Transcript Deriving Biological Inferences From Studies

Deriving Biological
Inferences From
Epidemiologic Studies
Dr. Mostafa Arafa
Associate Prof. of Family and
Community medicine
Faculty of Medicine & Medical Sciences
King Khaled University,
S.A.
[email protected]
Learning Objectives
To learn the inferences about a
disease’s etiology that can be
derived from different
epidemiologic studies.
To learn the reasoning by which
epidemiologists select the most
plausible inference.
The first step in the
epidemiologic analysis is the
demonstration of a statistical
relationship between a disease
and a biological characteristic.
The second step is to ascertain
the meaning of that
relationship.
Statistical associations can be
explained as:
1- Artificial
2- Due to association of interrelated
but non-causal variables
3- Due to uncontrolled confounding
4- Causal or etiological
Artificial Association
Artificial association can result
from biased methods of selecting
cases and controls. It may also
arise from biased methods of
recording or obtaining information
by interview. Errors in conduction
or design of the study also may
introduce spurious association.
Non-causal association
An association between many
variables can be observed and still
be non-causal, because many
variables can occur together
without being a part of causal chain.
Examination of interrelated
associations is useful as they may
suggest ways to reduce exposure to
causal
Confounding
If any factor either increasing or
decreasing the risk of a disease besides
the exposure under study is unequally
distributed in the groups that are
compared with regards to the disease,
this will give rise to difference in diseases
frequency in the compared groups. Such
distortion, termed confounding and
variables are called confounder
variables.
E
Etiological Factor
D
Diseases
CF
Confounding factor
Cigarette smoking
Lung cancer
Alcohols
If any study did not control adequately
control for potential confounders, the
inferences drawn from the results may
not be well founded. Studies in which
there was inadequate control of all
known confounders, the results of
which may be explained by an unequal
distribution of extraneous variables in
the study groups and not by the effect
of exposure on disease.
Methods used for controlling of
confounders
A) During the design of the study
* Restriction to a specific group
* Matching
B) During analysis
Stratification & multivariate analysis
Causal association
The logician’s definition of “cause”
is that a factor which must be
necessary and sufficient for the
occurrence of a disease.
The concept “necessary and
sufficient” implies there must be a
one-to-one relationship between the
factor and the disease.
Example of the sufficient cause
for development of a disease
A1
A2
A3
A4
A5
B
Cellular reaction
C
Disease
Example for necessary cause
for development of a disease
A1 + A2 + A3
B
Cellular reaction
C
Disease
Assessing Causality
The following concepts are used in
making a causal inference:
• Strength of association
• Consistency of observed association
• Specificity of the association
• Temporal sequence of events
• Dose-response relationship
• Biological plausibility of association
• Experimental evidence
Strength of association
It is measured by Relative Risk,
and Odds ratio. A strong
association between exposure and
outcome gives support to causal
hypothesis. When a weak
association is present, other
information is needed to support
causality.
Consistency of observed
association
Confirmation by repeated findings
of an association in different
studies, in different population,
and in different settings strengths
the inference of causal
association. It is equivalent to
replication of results in laboratory
experiments.
Specificity of the association
It has been postulated that one
exposure should cause one disease
and no other exposures should cause
the disease. This has its roots in
bacteriological models where one
organism is associated with one
disease. This could not be applied in
chronic diseases as one exposure
could lead to many adverse outcomes
e.g. smoking and cancers, CVD, Birthoutcome.
Temporal sequence of events
It is very obvious that an exposure
must precede the disease to cause
an effect. An example for that is the
prenatal exposure and malformation.
In case control studies the problem
of temporality is quite obvious,
however a cohort design can resolve
the issues of temporality.
Dose – response relationships
The risk of development of a
disease should be related to the
degree of exposure of the causal
factor e.g. duration of estrogen use
and the risk of endometrial cancer,
dosage of smoking and lung
cancer staging.
Biological plausibility
A causal hypothesis must be viewed in
the light of its biological plausibility.
Statistical significant relationship
should be understood in the view of
biological significance. A good example
is cigarette smoking and lung cancer
relationship which was initially
biologically implausible by some, but
carcinogens in cigarettes were
identified, which lent biological
plausibility to the observed
Experimental evidence
Randomized clinical trials is a
well run trial that may confirm a
causal relationship between an
exposure and outcome.
However ethical issues may
prevent the conduction of such
trials.
Summary
Epidemiologic inferences lead to action,
to changes in clinical practice, public
policy, legislation, health education or
new health directions. Epidemiologic
studies can provide very strong support
for hypotheses of either a causal or
indirect association. Inferences from such
studies must take into account all relevant
biological information. Epidemiologic and
other evidence can accumulate to point
where a causal hypothesis becomes
highly probable.