1. Causation

2. Lessons from John Snow Example
Form a hypothesis of the exposure-disease relationship
Identify appropriate study population, study area, and study period to obtain:
desired exposure contrast
exposure groups comparable on other risk factors (minimize confounding)
best available data on exposure and disease (and other risk factors)

3. Lessons from John Snow Example
Minimize selection, information and confounding biases through:
design of the study
“shoe-leather” epidemiology
Obtain a measure of the exposure-disease (dose-response) relationship
Rule out alternative hypotheses and make causal inference
Snow’s tables and map
Examples of those in cluster area who did not use the Broad Street Pump and did not get the disease
Two cases living outside the area whose only contact with the area was drinking from the pump.

4. Checklist for epidemiological and statistical reviews
From: Rushton L. Reporting of occupational and environmental research:
Use and misuse of statistical and epidemiological methods. Occup Environ Med 2000;57:1–9
Design:
Is there a clear statement of the study objectives?
Is there a clear description of the study design?
Is the study design appropriate?
In there a clear description of the study populations?
Is there a clear description of how the main variables were measured?
Is there an assessment of the adequacy of the sample size?
Presentation and analysis:
Are the statistical procedures adequately described or referenced?
Are the statistical procedures appropriate?
Are the response rates reported?
Are the data and the results adequately described?
Are confidence intervals and significant levels given where appropriate?

5. Checklist for epidemiological and statistical reviews
Bias and confounding:
Are the response or tracing rates satisfactory?
Are you reasonably satisfied that the results are not explained by bias in subject selection or measurement?
As far as you can tell, have confounding variables been taken into account appropriately—either in the analysis or in the discussion?

6. Serious errors and omissions occurring in occupational epidemiology papers
From: Rushton L. Reporting of occupational and environmental research:
use and misuse of statistical and epidemiological methods. Occup Environ Med 2000;57:1–9
Design:
Adequacy of sample size not considered
Bias in selection of subjects
Execution:
Data collection problems and missing data not adequately reported
Non-respondents not investigated
Sample selection and exclusions inadequately justified

7. Serious errors and omissions occurring in occupational epidemiology papers
Analysis:
Inappropriate or incorrect analysis of data (e.g., matched case-control data)
Modeling incorrect — e.g., inadequate adjustment for confounders
Presentation:
Inadequate description of the methodology and statistical procedures
No presentation of risk estimates and their confidence intervals
Interpretation:
Potential bias due to sample selection, no or poor response, missing values, exclusions
Lack of statistical power not considered
Misunderstanding and misinterpretation of results from models

8. What is Causation?
“We may define a cause to be an object followed by another…where if the first object had not been, the second never had existed” (David Hume)
Hume defined 3 properties of a cause:
Association (cause and effect occur together)
Time order (cause precedes effects)
Connection or direction (predictable link between cause and effect)

9. What is Causation?
Pragmatic definition: A cause is something that makes a difference
“The cause of any effect consists of a constellation of components that act in concert.” (JS Mill)
Factors in causation:
Predisposing factors creating enhanced susceptibility to the exposure
Age, sex, previous illness
Enabling factors favoring disease development
SES, poor nutrition, inadequate access to care
Precipitating factors: exposure to the agent
Reinforcing factors:
repeated exposure, exposure to other agents, stress

10. What is Causation? Factors in causation:
Predisposing factors creating enhanced susceptibility to the exposure
Age, sex, previous illness
Enabling factors favoring disease development
SES, poor nutrition, inadequate access to care
Precipitating factors: exposure to the agent
Reinforcing factors:
repeated exposure, exposure to other agents, stress

11. Causal Pie Model Multi-factorial model of causation
Each pie is a causal mechanism (“sufficient cause”) for the disease.
A disease has multiple causal mechanisms.
Each letter represents a causal factor that is a component of a causal mechanism.
A given causal mechanism requires the joint action of several component factors
“A” = necessary cause in this example since it causes disease through all three causal mechanisms.

12. Causal Pie Model Each “pie” represents a “sufficient cause”
A set of minimal conditions and events that inevitably produce the disease
A complete causal mechanism consisting of a constellation of component causes
Requires the joint action of its component causes
Each component cause plays a necessary role
A necessary cause is a component cause in every causal mechanism
A component cause in every “pie”
If absent, then the disease does not occur

13. Examples of component causes in a causal mechanism
Social
pressures
Air pollution
Lack of
exercise
Cigarette
smoking
Work
exposures
diet
Hereditary
factors
Pre-existing
medical
condition

14. Implications of the Causal Pie Model
The “strength” of a component cause depends on the prevalence in the population of the other component causes in the causal mechanism
A factor will appear to have a strong effect if the other component causes are common in the population
A factor will appear to have a weak effect if the other component causes are rare in the population

15. Implications of the Causal Pie Model
No component cause acts alone to produce disease
Every case of a disease is the result of multiple component causes acting jointly in a causal mechanism (“pie”).
Therefore,
each case of a disease could be attributed to each of the separate component causes that make up the causal mechanism (“pie”)
the sum of the fractions of a disease attributable to each of its causes does not equal 100% but instead has no upper limit

16. An example of the naïve view that every case of a disease has a single cause, and that two or more causes cannot contribute to the same case. Rushton L. How much does the environment contribute to cancer? Occupational and Environmental Medicine 2003;60:150–156

17. Causal Inference Two questions:
Is the exposure associated with the disease in our study?
Statistical inference
Obtain a point estimate of effect (e.g., RR)
Evaluate the precision of the estimate of effect
Assess how likely the result is compatible with no exposure effect

18. Causal Inference Two questions:
Is the observed association a causal one?
Causal inference
Weighing the evidence and ruling out chance and bias as causes

19. Inference in survey research
Examples:
Opinion polls
Inferring exposure prevalence or disease rate in a community based on a random sample of that community
Inference is from the sample to the source population from which sample was taken
Requires a random or “representative” sample be taken from the source population
Inference is specific to time and place of the sampling
A key assumption underlying the meaning of P-values and confidence intervals (random sampling) is satisfied

20. Inference in observational studies
The study population usually is not a random or representative sample from a source population.
Implications:
Inference is not from a sample to a source population from which the sample was taken
Inference is from the study population to a population of interest
Inference is based on biological plausibility and other causal criteria
Examples of types of inferences:
Effect of an occupational exposure ⇒ effect in general population
Effect of exposure in one plastics plant ⇒ Effect of exposure in all plastics plants
Effect of high occupational exposure ⇒ effect of low community exposure
Effects in animal study ⇒ effects in humans

21. Inference in observational studies
The purpose of statistical inference:
compare observed result with what would have happened in a hypothetical situation if exposures had no effect and were randomly assigned to the study population over a large number of repetitions.
Problem:
In observational studies, exposures are not randomly assigned
Implications:
Key assumption underlying the meaning of the p-values and confidence intervals (random allocation of exposure) is not satisfied.
Confidence intervals and p-values become indirect, descriptive measures of precision and the compatibility of the observed results with the null and alternative hypotheses

22. Statistical Inference in observational studies
The point estimate (e.g., risk ratio or odds ratio) provides the primary evidence for determining whether an observed exposure-disease association exists in our study
Confidence interval can be used in a descriptive fashion as indirect measure of the precision of the point estimate as well as the compatibility between the observed result and a range of hypothetical results including a result of no effect (i.e., the null hypothesis)
P-value can be used in a descriptive fashion as indirect measure of the extent of compatibility between the observed result and the hypothetical result of no exposure effect (i.e., the null hypothesis)
The confidence interval and p-value provide supplementary information that can help in determining whether an observed exposure-disease association exists in our study

23. Causal Inference
After deciding that an exposure-disease association has occurred in a study, the next question is whether this association is a causal one.
Purpose of causal inference:
to weigh the evidence for a claim that the observed exposure-disease association (also called a “statistical association”) constitutes a causal association
reasoning, using causal “criteria”, to weigh the evidence
rule out chance and biases as causes

24. Causal Inference
Causal criteria are used to weigh the evidence for a causal association
Although the results of an individual epidemiological study can be evaluated, often the results of several studies are evaluated in a qualitative review or a quantitative meta-analysis.
The most commonly used criteria were articulated by the British statistician Austin Bradford Hill in 1965

25. Causal Inference Causal “criteria” (“Viewpoints”) from Hill (1965)
Strength of Association
Consistency
Specificity
Temporality
Biological Gradient
Biological Plausibility
Coherence
Experiment
Analogy

26. Hill’s Viewpoints
“What I do not believe…is that we can usefully lay down some hard-and-fast rules of evidence that must be obeyed before we accept cause and effect.”
(Hill, 1965)
Hill’s viewpoints are not a checklist. A causal association can be inferred even if only a few of the viewpoints are satisfied.

27. Hill’s Viewpoints Strength of Association
Strong associations are more likely to be causal than weak associations because they are less likely to be explained by undetected biases
However, the fact that an association is weak does not rule out causality
Strength of association is measured by the risk ratio, relative risk, odds ratio, or regression coefficient, NOT the p-value

28. Hill’s Viewpoints Consistency
Repeated observation of an association in different populations and under different circumstances and study methods
Lack of consistency does not rule out causality
A causal factor requires the joint action (in the proper sequence) of other factors in the causal mechanism. This may not happen in a specific population.
May be helpful in ruling out a particular bias as the cause of the association, since it is unlikely that the bias would occur across all studies.

29. Hill’s Viewpoints Specificity
Requires that a cause lead to a single effect
The criterion is invalid since a cause (e.g., smoking) can have multiple effects
The criterion may be relevant for some infectious agents

30. Hill’s Viewpoints Temporality
The necessity that the cause precede the effect
This “viewpoint” is the only one that is absolutely necessary for an association to be causal

32. Hill’s Viewpoints Biological Gradient
The presence of a monotonic (unidirectional) dose-response curve
Increasing trend in disease frequency with increasing dose
Lack of a monotonic dose-response does not rule out a causal association
Some causal associations have a threshold dose that must be exceeded before the disease frequency increases
Confounding can produce the appearance of a monotonic dose-response between the exposure and disease
Non-differential exposure misclassification bias can distort a dose-response curve so that it is no longer monotonic

33. Hill’s Viewpoints Biological Plausibility
An hypothesized relationship between an exposure and a health outcome makes sense in the context of current biological knowledge
Not a necessary criterion since “…the association we observe may be new to science or medicine and we must not dismiss it too light-heartedly as just too odd.” (Hill)

34. Hill’s Viewpoints Key issues with Biological Plausibility:
No consensus on the amount of evidence sufficient to establish plausibility
No consensus on rules for assessing relevance or weight of evidence
Data gaps and uncertainties in current knowledge
Overemphasis on plausibility impedes acceptance of new facts, especially those that reveal deficiencies in current knowledge
Evidence underdetermines hypotheses
Biological evidence can support conflicting hypotheses
Assessment of evidence is open to different interpretations

35. Hill’s Viewpoints Coherence
Interpreting an association as causal should not conflict with what is known of the natural history and biology of the disease
Lack of coherence does not rule out a causal association
Conflicting information may be mistaken or misinterpreted

36. Hill’s Viewpoints Experimental Evidence
Hill used the example of a prevention study:
“…Because of an observed association some preventive action is taken. Does it in fact prevent?”
Others interpret Hill to mean evidence from laboratory experiments or human experiments
Experimental evidence is often unavailable therefore lack of such evidence does not rule out a causal association
Experimental evidence underdetermines an hypothesis
There are often several alternative explanations for the outcome of any experiment

37. Hill’s Viewpoints Analogy
“With the effects of thalidomide and rubella before us we would surely be ready to accept slighter but similar evidence with another drug or another viral disease in pregnancy” (Hill)

38. Hill’s Viewpoints
Hill explicitly rejected the usefulness of statistical significance testing for causal inference:
“No formal tests of significance can answer those questions. Such tests can, and should, remind us of the effects that the play of chance can create, and they will instruct us in the likely magnitude of those effects. Beyond that they contribute nothing to the “proof” of our hypothesis.”
“And far too often we deduce ‘no difference’ from ‘no significant difference’.”

39. Proposed Guidelines for Carcinogen Risk Assessment US Environmental Protection Agency, 1996