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6 Searching forand selecting studies
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6.4.6 Identifying fraudulent studies, other retracted publications, errata
andcomments
It is important to check at the initial search stage– before data extraction– whether eligible studies have been corrected or retracted, and also at the review update stageforany
retractions or corrections since initial publication. Reports of retracted studies in MEDLINE
are assigned the Publication Type ‘Retracted Publication’. However, journal editors do not
always retract studies that warrant this sanction (Elia 2014) ordonot do so promptly, so
corrections, errata, comments and expressions of concern should also be examined.
Another source to identify retractions is Retraction Watch (www.retractionwatch.com)
and the related Retraction Watch Database (www.retractiondatabase.org). Some reference management software links with the Retraction Watch database and sends an automatic notification when the reference to a study matches a retraction in the database.
Information on how to search for retracted publications, errata and expressions of concern can be found in the online technical supplement of the Cochrane Handbook for
Systematic Reviews of Interventions, Section3.9 (Lefebvre 2021).
Identifying fraudulent studies is challenging (Boughton 2021). For guidance, consult
the Cochrane Policy for Managing Potentially Problematic Studies (www.cochranelibrary.
com/cdsr/editorial-policies#problematic-studies) and the accompanying
implementation
guidance (documentation.cochrane.org/display/EPPR/Policy+for+managing+potentia
lly+problematic+studies%3A+implementation+guidance).
6.4.7 Minimizing therisk ofbias through search methods
Systematic reviews are distinct from traditional narrative reviews. Systematic reviews
set out to identify as many relevant studies as possible and document searches in a
transparent way and with sufficient detail to be reproduced. These features help to
minimize bias and increase the reliability of review findings (Easterbrook 1991, Egger
1998, Song 2000, Dickersin2005).
Bias resulting from the search process could arise when studies with certain results
(often the more positive results) are easier to find than others and the search strategy
has not sufficiently accounted for this phenomenon. However, most of the evidence for
possible bias comes from work on systematic reviews of the effectiveness of interventions, and it is not yet clear whether publication and reporting biases exist in the same
way for test accuracy studies. There is evidence of test accuracy studies failing to achieve
full publication (Brazzelli 2009, van Enst 2015, Korevaar 2016) or being subject to selective reporting (Rifai 2008). Higher accuracy has been shown to be associated with faster
publication of diagnostic imaging studies (van Enst 2015, Cherpak 2019, Treanor 2021).
However, whether there is a general relationship between study results, study size and
time to publication is uncertain. Language bias and bias in the reporting of certain subgroups or results within test accuracy publications require further research.
The risk of introducing bias from the search can be minimized by searching several
electronic databases and using additional methods to retrieve published and unpublished studies (Whiting 2008). A search of MEDLINE alone is generally not considered
adequate for systematic reviews and may lead to bias (van Enst 2014). Even if relevant
records are in MEDLINE, it can be difficult to retrieve them efficiently; by extending the
search to other sources, some of these records may be retrieved elsewhere (Golder
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2006, Whiting 2008). For intervention reviews, searching Embase in addition to MEDLINE
has been shown to affect the estimate of effectiveness (Sampson 2003), probably partly
due to its broader coverage of languages other than English. Evidence about the impact
of restricting a search to MEDLINE alone on sensitivity and specificity estimates in syntheses of test accuracy is sparse (van Enst 2014). Some analyses have suggested that
searching MEDLINE alone (van Enst 2014, Rice 2016) or MEDLINE and Embase, together
with reference checking (Preston 2015), may be sufficient for systematic reviews of test
accuracy. These findings, however, may not be representative, as they are based on
small, selected or exploratory convenience samples; have had to rely on known sets of
test accuracy studies, where the search strategies of the original meta- analyses were
test accuracy search filters, which may have reduced their sensitivity.
Some studies may be recorded in specialist databases instead of in MEDLINE, for
example according to topic (such as the Cumulative Index to Nursing and Allied Health
Literature, CINAHL) or geographical area (such as the Latin American and Caribbean
Health Sciences Literature, LILACS) (Pereira 2019). There is some evidence to suggest
that other databases that might yield additional studies for systematic reviews of test
accuracy include the Science Citation Index, BIOSIS and LILACS (Whiting 2008), Scopus,
PsycINFO and Embase (Rice 2016). Relying exclusively on a MEDLINE search may retrieve
a set of reports unrepresentative of those that would have been identified through a
more extensive search of additional sources (Rice 2016).
Supplementary search methods, beyond database searches, include checking reference lists (Greenhalgh 2005, Horsley 2011), forward citation searches, cosis (Janssens 2015, Belter 2016, Belter 2017, Janssens 2020), handsearching and
contacting experts, other research groups and test manufacturers. Additional approaches
are needed to detect unpublished test accuracy studies. Although they are not generally
required to be recorded in trial registries such as ClinicalTrials.gov and are less often
registered than other study types (Korevaar 2014a), prospective registration of test accuracy studies in existing public registries has been encouraged and a minimal data set has
been suggested of the key information needed to describe test accuracy studies within
registries, including study type, index test(s) and target condition (Korevaar 2017).
Consistently populating these specific data fields for test accuracy studies would also
help make the records more discoverable. Searching trial registries for test accuracy
studies may therefore prove helpful (Glanville 2021), as may checking for test accuracy
study protocols (Zarei 2018). Conference abstracts may also point review authors to
ongoing or completed but (as yet) unpublished studies (Cherpak 2019, Korevaar 2020).
citation analy-
6.5 Documenting andreporting thesearch process
It is crucial to document and report the search process clearly, with enough detail to
indicate how extensive the search strategy is, which in turn can be a marker of how
methodologically sound the review’s evidence base is likely to be. Thorough documentation and transparent reporting can also aid in the reproducibility of the search strategy and facilitate future updates of the review, as well as provide a resource of
potentially useful search terms and their combinations for other researchers of similar
reviews to consider.
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Consensus has been reached on a minimum set of items to report when documenting the search process in systematic reviews (PRISMA- S), including specific guidance
for reviews of diagnostic test accuracy (PRISMA- DTA and PRISMA- DTA for Abstracts).
PRISMA for Searching (PRISMA- S) (Rethlefsen 2021) is an extension to the PRISMA
(Preferred Reporting Items for Systematic Reviews and Meta- Analyses) Statement, and
specifically addresses the reporting of search strategies in systematic reviews.
PRISMA- S can be used in conjunction with the PRISMA Statement for Diagnostic Test
Accuracy, PRISMA- DTA (McInnes 2018), and its accompanying explanation and elaboration article (Salameh 2020), as well as the corresponding checklist, explanation and
elaboration PRISMA article for journal and conference abstracts, PRISMA- DTA for
Abstracts (Cohen 2021).
Incomplete reporting of the search process in systematic reviews has been noted
(Sampson 2008, Roundtree 2009, Niederstadt 2010), including in reviews of test accuracy (Salameh 2019), and can undermine confidence in the research itself. A study
measuring the completeness of reporting of 100 systematic reviews of test accuracy
against the PRISMA-
DTA reporting checklist found there was a need for improvement.
The sources searched were reported by 87 of the 100 reviews analysed, the last date the
search was run by 33 of 100 and the complete search strategies by 42 of 100 (Salameh
2019). In the same study, measuring against the PRISMA- DTA for Abstracts checklist,
improvements were also found to be needed, for example in reporting the databases
searched (63 of 100) and the last date the search was run (42 of 100).
6.5.1 Documenting thesearch process
Documenting the search process involves keeping a careful record of each step taken by
the searcher and will help make the final reporting of the search in the review much
easier and less likely to be incomplete. This internal record- keeping of the search process includes documenting all sources searched, on which date, search terms used, the
full strategies as they were run, the yield for each source, details about contacting
experts or test manufacturers, searching reference lists, scanning websites and search
iterations. Careful documentation is especially important for systematic reviews of test
accuracy as methods are still being refined. Searches that are carefully documented
can be more easily reported, while good reporting will provide future insight into the
best sources of studies and the effects of search strategies on likely sources of bias.
6.5.2 Reporting thesearch process
6.5.2.1 Reporting thesearch process inthe protocol
Any protocol of a systematic review of test accuracy submitted for publication should
report the intended search strategy, ideally for each electronic database but at least for
one major bibliographic database. Protocols for Cochrane Reviews of diagnostic test
accuracy are formally peer reviewed before they are published and before the actual
review process starts. Peer reviewing the search strategy at this early stage by information specialists with expertise in searching for reviews of test accuracy is very important, as it is the starting point for retrieving the evidence included in the review. Authors
of Cochrane Reviews of diagnostic test accuracy are therefore advised to report the
complete search strategy in the protocol of at least one database, including the verbatim search strings so that these can be peer reviewed. Any later changes to the search
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strategy, and additional search strategies for additional databases, should be described
in the final review.
6.5.2.2 Reporting thesearch process inthe review
In the final review report, the searches for systematic reviews should be reported in the
review abstract, methods and results sections, with relevant detail provided as supplementary appendices. Again, this enables peer review of the search strategy, which is an
important quality check for the review as a whole.
The PRISMA for Diagnostic Test Accuracy Statement (PRISMA- DTA) should be followed
(McInnes 2018). PRISMA- DTA provides a 27- point checklist including recommendations
for reporting of four items relating to searching, covering (1) what should be reported in
the abstract by referring to PRISMA-
DTA for Abstracts (Cohen 2021); (2) all information
sources used (their coverage and dates last searched); (3) full search strategies for each
source searched (any limits); and (4) study selection, ideally presented as a flow diagram through the search process. The PRISMA- DTA for Abstracts recommends that
keydatabases and the dates when each was last searched be included in the search
methods section of an abstract (Cohen 2021). Review authors may also wish to refer
tothe PRISMA extension for searching that covers reporting of literature searches in
systematic reviews of all types (PRISMA- S) (Rethlefsen 2021).
Full search strategies (preferably copied and pasted from the original saved search
strategies rather than retyped) should be included as an appendix to the review. All
other resources used should also be reported, including grey literature resources, handsearching, online searches, citation tracking (forward and backward citation searching
and co- citation searches) and any contact with study authors, experts or commercial
organizations, such as test manufacturers. Complex searches, such as the use of multistranded approaches in searching for test accuracy studies, should be explained in a
narrative way so that the logic of their construction is clear and so that they can be
understood and replicated (Cooper 2018).
The dates on which the searches were done may be different from the dates until
which the searches were done. For example, review authors may restrict searches to
complete years (e.g. until 1January 2021) instead of to the actual date of the search
(e.g. conducted on 3March 2021). Both dates should be reported in the review. The
starting year for the search should also be reported, especially if the database was not
searched from its inception date onwards (e.g. the starting date is dictated by the availability of the index test). Reporting the relevant dates of the search strategy and the
search conduct enables readers and peer reviewers to assess how relevant the current
review may be. Although guidelines for search dates may differ between tests and
target conditions (a test that evolves rapidly may need a more up- to- date search than a
test that remains stable for decades), a rule of thumb is for searches to have been
conducted within the 12months before publication of the review.
The results section of the review should include a comprehensive summary of the
result of the search, including the total number of citations identified and the number
of records remaining after deduplication; the number of records included after title
andabstract screening; and the number of reports that were finally included in the
review after full text assessment. The PRISMA Statement recommends reporting
the flow of citations through the systematic review process in a flow diagram
(see Figure 6.5.a). PRISMA 2020 flow diagram templates for both new reviews and
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*Consider, if feasible to do so, reporting the number of records identified from each database or register searched (rather tha
**If automation tools were used, indicate how many records were excluded by a human and how many were excluded by automation tools.
Identification of studies via databases and registers
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Records identified from*:
Databases (n =)
Registers (n =)
Recordsremoved before
screening:
Duplicate records removed
(n = )
Records marked as ineligible
by automation tools (n = )
Records removed for other
reasons (n = )
Identification of studiesv ia other methods
Recordsidentified from:
Websites (n = )
Organisations (n = )
Citation searching (n = )
etc.
Records screened
(n = )
Reports sought for retrieval
(n = )
Reports assessed for eligibility
(n = )
Studies included in review
(n = )
Reports of included studies
(n = )
Included Screening Identification
Records excluded**
(n = )
Reports not retrieved
(n =)
Reportsexcluded:
Reason 1 (n = )
Reason 2 (n = )
Reason 3 (n = )
etc.
Reports sought for retrieval
(n = )
Reports assessed for eligibility
(n = )
n the total number across all databases/registers).
Reports not retrieved
(n = )
Reportsexcluded:
Reason 1 (n = )
Reason 2 (n = )
Reason 3 (n = )
etc.
Figure6.5.a The PRISMA 2020 flow diagram for new systematic reviews that included searches of databases and registers only
6.6 Selecting relevant studies
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review updates can be downloaded from the PRISMA Statement website (www.prismastatement.org) andedited to suit. Flow diagrams can also be generated using a Shiny
App (www.eshackathon.org/software/PRISMA2020.html).
6.6 Selecting relevant studies
Once initial searches of bibliographic databases have been completed, the next stage of
the selection process, manual judgement for relevancy, can begin. In parallel, searching
of additional resources (e.g. handsearching, web searching or finding grey literature)
can continue, as this may take longer. Ongoing studies identified by the searches may
be completed during the course of the review, so can be judged later on during the
review process. Checking reference lists of included studies can only start after these
studies have been identified.
Starting with the bibliographic database searches, all search results should be merged
using reference management software and duplicate records of the same study report
removed, so that one set of initially retrieved unique records can be defined. Reference
management software, such as EndNote (www.endnote.com), Mendeley (www.
mendeley.com), RefWorks (www.proquest.com/productsZotero (www.zotero.org), use different algorithms for automated detection of duplicate
records, and open- source software programs have been developed for this purpose
(Jiang 2014, Rathbone 2015). No consensus has been reached on the optimal process
for deduplication and a combination of automated methods and visual inspection is
common. For a list of selected reference management software, see Section4.1 of the
online technical supplement of the Cochrane Handbook for Systematic Reviews of
Interventions (Lefebvre 2021). For more detail on methods for removing duplicates
using reference management software or the use of open- source software programs,
see Section4.3 of the online technical supplement (Lefebvre 2021).
The initial screening phase is based on an examination of titles and abstracts. In this
phase, obviously irrelevant records should be removed, for example reports about com-
services/refworks.html) and
than an accuracy question. Reviews, overviews and editorials should also be removed
but, where relevant to the review question, may be flagged for checking the reference
lists. It is important to be aware that titles and abstracts are not always good reflections
of the study described in the full report. It is therefore not advisable at this stage in the
process to exclude records because eligible index test(s) are not mentioned in the title
or abstract. Some studies may have compared multiple index tests, but reported only
one in the abstract.
Screening of titles and abstracts can be done in reference management software, in
online software applications specifically designed for literature reviews or in spreadsheet
software such as Excel. The benefit of online software applications is that deduplicated
search results can be uploaded and screened online and reports that pass the first stage
(title and abstract screening) are automatically forwarded to the next stage (full- text
screening). Some applications allow lists of relevant or irrelevant keywords to be added
and their appearance highlighted on each record to aid screening decisions, while others
sort the order of screening based on the potential relevance of the record using machine
learning algorithms. More information about these techniques and about further
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automation of the selection process through machine learning and text mining can be
found in Section4.6.6 of the Cochrane Handbook for Systematic Reviews of Interventions.
Although title and abstract screening can be carried out by one review author alone–
especially when this author is over- inclusive – it is recommended that at least two
review authors examine every title and abstract independently from each other. This
way errors can be minimized and bias from subjective exclusions reduced.
Review authors who are used to screening for intervention reviews may be surprised,
first by the initial number of records to be screened for a review of test accuracy, and
subsequently by the number of reports remaining for full- text assessment. The frequent
complexity of searches for test accuracy studies (see Section6.4) can lead to retrieval of
large numbers of records. The variety of study designs that may be used to evaluate the
accuracy of tests (see Chapter3) makes it difficult to define preferred designs to help
narrow study selection. Authors of diagnostic accuracy reviews should expect a relatively high workload in the screening and selection process (Petersen 2014).
6.6.1 Examine full- text reports forcompliance ofstudies witheligibility criteria
After the first round of selection based on title and abstract, the next phase is to examine full- text reports to identify studies that meet the review eligibility criteria. In this
phase the decision will be made to either include or exclude records, therefore every
record should be assessed by two review authors independently from each other. Aprespecified list of reasons for exclusion should be used and at least one explicit reason for
exclusion should be documented for every excluded study. Disagreements may be
solved by discussion, or by a third person or ‘arbiter’.
The information that is needed to assess study eligibility is often not available in the
abstract and may sometimes be ‘hidden’, or not explicitly stated, even in the full text of
the study report. For example, studies do not always report what reference standard
was used to define the presence of the target condition or provide key details about
how it was applied in the study. In these situations, review authors have to make judgement calls about study inclusion, e.g. if the reference standard (or other key aspect of
the study) is not clear, will studies be included or not? Ideally, potentially difficult decisions like this should be addressed a priori in the review eligibility criteria; however, it is
not possible to anticipate every nuance in advance and independent screening of every
text report helps ensure consistency in decision- making.
full-
Although time consuming, correspondence with investigators of potentially relevant
studies, for example to ask whether the correct reference standard was used or to clarify other aspects of study eligibility, can be very fruitful and help ensure all relevant
studies are included (see Chapter7, Section7.2.2).
During this phase, review authors may also encounter multiple reports of the same
study, or multiple studies in one study report (see also Chapter7, Section7.2.1). As the
studies, and not the reports, are the unit of analysis in a systematic review, it is important to find out whether multiple reports from the same studies have been published
and to link together multiple reports of the same study. It is possible, for example, that
a study was designed as a comparative study, but that the data for each test were
reported in a separate publication.
Studies are often excluded because complete 2×2 tables could not be derived.
However, if the study meets all the other eligibility criteria for a review, then it may be
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6.8 Chapter information
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important to flag how many studies could have been summarized but did not provide
the required data.
6.7 Future developments inliterature searching andselection
One problem in searching for and selecting test accuracy studies is the lack of relevant
index terms to capture the study design in some bibliographic databases. Another
problem is that relevant information may be reported in various study designs (see
Chapter3). In the long term it is hoped that there will be a mechanism for re- tagging
test accuracy studies in MEDLINE with a specific suitable Publication Type (similar to
the way in which randomized controlled trials are now labelled) to facilitate searching.
Such a term, ‘diagnostic test accuracy study’, was introduced in Embase in 2011
(Cochrane Community2011) as a check tag, a designated term for indexers to use to
denote study types. However, ‘diagnostic test accuracy study’ retrieves only about half
of the test accuracy studies in Embase (Gurung 2020) published since 2011, and obviously none before that date.
More promising developments include crowdsourcing and solutions involving the use
of artificial intelligence, such as machine learning. Searching for thousands of test accuracy studies using a very broad and sensitive search is one thing, but these records need
to be screened for relevance as well. Cochrane is exploring innovative methods including crowdsourcing techniques (where many volunteers contribute their time to screen
records) to assist in the screening of otherwise unmanageable numbers of records and
to minimize the risk of missing relevant studies. So far, crowd screening has been mainly
used to identify randomized controlled trials (Noelscreening approach could be developed to identify studies for systematic reviews of
diagnostic test accuracy and generate a sufficiently large data set from which to develop
a test accuracy study classifier, similar to the Cochrane RCT Classifier (Thomas 2021)
and the recently validated Cochrane COVID- 19 Study Classifier, using machine learning
techniques (Shemilt 2022). New techniques, including text mining or deep learning
methods, have applications for search strategy design, for screening the results for systematic reviews and for data extraction and quality assessment. Research has shown
that these approaches may improve the efficiency and accuracy of these tasks (Marshall
2019, Norman 2019). These approaches may be especially helpful for review updates,
although the final performance of the technique chosen strongly depends on careful
data preparation (data pre- processing) (Lange 2021). These are fast- moving areas of
research and development, and review authors are advised to keep an eye on such
techniques and approaches, to be able to take advantage of developments as they
become available.
Storr 2021), but it is hoped that this
6.8 Chapter information
Authors: René Spijker (Cochrane Netherlands, Utrecht University; Medical Library,
University of Amsterdam, The Netherlands), Jacqueline Dinnes (Institute of Applied
Health Research, University of Birmingham, UK), Julie Glanville (glanville.info, York, UK),
Anne Eisinga (Cochrane UK, Oxford, UK).
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Sources of support: Jacqueline Dinnes is supported by the NIHR Birmingham
Biomedical Research Centre at the University Hospitals Birmingham NHS Foundation
Trust and the University of Birmingham. The views expressed are those of the authors
and not necessarily those of the NHS, the NIHR or the Department of Health and Social
Care. No other authors declare sources of support for writing this chapter.
Declarations of interest: René Spijker is an Information Specialist with Cochrane
Netherlands. Jacqueline Dinnes is a member of Cochrane’s Diagnostic Test Accuracy
Editorial Team. Julie Glanville was a co- convenor of the Cochrane Information Retrieval
Methods Group at the time of writing the chapter, and is co- producer of several websites and resources mentioned in the chapter: ISSG Search Filters Resource, SuRe Info
and a Clinical Trials Registers resource. Anne Eisinga is an Information Specialist with
Cochrane UK. The authors declare no other potential conflicts of interest relevant to the
topic of their chapter.
Acknowledgements: This chapter re-
uses and builds on material included in the following chapters of the Cochrane Handbook for Systematic Reviews of Interventions to ensure
consistency in guidance for authors of Cochrane Reviews: (1) Lefebvre C, Glanville J,
Briscoe S, Littlewood A, Marshall C, Metzendorf M- I, Noel- Storr A, Rader T, Shokraneh F,
Thomas J, Wieland LS. Chapter4: Searching for and selecting studies. In: Higgins JPT,
Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook
for Systematic Reviews of Interventions version 6.2 (updated February 2021). Cochrane,
2021. (2) Lefebvre C, Glanville J, Briscoe S, Littlewood A, Marshall C, Metzendorf M- I,
Noel- Storr A, Rader T, Shokraneh F, Thomas J, Wieland LS. Technical supplement to
Chapter4: Searching for and selecting studies. In: Higgins JPT, Thomas J, Chandler J,
Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic Reviews
of Interventions version 6.2 (updated February 2021). Cochrane, 2021. We are grateful to
the authors for kindly sharing pre- publication drafts with us.
The authors thank Lotty Hooft, Madhukar Pai, Yngve Falck- Ytter, Lucas Bachmann,
Fritz Grossenbacher, Mark Bruyneel, Ruth Mitchell, Henrica CW de Vet, Ingrid I Riphagen,
Bert Aertgeerts, Daniel Pewsner and the many information specialists for contributions
to a previous version of this chapter.
The authors would like to thank April Coombe, Anna Noel-
Storr and the Cochrane
Information Specialist Executive for helpful peer review comments.
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