Mistakes in ... Booklet 2020 - 35

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Mistakes in... 2020

Mistake 10 Going it alone

Type of bias, misclassification
or confounding

Description

Sampling bias

Differences or errors introduced during the enrolment of study
participants compared with the target population

Control selection bias

A type of selection bias when controls are not truly representative of the
population from which the cases were drawn

Attrition/follow-up bias

Unequal loss of participants during a study; can be due to differential
follow-up of participants dependent on their exposure status

Nondifferential misclassification

Deviation in the measurement of one or more factors of interest in a study
that impacts all groups equally

Differential misclassification/
information bias

Deviation in the measurement of one or more factors of interest in a study
that impacts one group more than another

Observational bias

Differential recording of observations based on known exposure or
outcome status

Recall bias

Increased or decreased likelihood that a participant will remember
accurately an exposure based on their outcome status

Reporting bias

Increased or decreased likelihood that a participant chooses to reveal
or suppress certain information; more likely to occur if there is a stigma
attached (e.g. alcohol use)

Confounding

Arises from the nonrandom distribution of risk factors in the population
being studied. A confounding variable is associated both with the disease
and exposure of interest-if measured it can be adjusted for during the
data analysis. Confounding can be minimized or removed during the
study design process by restricting the study population or matching the
population based on certain known (or suspected) confounding factors.

Table 1 | Bias, misclassification and confounding. This is not an exhaustive list of terms or considerations in
bias, misclassification and confounding.

good mentors and experienced collaborators
(or those with special attributes relevant to the
project, such as access to a unique subset of
patients) can provide tremendous support-with
their input it may be possible to turn an
unfeasible project into a project that can be successfully completed.

Mistake 9 Overlooking ethical and
regulatory aspects
Overlooking ethical and regulatory aspects is no
less important a mistake than any of the others
already considered, but such considerations are
not unique to epidemiological studies. Studies
recruiting participants or using data obtained
from humans (most epidemiological studies)
have specific ethical considerations that were
enshrined in the Declaration of Helsinki in 1964
and are updated periodically at the World Medical
Association General Assembly.24 Different
considerations are required for studies involving
animals (including ensuring the welfare of animals
and the principles of replacement, reduction and
refinement). Ethical conduct, in clinical practice
and in research, should always be front and centre
in a clinician's mind.
A study protocol may be well designed to
answer the research question but fall short of
meeting ethical and regulatory requirements.
For example, collecting 50 ml of blood may be
sufficient to run all the tests needed for a new study
on necrotizing enterocolitis in preterm babies, but
such a blood draw may be considered unethical in

this population. This point also corresponds with
issues of feasibility (see mistake 8), but it is to be
hoped that such a request would not be approved
by the local review board for ethical reasons
before empirical data indicated the feasibility
challenge.
Ethical and regulatory aspects should,
therefore, be integrated into the study design
thought process, so that the end result is a
protocol that would meet ethical expectations,
receive the necessary approvals and meet
publishing standards. For studies involving
humans, the informed consent process,
risk/benefit ratio, conflicts of interest, adherence
to good clinical practice (GCP), and a clear
distinction between routine clinical practice
and study-related procedures are a partial list of
the elements that need to be addressed when a
new application for study approval is submitted.
Specific considerations regarding the use of data
are covered in the EU's General Data Protection
Regulation (GDPR) and most ethics applications
now also require data management and
processing considerations.25
Of course, different regions may have different
regulatory requirements, and these can also vary
based on the role of the researcher in the study
staff. For early-stage investigators, it is important to
be aware of the requirements that have to be met
both on the personal and protocol-associated level.
In addition to the often-required formal training,
having a good mentor and consulting colleagues
may prove invaluable when trying to overcome any
hurdles being faced for the first time.

For many of the mistakes discussed above, our
opinion is that early collaboration and
consultation with other colleagues and experts is
the best course of action. This includes, but is
not limited to, working with other clinicians,
epidemiologists, statisticians, triallists, ethicists,
methodologists, librarians, clinical coders,
patients and public representatives. Indeed,
trying to go it alone is invariably a mistake
because collaboration and consultation generate
the best, and most enjoyable, research, allowing
us to question each other's positionality, to refine
our thinking, and design the best study available
to answer our question. For certain questions, the
magnitude of what we hope to achieve can
only be attained through multisite, perhaps
multicountry, collaboration. Fostering a good
network from an early stage of your career,
building relationships, and not being afraid to
drop a potential collaborator an email or DM, can
all lead to fruitful research opportunities and
ultimately better designed and conducted
studies.
References
1. Hulley S, et al. Designing clinical research. 3rd ed.
Philadelphia: Lippincott Williams and Wilkins, 2007.
2. Haynes RB, et al. How to do clinical practice
research: a new book and a new series in the
Journal of Clinical Epidemiology. J Clin Epidemiol
2006; 59: 873-875.
3. Booth A, Papaioannou D and Sutton A. Systematic
approaches to a successful literature review. London:
Sage Publications Ltd, 2012.
4. Poklepović Peričić T and Tanveer S. 'Why systematic
reviews matter. A brief history, overview and practical
guide for authors', Elsevier Authors' update [https://
www.elsevier.com/connect/authors-update/whysystematic-reviews-matter] (2019, accessed 28
August, 2020).
5. Mahood Q, Van Eerd D and Irvin E. Searching for grey
literature for systematic reviews: challenges and
benefits. Research synthesis methods 2014; 5: 221-234.
6. Richardson WS, et al. The well-built clinical question:
a key to evidence-based decisions. ACP J Club 1995;
123: A12-A13.
7. Guyatt G, et al. Evidence-based medicine. A new
approach to teaching the practice of medicine. JAMA
1992; 268: 2420-2425.
8. Straus SE, et al. Evidence-based medicine: How to
practice and teach it. 4th ed. Edinburgh: Churchill
Livingstone, Elsevier, 2010.
9. Guyatt GH, et al. Users' guides to the medical
literature. IX. A method for grading health care
recommendations. JAMA 1995; 274: 1800-1804.
10. Deaton A and Cartwright N. Understanding and
misunderstanding randomized controlled trials.
Social Science & Medicine 2018; 210: 2-21.
11. Bonita, R, et al. Basic epidemiology. 2nd ed. World
Health Organization, 2006.
12. Maathuis MH and Nandy P. A review of some recent
advances in causal inference. In: Bülhmann P, et al.
(eds) Handbook of Big Data. New York, Chapman and
Hall/CRC, TaylorFrancis, 2016.
13. Sackett DL and Wennberg JE. Choosing the best
research design for each question. BMJ 1997;
315: 1636.
14. Allen M. Defining variables, In: The SAGE Encyclopedia
of Communication Research Methods. USA, SAGE
Publications, Inc., 2017.
15. Nelson EC, et al. Patient reported outcome measures
in practice BMJ 2015; 350: g7818.

http://https:// http://www.elsevier.com/connect/authors-update/why

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