Understanding and Using the STARD and PRISMA Guidelines · Introduction – Herbert Y. Kressel,...
Transcript of Understanding and Using the STARD and PRISMA Guidelines · Introduction – Herbert Y. Kressel,...
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Introduction – Herbert Y. Kressel, Professor of Radiology, Harvard Medical School, Editor-in-Chief, Radiology STARD – Patrick M. Bossuyt, Professor of Clinical Epidemiology, University of Amsterdam, and one of the original authors of STARD PRISMA - Matthew McInnes, Associate Professor of Radiology, University of Ottawa, 2014 RSNA Eyler fellow Putting it all together – Deborah Levine, Professor of Radiology, Harvard Medical School, Senior Deputy Editor, Radiology
Understanding and Using the STARD and PRISMA Guidelines
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Understanding and Using the STARD and PRISMA Guidelines: Introduction
Herbert Y. Kressel, MD Editor, Radiology
Miriam H. Stoneman Professor of Radiology Harvard Medical School, Boston MA
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Editors, Reviewers, and Readers: Key Questions
• Is it true? Scientific Validity • Can the results be generalized? Reproducibility • Is it Novel? • Is it Important • Is it Interesting
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ANNALS OF SCIENCE
THE TRUTH WEARS OFF Is there something wrong with the scientific method?
BY JONAH LEHRER DECEMBER 13, 2010
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Unreliable research
Trouble at the lab Scientists like to think of science as self-correcting. To an alarming degree, it is not
“I SEE a train wreck looming,” warned Daniel Kahneman, an eminent psychologist, in an open letter last year. The premonition concerned research on a phenomenon known as “priming”. Priming studies suggest that decisions can be influenced by apparently irrelevant actions or events that took place just before the cusp of choice. …. Dr Kahneman and a growing number of his colleagues fear that a lot of this priming research is poorly founded. Over the past few years various researchers have made systematic attempts to replicate some of the more widely cited priming experiments. Many of these replications have failed. In April, for instance, a paper in PLoS ONE, a journal, reported that nine separate experiments had not managed to reproduce the results of a famous study from 1998 purporting to show that thinking about a professor before taking an intelligence test leads to a higher score than imagining a football hooligan.
Oct 19th 2013 | http://www.economist.com/node/21588057
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Unable to reproduce reported results
• Amgen working with original authors to reproduce results could only replicate 6/53 key studies assessed
• Bayer Healthcare able to reproduce results in ¼ of 63 studies
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Why Most Published Research Findings Are False
… “Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true.”
… “Commercially available “data mining” packages
actually are proud of their ability to yield statistically significant results through data dredging.”
PLoS Med 2(8): e124. doi:10.1371/journal.pmed.0020124
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Probability that study is true depends on pretest probability, statistical power, and bias
PLoS Med 2(8): e124. doi:10.1371/journal.pmed.0020124
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NIH plans to enhance reproducibility
Francis S. Collins and Lawrence A. Tabak discuss initiatives that the U.S. NIH is exploring to restore the self-correcting nature of preclinical research
“A growing chorus of concern, from scientists and laypeople, contends that the complex system for ensuring the reproducibility of biomedical research is failing and is in need of restructuring. As leaders of the NIH, we share this concern…. Science has long been regarded as ‘self-correcting’, given that it is founded on the replication of earlier work. Over the long term, that principle remains true. In the shorter term, however, the checks and balances that once ensured scientific fidelity have been hobbled. This has compromised the ability of today’s researchers to reproduce others’ findings.”
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A history of the evolution of guidelines for reporting medical research: the long road to the EQUATOR Network.
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Concerns about the quality of research reporting are not new
“It is a commonplace of medical literature to find the most positive and sweeping conclusions drawn from a sample so meager as to make scientifically sound conclusions of any sort utterly impossible.” -Pearl, 1919
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“Frequently, indeed, the way in which the observations were planned must have made it impossible for the observer to form a valid estimate of the error … an idea of what results might be expected if the experiment were repeated under the same conditions.” -Mainland 1938
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Schor and Karten (1966)
• 34% - Conclusions drawn about population but no statistical tests applied to determine whether such conclusions were justified
• 31% - No use of statistical tests when needed
• 25% - Study design not appropriate for solving the stated problem
• 19% - Too much confidence placed on negative results with small-size samples
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Problems in Research Reporting Continue
Among 513 neuroscience articles published in 5 top-ranking journals (Science, Nature, Nature Neuroscience, Neuron, Journal of Neuroscience) in 2009-10, 50% of 157 articles which compared effect sizes used an inappropriate method of analysis
Nieuwenhuis, Nature Neuroscience, 2011
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In 100 orthopedic research papers published in seven journals in 2005-10, the conclusions were not clearly justified by the results in 17% and a different analysis should have been undertaken in 39%
Parsons et al Biomedcentral, 2012
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Reporting Standards are Desirable • “Standards governing the content and format of statistical aspects
should be developed to guide authors in the preparation of manuscripts.” -O’Fallon et al 1978, Biometrics 34:687-95
• “… editors could greatly improve the reporting of clinical trials by providing authors with a list of items that they expected to be strictly reported.” -DerSimonian et al 1982, NEJM 306:1332-7
• “An obvious proposal is to suggest that editors of oncology journals make up a check-list for authors….” -Zelen 1989, J Clin Oncol 7:827-8
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Efforts to Create Standards for Reporting • STARD Standards for Reporting Diagnostic Accuracy (Diagnostic Performance ) • CONSORT Consolidated Standards of Reporting Trials
(Randomized Control Trials) • PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analyses
• STROBE Strengthening the reporting of observational studies in epidemiology (cohort, case-control, and cross sectional studies)
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Guidelines Improve Reporting • Help identify the presence and nature of bias • Help identify methodological problems e.g sample
size, inappropriate analysis • Help ensure that the description of the methods is
adequate to reproduce study • They do not ensure that a study is novel or important
or interesting.
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STARD 2015
Complete And Transparent Reporting Of
Diagnostic Accuracy Studies
Patrick M. Bossuyt
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Diagnostic Accuracy
• How good is the test in correctly classifying patients as being diseased?
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Diagnostic Accuracy
• How good is the test in correctly classifying patients as having the target condition?
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Diagnostic Accuracy Study
Index Test
Gold Standard
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Diagnostic Accuracy Study
Index Test
Reference Standard
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Diagnostic Accuracy Study
Index Test
Reference Standard
Series of Patients
Cross-classification
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Ebola No Ebola
Positive
Negative
RT-PCR
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Ebola No Ebola
Positive 15 9
Negative 0 107
The results RT-PCR
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• Sensitivity & Specificity 100% 92%
• 95% CI (78% to 100%) (86% to 96%)
Ebola No Ebola
Positive 15 9
Negative 0 107
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Sources of variation Setting Patient characteristics Previous testing
Sources of bias:
– Incomplete verification – Unblinded reading – Multiple reference
standards
Accuracy is not fixed
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Pulmonary Embolism
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D-dimer testing
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Diagnostic Accuracy Study
D-dimer
Multislice CT
ED Patients
Cross-classification
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Patient Selection
D-dimer
CT
“Easy” Patients
Cross-classification
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Two Series: Two sets of Eligibility Criteria
D-dimer
PE patients
Cross-classification
Healthy controls
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Verification - partial
D-dimer
Multislice CT
ED Patients
Cross-classification
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Target Condition
Other Condition
Positive
Negative
The results Reference Standard
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Partial verification: random Reference Standard
Target Condition
Other Condition
Positive
Negative
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Partial verification: typical Reference Standard
Target Condition
Other Condition
Positive
Negative
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Multiple Reference Standards
D-dimer
Multislice CT
ED Patients
Cross-classification
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Multiple Reference Standards
D-dimer
Multislice CT
ED Patients
Cross-classification
Follow-up
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Multiple Reference Standards Reference Standard
Target Condition
Other Condition
Positive
Negative
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Multiple Reference Standards Reference Standard
Target Condition
Other Condition
Positive
Negative
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Multiple Reference Standards
D-dimer
Multislice CT
ED Patients
Cross-classification
Follow-up
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Multislice CT Follow-up
Multiple Reference Standards
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Multiple Reference Standards Reference Standard
Target Condition
Other Condition
Positive
Negative
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Multiple Reference Standards Reference Standard
Target Condition
Other Condition
Positive
Negative
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Multiple Reference Standards Reference Standard
Target Condition
Other Condition
Positive
Negative
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D-dimer testing
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JE Schrecengost; Clin Chem 2003
outpatients inpatients
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JAMA 15 SEP 1999; 282; 1061-1066
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Korevaar et al. Radiology. 2015 Mar;274(3):781-9
Item Reported
Inclusion and exclusion criteria 65% Participant sampling: consecutive vs. random vs. convenience 55% Blinding of index test readers 58% Baseline characteristics (age, sex, presenting symptoms) 61%
112 Diagnostic accuracy studies published in 2012
How well are studies reported?
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STARD (2003)
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Adherence to STARD
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STARD 2015: An update
• Incorporate new evidence – Improved understanding of sources of bias and variation
• Facilitate use
– Rearranging and rephrasing items – Improving consistency with other major reporting guidelines
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Project team (n=4)
Advisory board (n=13)
Contributors (n=68)
N=85
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Penny Whiting Marie Westwood Nandini Dendukuri David Simel Augusto Azuara-Blanco Rita Horvath Ann van den Bruel Anne Rutjes Lucas Bachmann Jeffrey Blume Frank Buntinx Blanca Lumbreras Chris Hyde Carl Heneghan Ewout Steyerberg Eleanor Ochodo Gianni Virgili Holly Janes Joris de Groot Jac Dinnes Carl Moons
Hans van Maanen William Summerskill Herbert Kressel Nader Rifai Robert Golub Philippe Ravaud Isabelle Boutron Richelle Cooper John Ioannidis Iveta Simera Andreas Ziegler Doug Altman Jon Deeks Kenneth Fleming Gordon Guyatt Myriam Hunink Jos Kleijnen Andre Knottnerus Erik Magid Barbara McNeil Matthew McQueen
Andrew Onderdonk Christopher Price Sharon Straus Stephen Walter Wim Weber Constantine Gatsonis Les Irwig David Moher Riekie de Vet David Bruns Paul Glasziou Jeroen Lijmer Drummond Rennie Hans Reitsma Jorgen Hilden Harry Büller Frank Davidoff John Overbeke Daniël Korevaar Lotty Hooft Jérémie Cohen Patrick Bossuyt
Mariska Leeflang Matthew Thompson Margaret Pepe Nynke Smidt Nancy Obuchowski Petra Macaskill Katie Morris Reem Mustafa Rosanna Peeling Steffen Petersen Sally Lord Holger Schunemann Susan Mallett Todd Alonzo Andrew Vickers Nancy L. Wilczynski Yemisi Takwoingi Nitika Pai Sarah Byron Stephanie Chang Stefan Lange
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Project plan • Stage 1: Literature search
– Aim: Identify potential new items
• Stage 2: Two-round survey
– Aim: Identify items that needed to be modified, removed, or added
• Stage 3: Two-day live meeting in Amsterdam
• Stage 4: Final input wider STARD group
• Stage 5: Piloting
• Stage 6: Finalization of checklist
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Survey question: An example
• Existing item: – Report any adverse events from performing the index tests or the reference standard.
• Consideration:
– Diagnostic accuracy studies typically lack the power and design to estimate adverse event rates.
– Many tests do not have intrinsic adverse events.
• Question: Should we: – Keep this item as it is – Modify this item (please explain) – Remove this item (our suggestion) – No opinion
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STARD 2003 items (n=25) Consensus reached? Keep item as it is 5
Modify item 13
Remove item 0
No consensus 7
Potential new items (n=8) Consensus reached? Include 4
Exclude 0
No consensus 4
Response rate: 86% (73/85)
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Project plan
• Stage 1: Literature search
• Stage 2: Two-round survey
• Stage 3: Two-day live meeting in Amsterdam
– Aim: Discuss items for which no consensus was reached
– Aim: Reach consensus on draft checklist
• Stage 4: Final input wider STARD group
• Stage 5: Piloting
• Stage 6: Finalization of checklist
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Project plan • Stage 1: Literature search
• Stage 2: Two-round survey
• Stage 3: Two-day live meeting in Amsterdam
– Aim: Discuss items for which no consensus was reached
– Aim: Reach consensus on draft checklist
• Stage 4: Final input wider STARD group
• Stage 5: Piloting
• Stage 6: Finalization of checklist
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A checklist is not the end product
• A list of reporting items is only the beginning
• We have to develop real tools – Teaching material – Writing aids – Reviewing tools – …
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STARD for Abstracts
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STARD for Trial Registration
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STARD 2015 Complete And Transparant Reporting
Of Diagnostic Accuracy Studies
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PRISMA: Guide for Authors
Matthew McInnes MD FRCPC
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What is a systematic review? • A systematic review attempts to collate all empirical
evidence that fits pre-specified eligibility criteria to answer a specific research question.
• It uses explicit, systematic methods that are selected to minimize bias, thus providing reliable findings from which conclusions can be drawn and decisions made.
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Why are they important? • Because of their rigorous, scientific approach,
systematic reviews and meta-analyses have been touted as the “highest level of evidence”
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What can they tell us? • More precise estimates of imaging test accuracy,
intervention effectiveness, or complication rate • Identify factors contributing to heterogeneity in
these measures • Compare diagnostic accuracy between two
modalities**
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Pickhardt, Radiology 2011
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McInnes, Radiology 2011
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Kiewet, Radiology 2012
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*2015 data is only up to date through May, yellow box is ‘projected for a full year’
Systematic Reviews Published in Imaging Journals (Diagnostic Test Accuracy Reviews only)
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*2015 data is through October 2015
Systematic Reviews Published in Radiology
*
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Systematic Review Benefits • No expensive infrastructure needed • No review board or other typical barriers to progress • Anyone (even you) can do it!
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• Failure to Choose a Review Question That Represents an Advance in Knowledge – no or only a very small number of studies – several well-conducted, large sample size studies have
evaluated the same research question and arrived at similar conclusions
– systematic review with a similar purpose has been recently published
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PICO • Patient
• Index Test
• Comparison (if relevant, may be N/A)
• Outcome (typically diagnostic accuracy as defined by reference standard)
Staunton, M. Radiology 2007
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PICO P = Patients with a focal liver lesion possibly adenoma vs. FNH, but no history of cirrhosis were included I (index test) = gadoxetic acid–enhanced MR imaging C = No comparison O (Outcome) = an acceptable reference standard for diagnosis of FNH or HCA defined as surgical pathology; biopsy; and clinical follow-up, imaging follow-up, or both.
McInnes et al. Radiology 2015
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PRISMA • Preferred Reporting Items for Systematic reviews and
Meta-Analysis • 27 item checklist to guide reporting of systematic
reviews
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PRISMA • Key information is often poorly reported in systematic
reviews, thus diminishing their potential usefulness • As is true for all research, systematic reviews should be
reported fully and transparently to allow readers to assess the strengths and weaknesses of the investigation
• Aim is to ensure clear presentation of what was planned, done, and found in a systematic review
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How to use PRISMA? • Guide protocol design • Guide manuscript writing
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Benefits of PRISMA? • PRISMA adherence is associated with systematic
review quality
Tunis et al. Radiology 2011
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Benefits of PRISMA? • PRISMA adherence is associated with citation rate
van der Pol et al. PLOSOne 2015
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Benefits of PRISMA? • Journals endorse PRISMA
– Radiology – jMRI
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Benefits of PRISMA?
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PRISMA (lnterventions)
Diagnostic Test Accuracy
Reviews
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DTA Review Challenges
Publication Bias
Heterogeneity
Meta-Analysis Method
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Risk of bias in included studies • Diagnostic accuracy reviews should use QUADAS-2 • NOT STARD, Cochrane risk of bias tool etc.
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http://www.bristol.ac.uk/quadas/quadas-2/
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QUADAS-2
Lee et al. Radiology 2015
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dta.cochrane.org
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Practical tips • Use PRISMA and other resources when planning your
systematic review • Create internal peer review team for protocol design
and manuscript writing
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Practical tips • Reach out to experts • Educate yourself
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Putting it all together -- Deborah Levine, MD
Senior Deputy Editor, Radiology
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• Be sure you have a novel/interesting/important research question
• Follow our Publication Information for Authors – Use a checklist! – Flow diagrams are helpful for summarizing not just
patient inclusion/exclusion but also for showing results of index test
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• You can follow all the rules and have the “perfect” methodology but if the question you are asking is not new, clinically relevant, or hypothesis generating…
• …then lack of novelty will make acceptance in our journal unlikely
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Novel: does it… • Add new information
• Provide new concepts
• Describe new technology
• Define new diagnostic or therapeutic approaches
• Resolve existing controversies
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Important: Does it…. • Change practice:
“News you can use”
• Help us understand biology or technology?
• Generate a new hypothesis and stimulate further research?
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Interesting/Informative: Does it…
• Add to considerably to our available information
• Have conclusions that provide clear direction
• Provide useful information
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Use our Publication Information for Authors
Our Goal: Help
you build and optimize the structure and content of your manuscript
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http://pubs.rsna.org/page/radiology/pia
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http://pubs.rsna.org/page/radiology/pia
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Define your purpose • Clearly stated purpose is the foundation of any
research study and research manuscript • Use that purpose in the abstract and at the end of
introduction • When you write your conclusions, be sure they directly
address the purpose
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Hypothesis-driven research • STARD 2015 asks for
Study objectives AND hypotheses – What are you trying to prove (disprove) – A clearly stated hypothesis helps focus the
entire research manuscript
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Methods – Details Radiology checklist • Ethical: IRB, HIPAA, informed consent • Remember to register your clinical trial! (ClinicalTrials.gov)
– prospective trials should be here, but consider this for retrospective trials as well!
• Funding source(s) • Conflicts of interest • Overlap in subjects from prior publications
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Methods: Study design Prospective (looking forward) vs. Retrospective (looking back) Prospective studies - Data collection (and usually analysis) are planned BEFORE the obtaining the index test and reference standard Written informed consent Frequently we see studies with retrospective analysis of prospectively acquired data
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Methods - participants • Describe the study population
– Inclusion/exclusion criteria and numbers – Dates of study enrollment/follow-up
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Inclusion bias • “Inclusion criteria were 100 consecutive patients with
right lower quadrant pain who had ultrasound and CT within 24 hours of surgery….” – How many patients had RLQ pain during the time
interval? – How many had US but no CT? – How many had CT but no US? – How many did not go to surgery? – How many had surgery >24 hours later?
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1000 patients with abdominal pain
CT (N=500)
US N=300
No imaging (N=200)
US group: Biased to Children women of reproductive age
CT: Older Biased to males
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1000
500 CT
200 US and CT
100 US, CT, and surgery in <24 hours
The 100 patients in the final population are biased towards the sickest patients who went to surgery AND also the types of patients/findings that would lead to a patient having both a CT and an US
STARD 2015 asks for distribution of alternative diagnosis in those without the target condition
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Participant accrual
• Was this based on: – Presenting symptoms – Results from previous tests
• Convenience sample – Had patients received the index test and/or reference
standard and you are basically mining the data
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Sample size and POWER STARD 2015 asks for “intended sample size and how it was determined” Power analysis
Best is a priori If you don’t have that, we may ask for a post hoc analysis, particularly for non-significant results If you have significant results, the issue will be if your population and methodology are generalizable
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Methods: Clarity is key
• Structure your methods such that someone else can EXACTLY reproduce your work – How do you perform your index test – How do you perform your reference standard? – Be specific. Use tables, as needed
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“In sufficient detail to allow replication”
Dolly First clone mammal created from an adult cell Born July 5, 1996 http://www.nms.ac.uk/explore/collections-
stories/natural-sciences/dolly-the-sheep/
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Blinded interpretations • Index test and reference standard
– Describe if reviewers were blinded – If reviewers were involved in patient care, is there
possible recall bias?
– Batched readings – what is read first? How might that bias results?
Time Time
Time
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Reference standard
• What you use as “truth” – Operative findings – Histology – Imaging studies
• time interval for clinical follow-up • Realize the biases inherent in your reference standard
– Why did you choose your reference standard?
Time Time
Time
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Reference test methods • Be very clear about the reference test
– Positive, Negative, Indeterminate
– How did you determine these thresholds? • If based on your own population, you can have biased
results – Was your study exploratory only? –need to be clear about
the strength of the conclusions that can/cannot be drawn
• If you don’t have an a priori threshold, did you have 2 patients groups, a development set and an independent test set?
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• Be certain to report on all items listed in MATERIALS & METHODS
• Do not report results for items not listed in MATERIALS & METHODS
• Give specific information, not generalities • Tip: Have someone not involved in your project read your
manuscript. Does it make sense? Do methods and results parallel each other?
Results
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Estimates • Report accuracy with measures of
uncertainty/confidence intervals • Report indeterminate and missing results • Report variability between subgroups
– Preferentially established before data results • Report reproducibility, inter- and intra-observer TIP: involve a statistician early in the planning process
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Text Table Graph
Provides detail Can be cumbersome
Explicit information Might make manuscript long
Illustrative summary Might lack granularity
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Discussion - Limitations
– sources of potential bias – statistical uncertainty – generalizability
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Discussion – Conclusion • Be sure that your summary interpretation is consistent
with • Hypothesis • Purpose • Methods • Results
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Putting it all together • Start with a great idea • Plan your study using guidelines and checklists • Involve a statistician early to help with planning • Write your manuscript using the appropriate
guidelines, publication information for authors, and checklists
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We look forward to seeing your next manuscript in submitted to Radiology