Download Causal Inference in Epidemiology

Causal Inference in Epidemiology
Ahmed Mandil, MBChB, DrPH
Prof of Epidemiology
High Institute of Public Health
University of Alexandria
Headlines
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Levels of causality
Definitions
Koch's postulates (1877)
Hill's criteria (1965)
Susser's criteria (1988, 1991)
Relating
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Exposures: causes, risk factors,
independent variables to
Outcomes: effects, diseases, injuries,
disabilities, deaths, dependent
variables
Statistical association versus
biological causation: cause-effect
relationship
Levels / Types of causality
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Molecular / Physiological
Personal / Social
Deterministic / probabilistic
What aspect of “environment” (broadly
defined) if removed / reduced /
controlled would reduce outcome /
burden of disease
Definitions (I)
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Deduction: reasoned argument proceeding from
the general to the particular.
Induction: any method of logical analysis that
proceeds from the particular to the general.
Conceptually bright ideas, breakthroughs and
ordinary statistical inference belong to the realm
of induction.
Induction period: the period required for a
specific cause to produce the disease (healthrelated outcome). Usually longer with NCDs
Definitions (II)
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Association (relationship): statistical dependence between
two or more events, characteristics or other variables.
Positive association implies a direct relationship, while
negative association implies an inverse one. The presence
of a statistical association alone does not necessarily
imply a causal relationship.
Causality (causation / cause-effect relationship): relating
causes to the effects they produce.
Cause: an event, condition, characteristic (or a
combination) which plays an important role / regular /
predicable change in occurrence of the outcome (e.g.
smoking and lung cancer)
Causes may be “genetic” and / or “environmental” (e.g.
many NCDs including: diabetes, cancers, COPD, etc)
Definitions (III)
Deterministic causality: cause closely
related to effect, as in “necessary” /
“sufficient” causes
 Necessary cause: must always PRECEDE
the effect. This effect need not be the sole
result of the one cause
 Sufficient
cause: inevitably initiates or
produces an effect, includes “component
causes”
Any given cause may be necessary, sufficient,
both, neither (examples)
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Definitions (IV)
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Component causes: together they
constitute a sufficient cause for the
outcome in question. In CDs, this may
include the biological agent as well as
environmental conditions (e.g. TB,
measles, ARF/RHD). In NCDs, this may
include a whole range of genetic,
environmental as well as personal /
psychosocial / behavioral characteristics
(e.g. diabetes, cancers, IHD)
Definitions (V)
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Probabilistic Causality: in epidemiology,
most associations are rather “weak” (e.g.
relationship between high serum cholesterol
and IHD), which is neither necessary nor
sufficient
Multiple causes result in what is known as
“web of causation”or “chain of causation”
which is very common for noncommunicable
/ chronic diseases
Effect Measures /
Impact Fractions
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Effect measures (e.g. odds ratio, risk
ratio) and impact fractions (e.g.
population attributable risk) are closely
related to the strength of association
The higher effect measures (away from
unity) and population attributable risk
(closer to 100 %) the more the exposure is
predictive of the outcome in question
E.g. PAR of 100 % means that a factor is
“necessary”
Deterministic causality (I)
Deterministic causality (II)
Deterministic causality (III)
Deterministic causality (IV)
Deterministic causality (V)
Deterministic causality (VI)
Definitions (IV)
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Predisposing factors: factors that prepare, sensitize,
condition or otherwise create a situation
(such as level
of immunity or state of susceptibility) so that the host
tends to react in a specific fashion to a disease agent,
personal interaction, environmental stimulus or specific
incentive. Examples: age, sex, marital status, family size,
education, etc. (necessary, rarely sufficient).
Precipitating factors: those associated with the definitive
onset of a disease, illness, accident, behavioral response,
or course of action. Examples: exposure to specific
disease, amount or level of an infectious agent, drug,
physical trauma, personal interaction,
occupational
stimulus, etc.
(usually necessary).
Weighing Evidence
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At individual level: clinical judgment (which
management scheme)
At population level: epidemiological
judgment (which intervention)
When weighing evidence from
epidemiological studies, we use “causal
criteria” (usually applied to a group of
articles, to deal with confounding) e.g. Hill’s
/ Susser’s criteria, which were preceded by
Koch’s postulates (on infectious diseases)
Henle-Koch's postulates (1877,1882)
Koch stated that four postulates should be met before a
causal relationship can be accepted between a
particular bacterial parasite (or disease agent) and the
disease in question. These are:
1. The agent must be shown to be present in every case
of the disease by isolation in pure culture.
2. The agent must not be found in cases of other
disease.
3. Once isolated, the agent must be capable of
reproducing the disease in experimental animals.
4. The agent must be recovered from the experimental
disease produced.
Hill's Criteria (1897 - 1991)
The first complete statement of the epidemiologic
criteria of a causality is attributed to Austin Hill (1897 1991). They are:
1.
Consistency (on replication)
2.
Strength (of association)
3.
Specificity
4.
Dose response relationship
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Temporal relationship (directionality)
6.
Biological plausibility (evidence)
7.
Coherence
8.
Experiment
Consistency (I)
Consistency (II)
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Meta-analysis is an good method for
testing consistency. It summarizes
odds ratios from various studies,
excludes bias
Consistency could either mean:
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Exact replication (as in lab sciences,
impossible in epidemiological studies)
Replication under similar circumstances
(possible)
Strength of Association
Expressions of Strength of Association
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Quantitatively:
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Effect measure (OR, RR): away from unity (the
higher, the stronger the association)
P-value (at 95% confidence level): less than 0.05
(the smaller, the stronger the association)
Qualitatively:
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Accept alternative hypothesis: an association
between the studied exposure and outcome exists
Reject null hypothesis: no association exists
Dose-response relationship (I)
Dose-response relationship (II)
Time-order (temporality, directionality)
Time order
Specificity of Outcome
Specificity of Exposure
Coherence
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Theoretical: compatible with pre-existing
theory
Factual: compatible with pre-existing
knowledge
Biological: compatible with current
biological knowledge from other species or
other levels of organization
Statistical: compatible with a reasonable
statistical model (e.g. dose-response)
Biological Coherence (I)
Biological Coherence (II)
Susser's criteria (I)
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Mervyn Susser (1988) used similar
criteria to judge causal
relationships.
In agreement with previous authors,
he mentioned that two criteria have
to be present for any association
that has a claim to be causal: i.e.
time order (X precedes Y); and
direction (X leads to Y).
Susser’s Criteria (II)
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Rejection of a hypothesis can accomplished
with confidence by only three criteria: time
order, consistency, factual incompatibility or
incoherence.
Acceptance or affirmation can be achieved
by only four, namely: strength, consistency,
predictive performance, and statistical
coherence in the form of
regular
exposure/effect relation.
Comparison of Causal Criteria
References
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6.
Porta M. A dictionary of epidemiology. New York,
Oxford: Oxford University Press, 2008.
Rothman KJ (editor). Causal inference. Chestnut Hill:
Epidemiology Resources Inc., 1988.
Hill AB. The environment and disease: Association or
causation.
Proceedings of the Royal Society of
Medicine 1965; 58: 295-300.
Susser MW. What is a cause and how do we know one ? A
grammar for pragmatic epidemiology. American
Journal of Epidemiology 1991; 133: 635-648.
Paneth N. Causal inference. Michigan State University.
Rothman J, Greenland S. Modern epidemiology. Second
edition. Lippincott - Raven Publishers, 1998.
Thank you for your kind attention