4 Association and Causation Flashcards
Q: What does association mean?
A: Association refers to the statistical dependence between two variables
A link, relationship or correlation
Q: What 4 factors should be considered when evaluating statistical association? Is there a specific order?
A: Chance
Bias
Confounding
Cause
MUST consider first three before you look at causal relationship
Q: Why does the role of chance need to be assessed when investigating association? How?
A: Most studies based on an estimate from samples rather than whole populations
Performing appropriate statistical significance tests by calculating confidence intervals (p value- the probability that a result could simply be due to chance, threshold is usually <0.05-> i.e. if p<0.05 we can be sure that the result of the study is not due to chance)
Q: What are confidence intervals?
A: the range within which the ‘true’ value (e.g. the strength of an association) is expected to lie with a given degree of certainty (e.g. 95% or 99%)
If independent samples are taken repeatedly from the same population, and a confidence interval calculated for each sample, then a certain percentage (e.g. 95%) of the intervals will include the true underlying population parameter
Q: What is bias in terms of disease and exposure? Consequence of? Controlled by analysis? Eliminated by increasing sample size?
A: Bias is a systematic error leading to an incorrect estimate of the effect of an exposure on the development of a disease or outcome of interest
A consequence of defects in design or execution of an epidemiological study.
Cannot be controlled in analysis of study.
Cannot be eliminated by increasing sample size.
Q: Name and describe the 2 broad types of bias.
A: Selection bias – occurs when there is a systematic difference between the characteristics of the people selected for a study and the characteristics of those who were not (non response bias)
Measurement bias – occurs when measurements or classifications of disease or exposure are inaccurate (recall bias)
Q: Describe a potential confounder in relation to investigating disease. Mixing effects between?
A: any factor which is believed to have a real effect on the risk of the disease under investigation and is also related to the risk factor under investigation
Mixing of effects between exposure, the disease and a third factor
Q: Give 2 examples of confounding factors.
A: – factors that have a direct causal link with the disease (e.g. smoking and lung cancer)
– factors that are good proxy measures of more direct unknown causes (e.g. age and social class)
Q: How is confounding accounted for? (4)
A: Account for confounding using matching, randomisation, stratification and multivariate analysis
Q: What are 4 common confounders?
A: Age
Sex
Socio-economic – Poorer people have higher rate of almost all disease – Higher risk of early death in poor people
Geography – Disease prevalence varies greatly by place
Q: What is the judgement of a cause-effect relationship based on?
A: chain of logic that addresses two main areas:
- Observed association between an exposure and a disease is valid
- Totality of evidence taken from a number of sources supports a judgement of causality
Q: What criteria is used for causation?
A: BRADFORD-HILL CRITERIA FOR CAUSATION includes FACTORS TO CONSIDER
Q: What are the 9 suggested criteria for causation? Which is absolutely necessary?
A: Strength Consistency Specificity Temporal relationship - NECESSARY Dose-response relationship Plausibility Experimental evidence Coherance Analogy
Q: Describe strength as a criteria for causation. How is it measured? Weak or strong more likely? (3)
A: – Strength of association measured by magnitude of relative risk.
– Strong association more likely causal than weak association(likely result of confounding or bias)
– BUT weak does not mean non-causal i.e. lung cancer and smoking
Q: Describe consistency as a criteria for causation. Lack of consistency means? (2)
A: – More likely to be causal if similar results in different populations using different study designs- unlikely studies subject to same type of errors.
– A lack of consistency does not exclude a causal association since different exposure levels and other conditions may reduce the impact of the causal factor.
Q: Describe specificity as a criteria for causation. Lack of means? (4)
A: – If a particular exposure increases the risk of a certain disease but not the risk of other diseases then this is strong evidence in favour of a cause-effect relationship e.g. Mesothelioma an aspestos.
– BUT one-to-one relationships between exposure and disease are rare and lack of specificity should not be used to refute a causal relationship e.g. cigarette smoking causes many diseases.
Q: Describe a temporal relationship as a criteria for causation. Establishing in cohort studies//cross sectional//case control? (4)
A: – Essential criterion.
– For a putative risk factor to be the cause of a disease it has to precede the disease.
– Generally easier to establish from cohort studies but rather difficult to establish from cross-sectional or case-control studies when measurements of the possible cause and the effect are made at the same time.
– HOWEVER, it does not follow that a reverse time order is evidence against the hypothesis.
Q: Describe the dose-response relationship as a criteria for causation. (3)
A: - Describes the change in effect on an organism caused by differing levels of exposure (or doses) to a stressor (usually a chemical) after a certain exposure time, or to a food
– Further evidence of a causal relationship is provided if increasing levels of exposure lead to increasing risks of disease.
– Some causal associations, however, show a single jump (threshold) rather than a monotonic trend.
Q: Describe plausibility as a criteria for causation. Taken seriously? (2)
A: – Association is more likely to be causal if consistent with other knowledge (e.g. -animal experiments, biological mechanisms, etc.).
– However, this criterion should not be taken too seriously because lack of plausibility may simply reflect lack of scientific knowledge.
Q: Describe coherence as a criteria for causation. (2)
A: – Implies that a cause and effect interpretation does not conflict with what is known of the natural history.
– BUT absence of coherent information as distinguished from the presence of conflicting information, should not be taken as evidence against an association being causal.
Q: Describe analogy as a criteria for causation.
A: – At best analogy provides a source of more elaborate hypotheses about the association in question.
– Absence of such analogies only reflects lack of imagination or experience, not falsity of the hypothesis
Q: Which 2 criteria are vague and not really important in assessing causation?
A: coherence, analogy
Q: What is weak association likely the result of?
A: confounding or bias
