1 Introduction to causal association and bias in epidemiological study Shashi Kant AIIMS

2 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

3 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)

4 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

5 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

6 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

7 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

8 VALID observation That alternative explanations for the observation are unlikely Alternative explanations include: Chance Bias Confounding

9 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

10 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

11 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

12 Selection bias - example VariableCases (cancer)Controls (no cancer) No. of autopsies Evidence of TB54 (6.6%) 133 (16.3%) Concluded: TB had protective effect against cancer (Pearl R. Am J Hyge 1929;9: Few years later Carlson & Bell in J Cancer Res 13: found no such association

13 Bias – Measurement or ascertainment or information bias Systematic error in eliciting the information Example: 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 cases called interviewer bias

14 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, and Posthumous elevation of life style or work category

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

16 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)

17 Limitations of Human Recall! Lilienfield Am, Graham S. J Natl. Cancer Instt, 1958; 21:

18 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

19 Rumination bias (Wynder) Hypothetical

20 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: )

21 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

22 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

23 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

24 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

25 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 animal Koch said first two were sufficient to prove causal association. Not useful for NCDs.

26 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 years for lung cancer and exposure to asbestos Strength of association – stronger association more likely to be causal

27 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

28 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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