups Example: mothers of malformed children were

asked about prenatal infection. More mild infections were remembered by cases

Controls were thus misclassified as being less exposed to cases and an association found

Direction of association depends on direction of misclassification

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Causal association in infectious diseases

Proposed by Henle in 1840 and expanded by Koch in 1880

Organism always found with the disease Organism not found with any other disease Organism produces disease in

experimental animalKoch said first two were sufficient to prove

causal association. Not useful for NCDs.

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Bradford Hill’s criteria for causal association Temporal relationship – difficult to

establish in case-control, and retrospective cohort study. Also, the incubation period may be kept in mind e.g. 15-20 years for lung cancer and exposure to asbestos

Strength of association – stronger association more likely to be causal

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Bradford Hill’s criteria for causal association contd…. Dose response relationship – Increased dose

of exposure accompanied by increased risk of disease. Absence does not rule out causal association – threshold exposure

Consistency of evidence – across study design, study population, and researchers

Biological plausibility – sometimes knowledge may be lacking e.g. rubella and congenital cataract, pellagra and nicotinic acid

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Additional criteria for causal association contd…. Cessation of exposure – extension of

the concept of dose response criteria e.g. Eosiniphilia myalgia syndrome and L-tryptophan

Alternate explanation ruled out Consistency with other knowledge e.g

cigarette sale and Ca lung rates

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