Quality Standards for Real-World Research Focus on
Observational Database Studies of Comparative Effectiveness
Nicolas Roche1, Helen Reddel2, Richard Martin3, Guy Brusselle4, Alberto Papi5, Mike
Thomas6, Dirjke Postma7, Vicky Thomas8, Cynthia Rand9, Alison Chisholm8, and David
1. Cochin Hospital Group, APHP and University Paris Descartes, Paris, France; 2. Woolcock Institute of
Medical Research, University of Sydney, Glebe, Australia; 3. National Jewish Medical and Research
Center, Denver, Colorado; 4. Department of Respiratory Medicine, Ghent University Hospital and Ghent
University, Ghent, Belgium; 5. Research Center on Asthma and COPD, University of Ferrara, Ferrara,
Italy; 6. Academic Primary Care, University of Aberdeen, Foresterhill Health Centre, Aberdeen, UK;
7. Department of Pulmonology, Center Groningen and University of Groningen, Groningen, The
Netherlands; 8. Research in Real Life, Cambridge, United Kingdom; 9. Johns Hopkins School of
Medicine, Baltimore, Maryland; and 10.Centre of Academic Primary Care, University of Aberdeen,
Aberdeen, UK
Aims of the paper
1. Improve knowledge and understanding of
methodological issues specifically related to
comparative observational studies using clinical
and administrative datasets
2. Provide checklists (for researchers and
reviewers) of key markers of quality when
conducting and appraising such studies.
Context: what is real-life research?
• Effectiveness and comparative effectiveness studies aim
to evaluate the (relative) benefits of available therapeutic
options as used in real clinical practice situations (i.e.,
in unselected patients receiving usual care).
• Such studies can use observational or clinical trial
designs but in both cases put emphasis on high
external validity.
• Their goal is to complement classical efficacy
randomized controlled trials (RCTs), with high internal
validity are required for the registration of treatments
Context: why do we need real-life research?
• In asthma, it has been shown that the highly selected
patient populations recruited to registration RCTs
represent less than 5% of the general target patient
• Although these efficacy trials are rigorous in design and
address important questions regarding the risk/benefit
profile of new therapies, their conclusions strictly apply
only to the selected population recruited to the trial
o They are limited in the extent to which they can be
extrapolated to reflect the treatment effects
achievable at the population level.
1. Herland K, et al. Respir Med 2005;99:11–19.
Context: why do we need real-life research?
• Patient characteristics controlled for by tight RCT design,
o Smoking status
o Excess weight,
o Presence of other comorbidities
o Concomitant treatments
o Environmental exposures. Similarly, in RCTs some clinical
management issues that can modulate the
• RCTs also utilize intense patient–physician interaction; control
patient behaviour, reinforce patient education, enforce therapy
adherence, insist on effective inhalation device (for inhaled
2. Peters-Golden M, et al. Eur Respir J 2006;27:495–503; 3. Price DB, et al. Allergy
2006;61:737–742; 4. Thomas M, et al. BMC Pulm Med 2006;6:S4; 5. Molimard M, et al. J
AerosolMed 2003;16:249–254; 6. Giraud V, et al. Respir Med 2011;105:1815–1822; 7. Price D, et
al. Treatment Strategies 2012;3:37–46.
Limitations: RCTs inclusions/exclusions
• Studies have
shown that
efficacy RCTs
exclude about
95% of asthma
and 90% of COPD
routine care
populations due to
strict inclusion
1. Herland K, et al. Respir
Med 2005;99:11–19.
Patient RCT eligibility drop-off with sequential
application of standard inclusion criteria
An integrated evidence base
• To ensure the widest possible generalizability of results,
highly controlled RCTs conducted in highly selected
populations must be complemented by larger studies :
o Performed in target populations (i.e., populations in whom
we intend to use the intervention)
o Settings
o Durations
that mimic the real world.
• The need for such research in asthma has been
advocated by several groups in recent years.8–10
8 Silverman SL. Am J Med 2009;122:114–120.
9 Reddel HK, et al. Am J Respir Crit Care Med 2009;180:59–99.
10 Holgate S, et al. Eur Respir J 2008;32:1433–1442.
REG’s integrated evidence framework
• The Respiratory Effectiveness Group (REG) has proposed a
new framework to enable classification of clinical research
studies in terms of their general design.
• The framework is intended to complement the previously
proposed PRECIS wheel (see later slide)
• The REG framework relies on two axes:
o One describing the type of studied population in relation to
the broadest target population
o The other describing the “ecology of care” (or
management approach) in relation to usual standard of
care in the community.
11. Roche N, et al. Lancet Respir Med 2013;1:e29–e30.
REG’s integrated evidence framework
• The position of a study within the framework serves as a
description of a study, not as a representation of the
quality of evidence it provides.
• The framework is tool for describing the basic
characteristics of the study design and population.
• Multiple studies can be placed relative to each other with
respect to their relevance to the general target
population, and for each study the appropriate quality
assessment tools can be identified.
11. Roche N, et al. Lancet Respir Med 2013;1:e29–e30.
REG’s integrated evidence framework
A means of positioning individual studies with respect to their relevance
to the general target population.
11. Roche N, et al. Lancet Respir Med 2013;1:e29–e30.
Improving Guidelines: PRECIS Wheel
• 9 “spokes,” each representing a
different element of the study
design (e.g., study eligibility
criteria, expertise of individuals
applying the intervention).
• Each spoke, or axis, represents
an explanatory– pragmatic (i.e.,
continuum, and aspects of a
trial are scored/positioned along
each respective axis depending
on the extent to which they
reflect the characteristics of an
explanatory (efficacy) RCT or a
pragmatic effectiveness trial
12. Thorpe KE, et al. CMAJ 2009;180:E47–E57.
Evidence quality assessment tools
RCTs: CONSORT Statement13
Pragmatic trials: CONSORT Statement14
Observational studies in epidemiology: STROBE statement)15
Pharmacoepidemiology and pharmacovigilance studies:
EMA-ENCePPchecklist for study protocols16
• Clinical trial protocols: SPIRIT recommendations17
• Datasets: Quality criteria and minimal datasets requirements for
observational studies – UNLOCK initiative18
• Meta- analyses reporting: QUOROM & PRISMA19
13. Altman DG, et al. Ann Intern Med 2001;134:663–694; 14. Zwarenstein M, et al. BMJ
2008;337:a2390; 15 Vandenbroucke JP, et al. Ann Intern Med 2007;147:W163–W194; 16.
documents/ENCePPGuideofMethStandardsinPE.pdf ;17. Chan A-W,et al. Lancet
2013;381:91–92; 18 Chavannes N, et al. Prim Care Respir J 2010;19:408; 19 Moher D, et
al. Lancet 1999;354:1896–1900; 20. Liberati A, et al. BMJ 2009;339:b2700.
Quality Standards: Observational studies
• Traditional Perception:
o Observational studies provide weak evidence to support
treatment recommendations
o Efficacy RCTs represent the top-level evidence in many
• RCTs cannot be non- interventional so questions of “usual
care” are best addressed via alternative means.
• Guidelines should include difference sources of evidence
and acknowledging that evidence from well-designed
observational studies may be moderate (or even strong)
if the treatment effect is large and the evaluation has
accounted for all of the plausible confounders and biases
in properly adjusted analyses.
Quality Standards: reporting
• Whatever the design of a study type, it is crucial that it is
reported in such a way that the:
o appropriateness and quality of the chosen
o relevance of the results can be assessed
by readers (e.g., care givers, researchers, guidelines
developers, policy makers, patients associations, journal
editors, and reviewers) so they can determine whether
(and how) they should use the findings
Observational studies: key limitations
• Main potential limitations of observational studies are:
o Selection bias (e.g., confounding by severity or
indication –differential prescribed based on unevaluable
patient characteristics)
o Information bias (e.g., data that leads to misclassificaton);
o Recall bias (when assessment of treatment exposure
and/or outcomes depend on patients’ or caregivers’
recall), and
o Detection bias (when an event of interest is less, or
more, likely to be captured in one treatment
group than in the other)
Observational studies: Preparation (I)
• A priori planning, prospective design – helps avoid “fishing”
strategies– and should specify:
o The purpose of the study (i.e., hypotheses to test)
o Primary and secondary outcomes
o Study design (i.e., cross-sectional or longitudinal)
o Pre-specified analyses – ensures all potentially relevant
variables required to characterize patients are included
Observational studies: Preparation (II)
A suitable database to answer the key study question should be selected
A reliable, identifiable index event should be defined (e.g. treatment
A detailed database extraction and statistical analysis plan must then
be prepared
The study population and subgroups of interest must be precisely
To reduce potential bias, possible confounders should be identified and
accounted for appropriately by matching and/or adjustment strategies.
A dedicated independent steering committee should guide these steps
The preparation process for observational studies should include the
registration and, if possible, the publication of a study protocol in a public
repository, with a commitment to publish regardless of results.
Observational studies: Analyses and reporting
• Demonstrate the robustness of results by:
o Assessing whether the studied database population is
representative of the target patients
o Establishing the consistency of results through sensitivity
analyses and across relevant patient subgroups and
o Demonstrating their reproducibility in different datasets
where similar criteria have been used to define the target
populations, index events and outcomes.
• Use of the same pre-defined population for all components of
analyses (e.g. effectiveness, tolerance and medico-economic
• The process of reporting results reporting should begin with a
flow chart detailing patient selection
Observational studies: Analyses and reporting
• Patient characteristics – demographic and medical
(including markers of disease severity, comorbidities and
concomitant treatments) should be described in detail and
compared between treatment groups.
• Use of patient matching or statistical modeling to adjust
for differences between treatment arms
• The results of all analyses that are conducted (e.g.
matched, unmatched, adjusted and unadjusted) results
should be reported to help demonstrate the robustness of
the chosen method of analysis
Observational studies: Matched analyses
• Matching if the differences are too great to apply
• adjusted analyses alone, matching should be considered as an
additional tool for ensuring similarity of patients based on key
demographic characteristics and markers of disease severity.
• This can be done using:
o Propensity scores – patients are assigned a score based on
their baseline profile and matched to other patients with a
similar score
o Matching individual patients using a predefined set of key
matching criteria
• Both of these processes require close liaison between medical
experts and statisticians to agree on suitable criteria for
Observational studies: Discussion of results
• Discussion has to address the specific aspects of the
study design.
• Consider the results from the perspective of the initial
hypotheses before being viewed from a broader
perspective – do they confirm or contradict the underlying
study hypothesis.
• Set the results of observational database effectiveness
studies within context by comparing them to those of
efficacy RCTs on the same topic.
o If they differ could it be because of unaddressed
bias in the observational study?
Observational studies: Discussion of results
• The authors should present the rationale for their
analysis approach and discuss whether they feel it has
successfully reduced the risk of bias.
• Limitations of the study must also be acknowledged.
• Conclusions should be qualified with a note about the
level of confidence that readers should have in the
reliability, robustness and generalizability of results (i.e.,
the level of evidence provided by their study) and new
studies should be suggested to challenge, strengthen or
extend the conclusions.
Quality criteria for observational database comparative studies
Quality criteria
Clear underlying hypotheses and specific research question(s)
Study design
Observational comparative effectiveness database study
Independent steering committee involved in a priori definition of the study methodology (including statistical analysis
plan), review of analyses and interpretation of results
Registration in a public repository with a commitment to publish results
High-quality database(s) with few missing data for measures of interest
Validation studies
Clearly defined primary and secondary outcomes, chosen a priori
The use of proxy and composite measures is justified and explained.
The validity of proxy measures has been checked.
Length of observation
Sufficient duration to reliably assess outcomes of interest and long-term treatment effects
Well-described inclusion and exclusion criteria, reflecting target patients’
characteristics in the real world
Study groups are compared at baseline using univariate analyses.
Avoid biases related to baseline differences using matching and/or adjustments.
Sensitivity analyses are performed to check the robustness of results.
Sample Size
Sample size is calculated based on clear a priori hypotheses regarding the occurrence of outcomes of interest and
target effect of studied treatment vs. comparator.
Flow chart explaining all exclusions
Detailed description of patients’ characteristics, including demographics, characteristics of the disease of interest,
comorbidities, and concomitant treatments
If patients are lost to follow-up, their characteristics are compared with those of patients
remaining in the analyses.
Extensive presentation of results obtained in unmatched and matched populations (if matching was performed)
using univariate and multivariate, unadjusted and adjusted analyses
Sensitivity analyses and/or analyses of several databases go in the same direction as
primary analyses.
Summary and interpretation of findings, focusing first on whether they confirm or contradict a priori hypotheses
Discussion of differences with results of efficacy randomized control trials
Discussion of possible biases and confounding factors, especially related to the observational nature of the study
Suggestions for future research to challenge, strengthen, or extend study results
Conclusions (I)
• An integrated approach to evidence evaluation combines
data of high internal validity (classical RCTs) with those
of greater external validity (pragmatic trials and
observational studies) to inform clinical decision making,
guidance, and policy.
• This requires the reliability and generalizability of
different study designs to first be determined:
o Characterisation of the study in terms of
generalizability of its ecology of care and study
population, then
o Assessment of its quality (using design-specific tools)
Conclusions (II)
• Further work is required in this area to turn the wellintended calls for better integration of different study
approaches into meaningful action.
• A systematic review of the existing respiratory guidelines
is required to identify where real-world studies can add
useful complementary data.
• There is also a need to test the REG’s integrated
• framework, to apply it to published research, and to use it
to critically appraise the quality of the existing real-world
evidence base.
• The REG plan to undertake these activities
Conclusions (III)
• Until the REG’s systematic reviews are complete, the paper
seeks to bring together the various challenges and
considerations faced by those conducting and reviewing
observational research and to provide useful checklists of key
quality markers for observational research.
• The checklists should be used as guidance such that their
principles of: a priori planning, appropriate analysis and
transparency should be embodied for all those seeking to
conduct high-quality observational research and recognized
by those appraising it

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