Week 2, Day 1. Conducting population-based research

International Epidemiological Association
6th International Course on Epidemiological Principles and Methods
Vilnius, Lithuania, 5-16 May 2014
Week 2, Day 1. Conducting
population-based research: the
Eastern European experience
Practical Session 39:
Response Rates, Selection Bias and
Representativeness
by Vladimir M. Shkolnikov, David A. Leon,
Domantas Jasilionis
May 12, 2014
1
Outline
•  A short introduction.
•  Preparatory work in groups.
Issue of representativeness. Discussion in the International
Journal of Epidemiology 2013; 42: 1012-1028.
Rothman et al. vs. Ebrahim&Davey Smith.
•  Discussion between groups (20 min). Arguments pro and contra
representativeness. Considerations and examples in
support of R or E&DS
•  Joint discussion (30 min). All groups together.
2
Sequence of work
Short explanation
in the conference
room 10 min
Preparatory work
of six groups in
three rooms
(40 min) plus
presentations
pro and contra
2x10 min
Joint discussion in
the conference
room 20 min
Introduction
Y
N
Y
N
Y
N
Summary
3
K.Rothman et al: Representativeness to be avoided
•  Against a view that a study is not worth undertaking if representativeness
cannot be achieved.
•  John Snow. Cholera and water source in London. A non-representative
population sample with comparable characteristics except for the source of
their water consumption.
•  Doll and Hill. Mortality of male British doctors depending on smoking. An
unrepresentative sample of all tobacco users. Clear research question and
use of samples allowing to built statistical evidence to answer the question.
•  Scientific generalization relates to circumstances in which a finding applies.
•  A study of therapeutic efficacy of a drug. Alternatives: A-subjects aged 40 to
49 only; B-equal numbers of subjects from groups 20-29, 40-49, and 60-69;
C-according to age structure of the general population. To see how the
effect varies by age, option B is preferable.
•  If a study population is representative, it does itself deliver a valid scientific
evidence. The overall associations observed in the study population may
not apply to every subgroup. Result obtained from a two-sex population,
4
does not necessarily apply to males or to females.
K.Rothman et al: Representativeness to be avoided (2)
•  Representative sampling is needed: a) for control sampling in some casecontrol studies; b) descriptive purposes – e.g. prevalence studies; c)
population impact studies.
•  A divide between a goal of understanding a causal relationship and a
practical goal of applying this knowledge to a population. The first goal is not
enhanced by representativeness. It depends on tightly controlled
comparisons drawn over a variety of relevant settings. It is the second goal
of the application of science that may require representativeness.
•  Surveys of opinions, of the prevalence of disease, of habits or of
environmental exposures may be informative, but they are not science in the
same way that causal studies about how nature works.
5
E.Shah&G.Davey Smith: we should not be deliberately
non-representative
•  We are concerned that the notion of Rothman et al will become an
accepted wisdom in epidemiology.
•  The purpose of epidemiology is not simply to assess causal hypotheses.
Rothman et al.: causal hypotheses testing is a “science” and descriptive
epidemiology is not a “science”.
•  Non-representative study groups may produce biased associations.
Multiple risk factors may be associated with participation in a study.
•  In a large volunteer cohort of the American Cancer Society, high alcohol
consumption was associated with reduced risk of stroke. What heavy
drinker would volunteer for a study about the health effects of their lifestyle?
Heavy drinkers in the cohort are unlikely to be representative of all heavy
drinkers (they may be more non-smoking, exercising etc.) and (therefore)
exposed to a lower risk of stroke.
•  Randomised trials: internal validity may be achieved by limiting recruitment
to narrowly defined group. Criticized for not recruiting adults of older age
with multiple morbidities (potential users).
6
E.Shah&G.Davey Smith: we should not be deliberately
non-representative
•  UK Biobank Study. 500 thousand participants, reponse rate = 5.5%.
Genetic variants are unlikely to be associated with self-selection to the
study. Leads to unbiased purely genetic associations but non-genetic
associations should be interpreted with caution.
•  Age of “Big data”. It might be possible to predict insurance risk from
hundreds of variables including amount of TV watching, use of various
Internet sites etc. instead of performing lab tests. But epidemiologists are
not ready to abandon the difficult business of characterizing causality.
7
References
-  Rothman K.J., Gallacher J.E.J., Hatch E. 2013. Why representativeness should be
avoided. International Journ Epidemiology 42: 1012-14.
-  Elwood J.M. 2013. On representativeness. International Journ Epidemiology 42: 1014-15.
-  Nohr E.A., Olsen J. 2013. Commentary: epidemiologists have debated
representativeness for more than 40 years – has the time come to move one?
International Journ Epidemiology 42: 1016-17.
-  Richardi L., Pizzi C., Pearce N. 2013. Commentary: Representativeness is usually not
necessary and often should be avoided. International Journ Epidemiology 42: 1018-22.
-  Ebrahim S., Davey Smith G. 2013. Commentary: should we always deliberately be
non-representative? International Journ Epidemiology 42: 1022-26.
-  Rothman K.J., Gallacher J.E.J., Hatch E. 2013. Rebuttal: when it comes to scientific
inference, sometimes a cigar is just a cigar. International Journ Epidemiology 42:
1026-28.
8
An example. How to capture risk of alcohol drinking
9

Download Report