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Quality Standards for Real-World Research Focus on Observational Database Studies of Comparative Effectiveness.

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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 Price10

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
Aims of the paper
  • Improve knowledge and understanding of methodological issues specifically related to comparative observational studies using clinical and administrative datasets
  • Provide checklists (for researchers and reviewers) of key markers of quality when conducting and appraising such studies.
context what is real life research
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 observationalor 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
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 population.1
  • 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
    • 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 research1
Context: why do we need real-life research?
  • Patient characteristics controlled for by tight RCT design, include:2–5
    • Smoking status
    • Excess weight,
    • Presence of other comorbidities
    • Concomitant treatments
    • 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 therapies)6,7

2. Peters-Golden M, et al. EurRespir 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. RespirMed 2011;105:1815–1822; 7. Price D, et al. Treatment Strategies 2012;3:37–46.

limitations rcts inclusions exclusions
Limitations: RCTs inclusions/exclusions

Patient RCT eligibility drop-off with sequential application of standard inclusion criteria

  • Studies have shown that efficacy RCTs exclude about 95% of asthma and 90% of COPD routine care populations due to strict inclusion criteria.1



1. Herland K, et al. Respir Med 2005;99:11–19.

an integrated evidence base
An integrated evidence base
  • To ensure the widest possible generalizabilityof results, highly controlled RCTsconducted in highly selected populations must be complemented by larger studies :
    • Performed in target populations (i.e., populations in whom we intend to use the intervention)
    • Settings
    • 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 RespirCrit Care Med 2009;180:59–99.

10 Holgate S, et al. EurRespirJ 2008;32:1433–1442.

reg s integrated evidence framework
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:
    • One describing the type of studied population in relation to the broadest target population
    • 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 framework1
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 qualityof 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 framework2
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
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., efficacy–effectiveness) 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
Evidence quality assessment tools
  • RCTs: CONSORT Statement13
  • Pragmatic trials: CONSORT Statement14
  • Observational studies in epidemiology: STROBE statement)15
  • Pharmacoepidemiology and pharmacovigilance studies: EMA-ENCePPchecklistfor 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. ENCePP:

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
Quality Standards: Observational studies
  • Traditional Perception:
    • Observational studies provide weak evidence to support treatment recommendations
    • Efficacy RCTs represent the top-level evidence in many guidelines.


  • RCTscannot 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
Quality Standards: reporting
  • Whatever the design of a study type, it is crucial that it is reported in such a way that the:
    • appropriateness and quality of the chosen methodology
    • 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
Observational studies:key limitations
  • Main potential limitations of observational studies are:
    • Selection bias (e.g., confounding by severity or indication–differential prescribed based on unevaluable patient characteristics)
    • Information bias (e.g., data that leads to mis-classificaton);
    • Recall bias(when assessment of treatment exposure and/or outcomes depend on patients’ or caregivers’ recall), and
    • Detection bias (when an event of interest is less, or more, likely to be captured in one treatmentgroup than in the other)
observational studies preparation i
Observational studies:Preparation (I)
  • A priori planning, prospective design – helps avoid “fishing” strategies– and should specify:
    • The purpose of the study (i.e., hypotheses to test)
    • Primary and secondary outcomes
    • Study design (i.e., cross-sectional or longitudinal)
    • Pre-specified analyses – ensures all potentially relevant variables required to characterize patients are included
observational studies preparation ii
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 change)
  • A detailed database extraction and statistical analysis plan must then be prepared
  • The study population and subgroups of interest must be precisely defined.
  • To reduce potential bias, possible confoundersshould 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
Observational studies: Analyses and reporting
  • Demonstrate the robustness of results by:
    • Assessing whether the studied database population is representative of the target patients
    • Establishing the consistency of results through sensitivity analyses and across relevant patient subgroups and
    • 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 outcomes)
  • The process of reporting results reporting should begin with a flow chart detailing patient selection
observational studies analyses and reporting1
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
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:
    • Propensity scores – patients are assigned a score based on their baseline profile and matched to other patients with a similar score
    • 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 matching.
observational studies discussion of results
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.
    • If they differ could it be because of unaddressedbias in the observational study?
observational studies discussion of results1
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.
conclusions i
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 generalizabilityof different study designs to first be determined:
    • Characterisation of the study in terms of generalizability of its ecology of care and study population, then
    • Assessment of its quality (using design-specific tools)
conclusions ii
Conclusions (II)
  • Further work is required in this area to turn the well-intended 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 research
  • 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
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