Association and causation

Question 1

What does a p value of 0.33 denote

Answer 1

33% chance that the result was due to chance.

Question 2

What does the p value usually have to be for the results to be statistically significant

Answer 2

p < 0.05.

Question 3

What is an association

Answer 3

Association refers to the statistical dependence between two variables, that is the degree to which the rate of disease in persons with a specific exposure is either higher or lower than the rate of disease without that exposure.
It is a link, relationship or correlation.

Question 4

At what stage do we have to think about bias

Answer 4

We have to think about bias at the design stage, as it is hard to eliminate during the analysis.

Question 5

When evaluating a statistical association what do we have to consider

Answer 5

Consider chance, bias, confounding, cause

Question 6

How can we get around chance in a study

Answer 6

Larger sample size, so it is representative of the population, reduces the probability that the results are due to chance.
Power calculations
P values and statistical significance

Question 7

How do we assess the role of chance

Answer 7

• Most studies based on an estimate from samples • The role of chance can be assessed by performing appropriate statistical significance tests and by calculating confidence intervals

Question 8

Describe a confidence interval

Answer 8

• 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

Question 9

Describe the p value

Answer 9

• The probability that a result could simply be due to chance
• Threshold usually <0.05 = 1/20 – ie if p<0.05 we can be pretty sure (at least 95% certain) that result of a study is not due to chance – If p>0.05 then result could be due to chance

Question 10

What is meant by bias

Answer 10

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. The observed effect will be either above or below the true value, depending on the nature of the systematic error.
There are two types:
Selection bias
Measurement bias

Question 11

Describe bias

Answer 11

• Biasis a consequence of defects in the design or execution of an epidemiological study. • Biascannot be controlled in the analysis of a study, and it cannot be eliminated by increasing the sample size.

Question 12

What is meant by selection bias

Answer 12

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.

Question 13

What is meant by measurement bias

Answer 13

Measurement (or information) – occurs when measurements or classifications of disease or exposure are inaccurate

Question 14

Give some examples of selection bias

Answer 14

– Non-response bias – Healthy entrant effect e.g. “healthy worker” – Loss to follow-up (attrition bias)

Question 15

Give some examples of measurement bias

Answer 15

Recall bias- people with a condition are more likely to recall events around a diagnosis and are likely to associated these events with the disease. Common in case-control studies. Different accuracies of equipment is also an example of measurement bias.

Question 16

How can we reduce selection bias

Answer 16

Take random samples from the population- no choice in selection.

Question 17

What is meant by confounding

Answer 17

• A potential confounder is 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. • This includes – 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).

Question 18

Give some examples of confounding

Answer 18

• Age • Sex • Socio-economic status – Poorer people have rates of almost all diseases • Geography – Disease prevalence varies greatly by place – North and South
Old more likely to die earlier, women likely to die earlier, poorer people more likely to die earlier

Question 19

Provide a scenario where confounding can interfere with causality

Answer 19

• Coffee consumption is associated with the risk of cancer of the pancreas. • Disputed because coffee consumption is correlated with cigarette smoking, and cigarette smoking was known to be a risk factor for pancreatic cancer.

Question 20

How could you reduce confounding

Answer 20

Restrict analysis to individual groups- high deprivation, low deprivation
Standardise for age and sex in results
Regression techniques which look at effect of individual risk factors put in anlayis and calculate that independently from all other risk factors, dissociating confounding from the associations.
▪ Design Stage: • Restriction – Inclusion/exclusion criteria. • Randomisation. ▪ Analysis Stage: • Stratification – risks are calculated separately for each confounding variable. • Standardisation. • Regression analysis.

Question 21

When do we consider cause

Answer 21

Once, bias, chance and confounding have been analysed/eliminated.

Question 22

How do we judge causality

Answer 22

• Judgement 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

Question 23

What is the purpose of Bradford-hill criteria

Answer 23

It is a tool to help us judge causality. Not all the criterion are essential. TEMPORAL is essential however.

Question 24

What are the factors to consider

Answer 24

Factors to consider • Temporal relationship • Plausibility • Consistency with other investigations • Strength of the association • Dose-response relationship • Specificity • Experimental evidence • Coherence • Analogy also consider reversibility

Question 25

Describe the role of strength

Answer 25

The strength of an association is measured by the magnitude of the relative risk. A strong association is more likely to be causal than is a weak association, which could more easily be
the result of confounding or bias. However, a weak association does nor rule out a causal connection. For example, passive smoking and lung cancer.

Question 26

Describe the role of consistency

Answer 26

If similar results have been found in different populations using different study designs then the association is more likely to be causal since it is unlikely that all studies were subject to the same type of errors. However, 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 in certain studies.

Question 27

Describe the role of specificity

Answer 27

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. However, one-to-one relationships between exposure and disease are rare and lack of specificity should not be used to refute a causal relationship; for example cigarette smoking causes many diseases

Question 28

Describe the role of temporal relationship

Answer 28

This is an essential criterion. For a putative risk factor to be the cause of a disease it has to precede the disease. This is 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

Question 29

Describe the role of dose-response relationship

Answer 29

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.

Question 30

Describe the role of plausibility

Answer 30

The 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. The idea of microscopic animals or animalcules as cause of disease was distinctly implausible before Van Leeuwenhoek’s microscope

Question 31

describe the role of coherence

Answer 31

Coherence implies that a cause and effect interpretation does not conflict with what is known of the natural history. However absence of coherent information as distinguished from the presence of conflicting information, should not be taken as evidence against an association being causal. quite vague

Question 32

Describe the role of experimental evidence

Answer 32

Experimental evidence on humans or animals. Evidence from human experiments is seldom available and animal research relates to different species and different levels of exposure.

Question 33

Describe the role of analogy

Answer 33

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 (Bradford Hill 1965).

Question 34

What is meant by reversibility

Answer 34

If you take away the exposure, does the risk of the outcome go down