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7.4 Data collection tools
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creating an electronic form and any guidance or codebook to be used by data extractors.
Review authors also should consider the compatibility of any electronic form or data
system with analytical software, as well as mechanisms for recording, assessing and
correcting data entry errors.
Data described in multiple reports (or even within a single report) of a study may not
be consistent. Review authors will need to describe how they will work with multiple
reports in the protocol, for example by pre- specifying which report will be used when
sources contain conflicting data that cannot be resolved by contacting the investigators. Likewise, when there is only one report identified for a study, review authors
should specify the section within the report (e.g. abstract, methods, results, tables and
figures) for use in case of inconsistent information.
A good data collection form should minimize the need to go back to the source
documents. When designing a data collection form, review authors should involve all
members of the team; that is, content area experts, authors with experience in systematic review methods and data collection form design, statisticians and persons
who will perform data extraction. Here are suggested steps and some tips for designing
a data collection form, based on the informal collation of experiences from numerous
review authors (Li 2015).
Step1. Develop outlines of tables and figures expected to appear in the systematic
review, considering the comparisons to be made between index tests within the review,
and whether different definitions of the target condition or multiple reference standards are to be included. This step will help review authors decide the right amount of
data to collect (not too much or too little). Collecting too much information can lead to
forms that are longer than original study reports, and can be very wasteful of time.
Collection of too little information, or omission of key data, can lead to the need to
return to study reports later in the review process.
Step 2. Assemble and group data elements to facilitate form development. Review
authors should consult Table7.3.a, in which the data elements are grouped to facilitate
form development and data collection. It may be more efficient to group data elements
in the order in which they are usually found in study reports (e.g. starting with refer-
information, followed by eligibility criteria, description of index test, description
ence
of reference standard, baseline characteristics and results).
Step3. Identify the optimal way of framing the data items. Much has been written about
how to frame data items for developing robust data collection forms in primary research
studies. We summarize a few key points and highlight issues that are pertinent to
systematic reviews.
●
Ask closed- ended questions (i.e. questions that define a list of permissible responses)
as much as possible. Closed- ended questions do not require post hoc coding and pro-
vide better control over data quality than open- ended questions. When setting up a
closed- ended question, one should anticipate and structure possible responses and
include an ‘other, specify’ category, because the anticipated list may not be exhaus-
tive. Avoid asking data extractors to summarize data into uncoded text, no matter
how short it is.
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●
Avoid asking a question in such a way that the response may be left blank. Include
‘not applicable’, ‘not reported’ and ‘cannot tell’ options as needed. The ‘cannot tell’
option tags uncertain items that may promote review authors to contact study
authors for clarification, especially on data items critical to reach conclusions.
●
Remember that the form will focus on what is reported in the report rather than what
has been done in the study. The study report may not fully reflect how the study was
actually conducted. For example, a question ‘Did the study report whether the reference standard results were interpreted without knowledge of the results of the index
test?’ is more appropriate than ‘Were assessors blinded?’
●
Where a judgement is required, record the raw data (i.e. quote directly from the
source document) used to make the judgement. This can be particularly important
for any data that will support review author judgements of study quality. It is also
important to record the source of information collected, including where it was
found in a report or whether information was obtained from unpublished sources or
personal communications. As much as possible, questions should be asked in a way
that minimizes subjective interpretation and judgement to facilitate data
comparison
and adjudication.
●
Incorporate flexibility to allow for variation in how data are reported. It is strongly
recommended that outcome data be collected in the format in which they were
reported and transformed in a subsequent step if required. Review authors also
should consider the software they will use for analysis and for publishing the
review.
Step4. Develop and pilot- test data collection forms, ensuring that they provide data in
the right format and structure for subsequent analysis. In addition to data items
described in Step2, data collection forms should record the title of the review as well as
the person who is completing the form and the date of completion. Forms occasionally
need revision; forms should therefore include the version number and version date to
reduce the chances of using an outdated form by mistake. Because a study may be
associated with multiple reports, it is important to record the study ID as well as the
report ID. Definitions and instructions helpful for answering a question should appear
next to the question to improve quality and consistency across data extractors
(Stock1994). Provide space for notes, regardless of whether paper or electronic forms
are used.
All data collection forms and data systems should be thoroughly pilot- tested before
launch. Testing should involve several people extracting data from at least a few reports.
The initial testing focuses on the clarity and completeness of questions. Users of the
form may provide feedback that certain coding instructions are confusing or incomplete (e.g. a list of options may not cover all situations). The testing may identify data
that are missing from the form, or likely to be superfluous. After initial testing, accuracy
of the extracted data should be checked against the source document or verified data
to identify problematic areas. It is wise to draft entries for the table of ‘Characteristics of
included studies’ and complete a risk- of- bias assessment (Chapter8) using these pilot
reports to ensure that all necessary information is collected. A consensus between
review authors may be required before the form is modified to avoid any misunderstandings or later disagreements. It may be necessary to repeat the pilot testing on a
new set of reports if major changes are needed after the first pilot test.
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Problems with the data collection form may surface after pilot testing has been
completed, and the form may need to be revised after data extraction has started.
When changes are made to the form or coding instructions, it may be necessary to
return to reports that have already undergone data extraction. In some situations, it
may be necessary to clarify only coding instructions without modifying the actual data
collection form.
7.5 Extracting data fromreports
7.5.1 Introduction
In most systematic reviews, the primary source of information about each study is published in reports of studies, usually in the form of journal articles. Despite recent developments in machine learning models to automate data extraction in systematic reviews
(see Chapter6), data extraction is still largely a manual process. Electronic searches for
text can provide a useful aid to locating information within a report. Examples include
using search facilities in PDF viewers, internet browsers and word processing software.
However, text searching should not be considered a replacement for reading the report,
since information may be presented using variable terminology and presented in multiple formats.
7.5.2 Who should extract data?
Data extractors should have at least a basic understanding of the topic and have knowledge of study design, data analysis and statistics. They should pay attention to detail
while following instructions on the forms. Because errors that occur at the data extraction stage are rarely detected by peer reviewers, editors or users of systematic reviews,
it is recommended that more than one person extract data from every report to minimize errors and reduce introduction of potential biases by review authors. As a minimum, information that involves subjective interpretation (e.g. related to quality
judgements) and information that is critical to the interpretation of results (e.g. accuracy data) should be extracted independently by at least two people. In common with
implementation of the selection process (see Chapter6), it is preferable that data
extractors are from complementary disciplines, for example a methodologist and a
topic area specialist. It is important that everyone involved in data extraction has practice using the form and, if the form was designed by someone else, receives appropriate
training.
Evidence in support of duplicate data extraction for at least some data extraction
items comes from a number of indirect sources. One study observed that independent
data extraction by two review authors resulted in fewer errors than data extraction by a
single review author followed by verification by a second (Buscemi 2006). A second
study showed similar odds of errors for independent data extraction compared to
extraction by a less experienced review author with verification by a more experienced
data abstractor for items related to study characteristics, but suggested higher odds of
errors for items related to outcomes and results (Li 2019). A further study suggested that
more experienced extractors made fewer errors in regard to outcomes and results, but
more errors in baseline characteristics (Jian- Yu 2020).
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7.5.3 Training data extractors
Training of data extractors is intended to familiarize them with the review topic and
methods, the data collection form or data system, and issues that may arise during data
extraction. Results of the pilot testing of the form should prompt discussion among
review authors and extractors of ambiguous questions or responses to establish consistency. Training should take place at the onset of the data extraction process and periodically over the course of the project (Li 2015). For example, when data related to a
single item on the form are present in multiple locations within a report (e.g. abstract,
main body of text, tables and figures) or in several publications, the development and
documentation of instructions to follow an agreed algorithm are critical and should be
reinforced during the training sessions.
Some researchers have proposed that some information in a report, such as its
authors, be blinded to the review author prior to data extraction and assessment of risk
of bias (Jadad 1996). However, blinding of review authors to aspects of study reports
generally is not recommended for Cochrane Reviews, as there is little evidence that it
alters the decisions made (Berlin1997).
7.5.4 Extracting data frommultiple reports ofthe same study
Studies can be reported in more than one publication or in more than one source
(Tramèr 1997, von Elm 2004). In systematic reviews of test accuracy, a single source
often provides complete information about a study, but some studies do have multiple
associated reports, which may contain conflicting information (Mayo- Wilson 2017a,
Mayo- Wilson 2017b, Mayo- Wilson 2018). Alternatively, multiple publications may arise
from the same participant cohort, with detailed testing methods or participant eligibility criteria provided only in the earliest publications rather than described in the eligible publication of interest.
Because the unit of interest in a systematic review is the study and not the report,
information from multiple reports often needs to be collated and reconciled. It is not
appropriate to discard any report of an included study without careful examination,
since it may contain valuable information not included in the primary report. Review
authors will need to decide between two strategies:
●
extract data from each report separately, then combine information across multiple
data collection forms; or
●
extract data from all reports directly into a single data collection form.
The choice of which strategy to use will depend on the nature of the reports and may
vary across studies and across reports. For example, when a full journal article and multiple conference abstracts are available, it is likely that the majority of information will
be obtained from the journal article; completing a new data collection form for each
conference abstract may be a waste of time. Conversely, when there are two or more
detailed journal articles, perhaps relating to different recruitment periods for the same
cohort or to different index tests for the same cohort, then it is likely to be easier to perform data extraction separately for these articles and collate information from the data
collection forms afterwards. When data from all reports are extracted into a single data
collection form, review authors should identify the ‘main’ data source for each study
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when sources include conflicting data and these differences cannot be resolved by
contacting study authors (Mayo- Wilson 2018). Flow diagrams such as those modified
from the PRISMA statement can be particularly helpful when collating and documenting
information from multiple reports (Mayo- Wilson 2018).
7.5.5 Reliability andreaching consensus
When more than one review author extracts data from the same reports, there is
potential for disagreement. After data have been extracted independently by two or
more extractors, responses should be compared to ensure agreement or to identify
discrepancies. An explicit procedure or decision rule should be specified in the protocol for identifying and resolving disagreements. The source of the disagreement may
be an error by one of the extractors, which can be easily resolved. Thus, discussion
among the review authors is a sensible first step. A disagreement may also require
arbitration by another person. Disagreements that cannot be resolved could be
addressed by contacting the study authors.
Agreement of coded items before reaching consensus can be quantified, for example
using kappa statistics (Orwin1994), although this is not routinely done in Cochrane
Reviews. Regardless, the presence and resolution of disagreements should be carefully
recorded. Maintaining a copy of the data ‘as extracted’ (in addition to the consensus
data) allows assessment of the reliability of coding. Examples of ways in which this can
be achieved include the following.
●
Use one review author’s (paper) data collection form and record changes after con-
sensus in a different ink colour.
●
Enter consensus data onto an electronic form.
●
Record original data extracted and consensus data in separate forms (some online
tools do this automatically).
If agreement is assessed, this should be done only for the most important data (e.g. key
risk of bias assessments, or availability of key outcomes).
Throughout the review process informal consideration should be given to the reliability
of data extraction. For example, if after reaching consensus on the first few studies the
review authors note a frequent disagreement for specific data, then coding instructions
may need modification. Furthermore, a review author’s coding strategy may change
over time as the coding rules are forgotten, indicating a need for retraining and, possibly,
some recoding.
7.5.6 Suspicions ofscientific misconduct
Systematic review authors can uncover suspected misconduct in the published literature. Misconduct includes fabrication or falsification of data or results, plagiarism and
research that does not adhere to ethical norms. Review authors need to be aware of
scientific misconduct, because the inclusion of fraudulent material could undermine
the reliability of a review’s findings. Plagiarism of results data in the form of duplicated
publication (either by the same or by different authors) may, if undetected, lead to
study participants being double- counted in a synthesis. If plagiarism is suspected,
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text- matching software and systems such as CrossCheck may be helpful, but they can
detect only matching text, so data tables or figures need to be inspected by hand or
using other systems (e.g. to detect image manipulation). The comparison of extracted
2×2 tables could also provide a useful means of detecting duplicated studies. Section7.2.1
lists a number of different strategies for identifying multiple reports of the same study.
If misconduct is suspected, Cochrane Review authors are advised to consult with their
Cochrane editors. Searching for comments, letters or retractions may uncover additional
information. Sensitivity analyses can be used to determine whether the studies arousing
suspicion are influential in the conclusions of the review. Guidance for editors for
addressing suspected misconduct will be available from Cochrane’s Editorial Publishing
and Policy Resource (see community.cochrane.org). Further information is available
from the Committee on Publication Ethics (COPE; publicationethics.org), including a
series of flowcharts on how to proceed if various types of misconduct are suspected.
Cases should be followed up, typically including an approach to the editors of the
journals in which suspect reports were published. It may be useful to write first to the
primary investigators to request clarification of apparent inconsistencies or unusual
observations.
Because investigations may take time and institutions may not always be responsive
(Wager2011), studies suspected of being fraudulent should be classified as ‘awaiting
assessment’. If a misconduct investigation indicates that the publication is unreliable,
or if a publication is retracted, it should not be included in the systematic review, and
the reason should be noted in the ‘excluded studies’ section.
7.5.7 Key points inplanning andreporting data extraction
In summary, the methods section of both the protocol and the review should detail:
●
the data categories that are to be extracted;
●
how extracted data from each report will be verified (e.g. extraction by two review
authors, independently);
●
whether data extraction is undertaken by content area experts, methodologists or both;
●
pilot testing, training and existence of coding instructions for the data collection
form;
●
how data are extracted from multiple reports from the same study;
●
how data should be handled for multiple index tests or multiple reference standards
from the same study; and
●
how disagreements are handled when more than one review author extracts data
from each report.
7.6 Managing andsharing data andtools
When data have been collected for each individual study, it is helpful to organize them
into a comprehensive electronic format, such as a database or spreadsheet, before
such data can be used for a meta- analysis or other synthesis. When data are collated
electronically, all or a subset of them can easily be exported for cleaning, consistency
checks and analysis.
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Table7.6.a Example ofa simple review witha single ‘test’
Study Test TP FP FN TN Analysis
Basile 2015 Ultrasound 17 10 17 2 1
Brun 2012 Ultrasound 80 0 8 8 1
Brun 2014 Ultrasound 27 0 1 4 1
Chenaitia 2012 Ultrasound 116 0 2 12 1
Gok 2015 Ultrasound 52 0 4 0 1
Kim 2012 Ultrasound 38 1 6 2 1
Lock 2003 Ultrasound 43 0 15 2 1
Nikandros 2006 Ultrasound 15 0 1 0 1
Radulescu 2015 Ultrasound 28 0 2 2 1
Vigneau 2005 Ultrasound 34 0 1 0 1
Tabulation of collected information about studies can facilitate classification of study
data sets into appropriate groups. It is important through this process to retain clear
information on the provenance of the data, with a clear distinction between data from
a source document and data obtained through calculations.
Although RevMan cannot be used for meta- analysis of test accuracy data (see
Chapter9), we recommend that all extracted and checked 2×2data and covariates are
copied to RevMan before exporting them for analysis. This ensures as far as possible
that the data used to create forest plots of sensitivity and specificity and any SROC plots
are the same as those used for meta- analysis.
An example of a simple review is provided in Table7.6.a. This review of ultrasonography for confirmation of gastric tube placement in people who are unable to swallow
included 10 studies contributing 2×2data to a single RevMan ‘test’ (Tsujimoto 2017).
An example of a more complex review is provided in Table7.6.b. This review of secondtrimester serum tests for screening for Down syndrome included 59 studies with 2×2
tables organized into 89 different RevMan ‘tests’ (Alldred 2012). A number of different
assays were reported at varying thresholds and data were therefore organized according to test and threshold. The ‘Analysis’ column shows how data sets might be coded for
analysis purposes.
Another example of a complex review is shown in Table7.6.c. This review of rapid tests
for diagnosing malaria (Abba 2014) included studies of tests to detect two different types
of malaria (non- falciparum or Plasmodium vivax) using multiple different assays (e.g.
Falcifax or Onsite Pf/Pv) against different reference standards (microscopy or polymerase
chain reaction (PCR)) that are likely to significantly affect observed accuracy.
Although the collation and reconciliation of information from multiple reports is recommended (Section7.5.4), sometimes more than one 2×2 table from a study will be
eligible for the same analysis. For example, a study can report accuracy data for the
same test in more than one participant cohort from different centres. In the review of
rapid tests for diagnosing malaria, one study contributed data to the same analysis
from 10 different health centres (Table7.6.d).
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Table7.6.b Example ofa more complex review withmultiple ‘tests’
Study Test TP FP FN TN Analysis
Bartels 1990 SP1 at mixed cut- points 4 0 39 282 4
Bartels 1990 SP1 at 2.5MoM 7 4 36 278 5
Haddow 1998 Inhibin A at mixed cut-
Haddow 1998 Inhibin A at 2MoM 27 21 25 235 3
Pandian 2004 Inhibin A at mixed cut-
Pandian 2004 Inhibin A at 5% FPR 5 4 11 80 2
Pandian 2004 SP1 at mixed cut-
Pandian 2004 SP1 at 5% FPR 13 4 3 80 6
Van Lith 1992 Inhibin A at mixed cut- points 5 8 5 72 1
Van Lith 1992 Inhibin A at 2MoM 5 8 5 72 3
Wald 2003a Inhibin A at mixed cut-
Wald 2003a Inhibin A at 5% FPR 48 51 34 959 2
FPR, false positive rate; MoM, multiples of the median.
Table7.6.c Example ofa more complex review withmultiple ‘tests’ andreference standards
Study Test TP FP FN TN Target condition Reference
Alam 2011 OnSite Pf/pv 20 3 6 309 P. vivax PCR 4
Chanie 2011 Carestart Pf/Pv 25 4 0 1063 P. vivax Microscopy 3
Mekonnen 2010 Carestart Pf/Pv 61 0 3 176 P. vivax Microscopy 3
Rakotonirina
2008
Rakotonirina
2008
Sharew 2009 Carestart Pf/Pv 155 9 1 503 P. vivax Microscopy 3
Singh 2010 Parascreen 44 6 13 309 Non- falciparum Microscopy 1
Xiaodon 2013 Carestart Pf/Pan 59 0 6 115 Non- falciparum Microscopy 1
Xiaodon 2013 Carestart Pf/Pan 59 0 6 113 Non- falciparum PCR 2
Yan 2013 One Step Malaria
Yan 2013 One Step Malaria
Yan 2013 Pf/Pv Malaria
PALUTOP 19 0 2 292 P. vivax PCR 4
OptiMAL 15 6 2 290 Non- falciparum PCR 5
Pf/Pan
Pf/Pan
Device
points 27 21 25 235 1
points 5 4 11 80 1
points 13 4 3 80 4
points 48 51 34 959 1
standard
51 5 22 528 Non-
51 15 20 520 Non- falciparum PCR 2
43 7 30 270 P. vivax PCR 4
falciparum Microscopy 1
Analysis
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7.7 Chapter information
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Table7.6.d Example ofa review witha study withmultiple centres
Study Test TP FP FN TN Target condition Reference standard
Ashton 2010 Parascreen 203 96 43 2041 Non- falciparum Microscopy
Bendezu 2010 Parascreen 64 6 19 243 Non-
Elahi 2013 Parascreen 49 3 5 270 Non-
Endeshaw 2012(a) Parascreen 3 4 3 190 Non-
Endeshaw 2012(b) Parascreen 32 4 4 160 Non-
Endeshaw 2012(c) Parascreen 6 3 5 184 Non-
Endeshaw 2012(d) Parascreen 2 1 2 195 Non-
Endeshaw 2012(e) Parascreen 5 7 0 185 Non-
Endeshaw 2012(f) Parascreen 8 0 0 192 Non-
Endeshaw 2012(g) Parascreen 3 1 2 192 Non-
Endeshaw 2012(h) Parascreen 14 0 2 184 Non-
Endeshaw 2012(i) Parascreen 10 4 0 186 Non-
Endeshaw 2012(j) Parascreen 4 3 12 181 Non-
Singh 2010 Parascreen 44 6 13 309 Non-
falciparum Microscopy
falciparum Microscopy
falciparum Microscopy
falciparum Microscopy
falciparum Microscopy
falciparum Microscopy
falciparum Microscopy
falciparum Microscopy
falciparum Microscopy
falciparum Microscopy
falciparum Microscopy
falciparum Microscopy
falciparum Microscopy
Ideally, data only need to be extracted once and should be stored in a secure and
stable location for future updates of the review, regardless of whether the original
review authors or a different group of authors update the review (Ip2012).
Standardizing and sharing data collection tools as well as data management systems
among review authors working in similar topic areas can streamline systematic review
production. Review authors have the opportunity to work with study investigators,
journal editors, funders, regulators and other stakeholders to make study data publicly
available, increasing the transparency of research. When legal and ethical to do so, we
encourage review authors to share the data and the tools used in their systematic
reviews to reduce waste and to allow verification and reanalysis, because data will not
have to be extracted again for future use (Mayo- Wilson 2018).
7.7 Chapter information
Authors: Jacqueline Dinnes (Institute of Applied Health Research, University of
Birmingham, UK), Jonathan J. Deeks (Institute of Applied Health Research, University of
Birmingham, UK), Mariska M. Leeflang (Department of Epidemiology and Data Science,
University of Amsterdam, The Netherlands), Tianjing Li (Department of Ophthalmology,
University of Colorado Anschutz Medical Campus, USA)
Sources of support: Jonathan J. Deeks is a UK National Institute for Health Research
(NIHR) Senior Investigator Emeritus. Jonathan J. Deeks and Jacqueline Dinnes are supported by the NIHR Birmingham Biomedical Research Centre at the University Hospitals
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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: Jacqueline Dinnes, Jonathan J. Deeks and Mariska M.
Leeflang are members of Cochrane’s Diagnostic Test Accuracy Editorial Team. Mariska
M. Leeflang is co- convenor of the Cochrane Screening and Diagnostic Tests Methods
Group. Tianjing Li is co-convenor of the Cochrane Comparing Multiple Interventions
Methods Group and a member of the Cochrane Methods Executive. The authors declare
no other potential conflicts of interest relevant to the topic of this chapter.
Acknowledgements: This chapter re-
uses and builds on material included in the following chapter of the Cochrane Handbook for Systematic Reviews of Interventions to
ensure consistency in guidance for authors of Cochrane Reviews: Li T, Higgins JPT,
Deeks JJ (editors). Chapter5: Collecting data. In: Higgins JPT, Thomas J, Chandler J,
Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic
Reviews of Interventions version 6.1 (updated September 2020). Cochrane, 2020.
The authors would like to thank Jérémie Cohen, Eleanor Ochodo and Jean- Paul
Salameh for useful peer review comments.
7.8 References
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diagnostic tests for diagnosing uncomplicated non- falciparum or Plasmodium vivax
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CD011431.
Alldred SK, Deeks JJ, Guo B, Neilson JP, Alfirevic Z. Second trimester serum tests for Down’s
Syndrome screening. Cochrane Database of Systematic Reviews 2012; 6: CD009925.
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Pennsylvania Meta- analysis Blinding Study Group. Lancet 1997; 350: 185–186.
Buscemi N, Hartling L, Vandermeer B, Tjosvold L, Klassen TP. Single data extraction
generated more errors than double data extraction in systematic reviews. Journal of
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Cader MZ, Filer A, Hazlehurst J, de Pablo P, Buckley CD, Raza K. Performance of the 2010
ACR/EULAR criteria for rheumatoid arthritis: comparison with 1987 ACR criteria in a very
early synovitis cohort. Annals of the Rheumatic Diseases 2011; 70: 949.
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Danko KJ, Dahabreh IJ, Ivers NM, Moher D, Grimshaw JM. Contacting authors by telephone
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