R E S E A R C H A R T I C L E Open Access
Frequency of data extraction errors and
methods to increase data extraction
quality: a methodological review
, Pauline Klaßen and Dawid Pieper
Background: Our objective was to assess the frequency of data extraction errors and its potential impact on results
in systematic reviews. Furthermore, we evaluated the effect of different extraction methods, reviewer characteristics
and reviewer training on error rates and results.
Methods: We performed a systematic review of methodological literature in PubMed, Cochrane methodological
registry, and by manual searches (12/2016). Studies were selected by two reviewers independently. Data were
extracted in standardized tables by one reviewer and verified by a second.
Results: The analysis included six studies; four studies on extraction error frequency, one study comparing different
reviewer extraction methods and two studies comparing different reviewer characteristics. We did not find a study
on reviewer training. There was a high rate of extraction errors (up to 50%). Errors often had an influence on effect
estimates. Different data extraction methods and reviewer characteristics had moderate effect on extraction error
rates and effect estimates.
Conclusion: The evidence base for established standards of data extraction seems weak despite the high
prevalence of extraction errors. More comparative studies are needed to get deeper insights into the influence of
different extraction methods.
Keywords: Systematic reviews, Data extraction, Accuracy, Errors, Reviewers
Systematic reviews (SRs) have become the cornerstone
of evidence based healthcare. A SR should use explicit
methods to minimize bias with the aim to provide more
reliable findings . The reduction of bias concerns all
process steps of the review. For example, bias can occur
in the identification of studies, in the selection of studies
(e.g. unclear inclusion criteria), in the data collection
process and in the validity assessment of included stud-
ies . Many efforts have been made to further develop
methods for SRs.
However, the evidence base for most recommenda-
tions that aim to minimize bias in the preparation
process for a systematic review in established guidelines
is sparse [1, 3, 4]. Previous studies found only little re-
search on the influence of different approaches on risk
of bias in systematic reviews . The use of work exten-
sive methods without really knowing the benefit might
waste scientific resources. For example a recent study
found that alternatives to duplicate study selection by
two independent reviewers (e.g. liberal acceleration) are
more cost-effective than independent study selection .
As the timely preparation and publication of SR is an
important goal to support decision making consider-
ations on the balance between resources/time and valid-
ity play an important role .
Data extraction is a crucial step in conducting SRs.
The term data collection is often used synonymously.
We defined data extraction as any type of extracting data
from primary studies into any form of standardized ta-
bles. It is one of the most time-consuming and most
critical tasks for the validity of results of a SR . Data
* Correspondence: Tim.Mathes@uni-wh.de
Institute for Research in Operative Medicinem, Chair of Surgical Research,
Faculty of Health, School of Medicine, Witten/Herdecke University,
Ostmerheimer Str. 200, 51109 Cologne, Germany
© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Mathes et al. BMC Medical Research Methodology (2017) 17:152
extraction builds the basis for the results and conclusion
in a SR. However, a previous study has shown a very
high prevalence of data extraction errors in SRs .
Our objective was to assess the frequency of data ex-
traction errors and its potential impact on results in sys-
tematic reviews of interventions. Furthermore, we
evaluated the effect of different extraction methods (e.g.
independent data extraction by two reviewers vs. verifi-
cation by a second reviewer), reviewer characteristics
(e.g. experience) and reviewer training on error rates
Information sources and search
We searched all PubMed databases and the Cochrane
Methodology Register (12/2016). The full search strat-
egies are provided in Additional file 1. The search strat-
egies were tested by checking whether articles already
known to us (e.g. Buscemi et al. ) were identified. We
screened all abstracts of the Cochrane Colloquium (since
2009) and searched the Cochrane database of oral, pos-
ter and workshop presentations (since 1994) in Decem-
ber 2016. In addition we cross checked the references-
list of all included articles, excluded articles, established
guidelines for systematic review preparation and system-
atic reviews on similar topics [1, 3–5, 7, 10]. Moreover,
we screened the publications linked in the related arti-
cles functions (cited articles and similar articles) in
PubMed for all included articles.
Eligibility criteria and study selection
Two types of articles were eligible. First, we included ar-
ticles on the frequency of data extraction errors. Second,
we included studies that compared aspects that can in-
fluence the quality of data extraction. We considered the
following types of comparisons:
–Comparison of different methods (index methods)
for data extraction regarding the involvement of a
second reviewer for quality assurance (e.g.
independent data extraction versus extraction by
one reviewer and verification by a second) for data
extraction (in the following called extraction
–Reviewer characteristics: experience, degree,
–Reviewer training: training on data extraction before
performing the review, including extraction of a
sample and calibration with the second reviewer and
oral and written instructions.
Studies on technical tools (software, dual monitors,
data extraction templates etc.) to reduce data extraction
errors were excluded.
All studies (studies on error frequency and compara-
tive studies) had to report a quantitative measure for
data extraction errors (e.g. error rate), determined by
use of a reference standard (a data extraction sample
that was considered correct). We included only studies
on the assessment of data extraction for intervention re-
views written in English or German.
All titles/abstracts identified in the electronics data-
bases were screened by two reviewers independently.
The abstracts of the Cochrane Colloquium and the data-
base of oral poster and workshop presentations were
screened by one reviewer. All potentially relevant full-
texts were screened by two reviewers (not independ-
ently). In case of discrepancies, we discussed eligibility
Data collection and analysis
We used a semi-structured template for data extraction
(see Tables 1, 2 and 3). For each study, we extracted the sam-
ple size (the number of included studies and, if applicable
the number of systematic reviews feeding the sample), infor-
mation on the index and reference method, and information
on outcome measures. We extracted all data on data ex-
traction errors and the influence on pooled effect esti-
mates. If possible, we distinguished data extraction errors
in accuracy (e.g. correct values), completeness (e.g. all
relevant outcomes are extracted)/selection (e.g. choice of
correct outcome measure or outcome measurement
time point) and correct interpretation (e.g. confuse mean
and median). In addition, we extracted quantitative mea-
sures for effort (e.g. time, resource use) and rates of
agreement between different approaches.
If provided in the article, we extracted confidence
limits (preferred) or p-values for all outcomes/compari-
sons. Rates with a denominator of at least 10 were con-
verted into percentages to increase the comparability of
All data were extracted in standardized tables. Be-
fore starting the data extraction the involved re-
viewers discussed each article to agree about the
relevant items for data extraction to avoid misinter-
pretation and omission. Subsequently, one reviewer
(experienced statistician) extracted all data and a sec-
ond reviewer (experienced epidemiologist) verified
the accuracy of data extraction.
Synthesis of data
Each included article was summarized in a structured
narrative way. The narrative synthesis includes informa-
tion on the sample (included reviews, included studies),
the index method, the reference standard considered as
correct data extraction and results (measures for
the quantification of errors and measures for the quanti-
fication of influence on the effect estimates).
Mathes et al. BMC Medical Research Methodology (2017) 17:152 Page 2 of 8
The search in the electronic databases resulted in 818
hits. The search of the abstracts of the Cochrane Col-
loquium and the database of the Cochrane database
of oral, poster and workshop presentations revealed
three additional potentially relevant publications. Add-
itionally, the reference list of included articles and
systematic reviews on similar topics did not reveal
further relevant articles. For two studies, no full-texts
were available. We screened the full-texts of nine arti-
cles. Of these, three studies were excluded [11–13].
The study selection process is illustrated in the flow-
diagram (Fig. 1).
The analysis included six studies [8, 9, 14–17]; four
studies on extraction error frequency [8, 15–17], one
study comparing different reviewer extraction methods
 and two studies comparing different reviewer
characteristics [14, 15] (Tendal et al.  included in
both analysis). No studies on reviewer training were
Studies on frequency of extraction errors
Carroll et al. compared the results of three dichotomous
outcomes . The database was three systematic re-
views on the same topic that included the same studies
(N= 8). Their own systematic review was used as the ref-
erence standard. Deviations in the other systematic re-
views were considered as errors. The rate of data
extraction errors ranged between 8% and 42% (depend-
ing on outcome and review). Differences in pooled effect
estimates were small (difference of relative risk, range:
Gøtzsche et al.  replicated the results of 27 meta-
analyses. For the quantification of errors two trials of each
meta-analysis were randomly selected. The reference
Table 1 Results of studies quantifying the frequency of data extraction errors
Study Studies included (reviews) Measure Result
Carroll 2013  8 (3) Selection (outcome 1) 17% (review 1 vs. reference standard); 8%
(review 2 vs. reference standard)
Selection (outcome 2) 42% (review 1 vs. reference standard); 25%
(review 2 vs. reference standard)
Selection (outcome 3) 21% (review 1 vs. reference standard); 25%
(review 2 vs. reference standard)
Inaccuracy (outcome 1) 8% (review 1 vs. reference standard); 8%
(review 2 vs. reference standard)
Inaccuracy (outcome 2) 17% (review 1 vs. reference standard); 13%
(review 2 vs. reference standard)
Inaccuracy (outcome 3) 13% (review 1 vs. reference standard); 8%
(review 2 vs. reference standard)
Difference in meta-analysis (outcome 1) RR 1.70 (reference standard) /
RR 1.71 (review 1)
Difference in meta-analysis (outcome 2) RR 0.85 (reference standard) / RR 0.87
(review 1) / RR 0.80 (review 2)
Difference in meta-analysis (outcome 3) RR 0.38 (reference standard)
/ RR 0.40 (review 1)
Gøtzsche 2007  54 (random selected;
Difference in SMD >0.1 of at least 1
of the 2 included trials
20 (10 meta-analysis
) Difference in SMD >0.1 of pooled
Jones 2005  NR (34) Errors (all types) 50%
Correct interpretation 23.3%
Impact on results All data-handling errors led to changes
in the summary results, but none
of them affected review conclusions
Tendal 2009  45 (10 meta-analysis) Difference in SMD because of
reviewer disagreements < 0.1
Difference in SMD because of
reviewer disagreements < 0.1
: meta-analyses at least including one erroneous trial;
author statement (no quantitative measures provided); ns no significant differences; NR not reported, RD
relative difference, RS reference standard, SMD standardized mean difference
Mathes et al. BMC Medical Research Methodology (2017) 17:152 Page 3 of 8
standard was data extraction by two independent re-
viewers. A difference in the standardized mean difference
of greater 0.1 was classified as error. In 63% of meta-
analysis at least one of the two trials were erroneous. Of
these meta-analyses 70% also showed an error of the
pooled effect estimate.
In the study of Jones et al.  42 systematic reviews of a
Cochrane group were included. A statistician checked the
data of these systematic reviews for errors. Half of the sys-
tematic reviews contained errors and approximately 23% of
this were misinterpretations (e.g. confusing standard devia-
tions and standard error). According to the study authors
Table 2 Characteristics of studies comparing different reviewer extraction methods and reviewer characteristics
Study Comparator/s Reference
Buscemi 2006  One reviewer verification
by a second
Two reviewers independently Extraction by one reviewer
and verification by an
N= 30 (6 meta-analysis)
Horton 2010  Minimal data extraction
experience (n= 28)
Moderate data extraction
experience (n= 19)
Substantial data extraction
experience (n= 23)
review experience (n= 28)
Moderate systematic review
experience (n= 31)
Substantial systematic review
experience (n= 18)
Minimal overall experience
(n= 26) Moderate overall
Tendal 2009  Experienced methodologists PhD students No reference standard
(comparison of raw
agreement between reviewers)
45 (10 meta-analysis)
denominator or subtrahend;
based on time involved in systematic reviews and data extraction and the number of systematic reviews; NA not applicable
Table 3 Results of studies comparing different reviewer extraction methods and reviewer characteristics
Measure Result (effect measure, CI or p-value)
Buscemi 2006  Agreement rate 28.0% (95% CI: 25.4, 30.7, range 11.1–47.2%)
Errors (all types) RD 21.7% (p= 0.019)
Omission RD 6.6% (p= 0.308)
Time (min, mean) RD −49 (p= 0.03)
Difference of pooled effect estimates * 0/0
Horton 2010  Errors (all types) 24.3%/26.4%/25.4% (p= 0.91)
Inaccuracy 14.3%/13.6%/15.7% (p= 0.41)
Omission 10.0%/12.1%/12.1% (p= 0.24)
Time (min, mean) 200/149/163 (p = 0.03)
MD in point estimates of meta -analysis ns (5 outcomes)
Errors (all types) 25.0%/ 26.1%/ 24.3% (p= 0.73)
Inaccuracy 14.6%/ 13.2%/ 15.7% (p= 0.39)
Omission 10.0%/ 11.4%/ 10.7% (p= 0.53)
Time (min, mean) 198/179/ 152 (p= 0.01)
MD in point estimates of meta -analysis ns (5 outcomes)
Errors (all types) 26.4%/27.9%/27.9% (p= 0.73)
Inaccuracy 16.4%/12.1%/15.7% (p= 0.22)
Omission 10.4%/12.1%/13.6% (p= 0.47)
Time (min, mean) 211/180/173 (p= 0.12)
MD in point estimates of meta -analysis ns (5 outcomes)
Tendal 2009  Difference of SMD < 0.1 61%/46% (NR)
Difference of SMD < 0.1 (pooled estimates) 33%/27% (NR)
*p<0.05; CI confidence interval, MD mean difference, ns not statistical significant differences (according to authors, significance not specified), NR not reported, RD
relative difference, SMD standardized mean difference
Mathes et al. BMC Medical Research Methodology (2017) 17:152 Page 4 of 8
“all data-handling errors led to changes in the summary re-
sults, but none of them affected review conclusions”.
Tendal et al.  estimated the influence of deviations
in data extractions between reviewers on results. A stan-
dardized mean difference smaller than 0.1 was consid-
ered as reviewer agreement. Approximately, 53% and
31% reviewers agreed on the trial level and meta-analysis
level, respectively. At the level of meta-analysis the dif-
ference in standardized mean difference (SMD) was at
least 1 in one of ten meta-analyses.
influence on the effect estimates for each included study.
Studies comparing different reviewer extraction methods
and reviewer characteristics
Buscemi et al.  compared data extraction of 30 ran-
domized controlled trials by two independent reviewers
with data extraction by one reviewer and verification by
a second. The reference standard was the data extraction
by one reviewer with verification by an experienced stat-
istician. The agreement rate of the two extraction
methods was 28%. The risk difference of the total error
rate was statistically significant (risk difference 21.7%, p
= 0.019) in favor of double data extraction. This differ-
ence was primarily because of inaccuracy (risk difference
52.9%, p= 0.308). However, in average double data ex-
traction took 49 min longer. Pooled effect estimates of
both extraction methods varied only slightly and were
not statistically significant different from the reference
standard in any of the meta-analyses (N= 6).
In the study by Horton et al.  the data extractions
of minimally experienced, moderately experienced and
substantially experienced reviewers were compared. Ex-
perience classifications were based on years involved in
systematic review preparation (≤2, 4–6, >7) and the num-
ber of performed data extractions (≤50, 51–300, >300).
Three outcome measures were used; systematic review ex-
perience, extraction experienceandacompositeofthese
two (overall experience). The reference data extractions
were prepared by independent data extraction by two re-
viewers and additional verification with the data in the ori-
ginal review. Error rates were similar across all overall
experience levels. The median error rates ranged between
24.3%–26.4%, 13.6%–15.7% and 10.0%–12.1% for total er-
rors, inaccuracy and omission, respectively (p-values >0.2
for all comparisons). Unexperienced reviewers required
more time for data extraction (range: 173–211). There were
no statistically significant differences (according authors, p-
values not specified) in point estimates (5 outcomes) of
meta-analysis between overall reviewer experience levels.
Tendal et al.  estimated the influence of deviations
in data extractions between reviewers on results. Agree-
ment was defined as a standardized mean difference for
the effect estimates smaller than 0.1. Agreement was cal-
culated for all pairs of experienced methodologist and all
pairs of PhD students. Experienced methodologists
agreed more than the PhD students (61% vs. 46% of tri-
als; 33% vs. 27% of meta-analyses).
Table 2 shows the characteristics, and Table 3 the re-
sults of the comparison of different extraction methods
and reviewer characteristics.
There is a high prevalence of extraction errors [8, 13, 15,
16]. However, extraction errors seem to have only a mod-
erate impact on the results/conclusion of systematic re-
views. Nevertheless, the high rate of extraction errors
indicates that measures for quality assurance of data
Fig. 1 Flow-diagram of study selection
Mathes et al. BMC Medical Research Methodology (2017) 17:152 Page 5 of 8
extraction are important to minimize the risk of biased
results and wrong conclusions .
Comparative evidence on the influence of different re-
viewer extraction methods and reviewer characteristics
is sparse [9, 14, 15]. Reviewer characteristics seem to
have only a moderate influence on extraction errors [7,
9]. Data extraction by two independent reviewers seems
to result in less extraction errors than data extraction by
one reviewer and verification by a second. These large
differences might cause significant difference in effect
estimates . However, in view of the limited influence
on the conclusions of a systematic review, double data
extraction of all data by two independent reviewers
seems not always necessary, but reduced data extraction
methods might be justified. The basic principal of re-
duced extraction methods is the focus on critical aspects
(e.g. primary outcomes), which constitute the basis for
conclusions, and reduced emphasize on the data extrac-
tion of less important aspects (e.g. patient characteris-
tics, additional outcomes). Also in the Methodological
Expectations of Cochrane Intervention Reviews
(MECIR), it is stated that “dual data extraction may be
less important for study characteristics than it is for out-
come data, so it is not a mandatory standard for study
characteristics”and “dual data extraction is particularly
important for outcome data, which feed directly into
syntheses of the evidence, and hence to the conclusions
of the review”. This is comparable to the recent pol-
icy of the Institute of Medicine (IOM). The IOM states
that “at minimum, use two or more researchers, working
independently, to extract quantitative and other critical
data from each study”.
Such methods would reduce the effort for data ex-
traction. Considering that the reviewer experience
showed only little influence on extraction errors, cost
might be further reduced by not employing only ex-
perienced methodologists but also staff with less sys-
tematic review experience for data extraction [14, 15].
However, reviewer training seems especially import-
ant, if less experienced reviewers are involved. The
reviewer team should be trained in data extraction
(e.g. using a sample) before performing the complete
data extraction to harmonize data extraction and clear
up common misunderstandings. This could in par-
ticular reduce interpretation and selection errors .
The reduction of time and effort is especially relevant
for rapid reviews because this form of evidence syn-
thesis aims timely preparation while remaining sys-
tematic . Thus, clarifying which methods can
reduce the time required for preparation without sig-
nificantly increasing the risk of bias would also con-
tribute to better control the short cuts in rapid
reviews. The risk of bias of reduced data extraction
methods could be further reduced if a detailed
systematic review protocol and data entry instructions
are prepared beforehand because the risk of selecting
wrong outcomes (e.g. time points, measures) and
omission would be reduced [15, 22].
The wide range of agreement between different extrac-
tion methods suggests that some studies are more diffi-
cult to extract than others . There can be many
reasons for this. First, data extraction is probably
dependent on the reporting quality in primary studies.
Often methods and results of trials are insufficiently re-
ported [23, 24]. Bad reporting can complicate the identi-
fication of relevant information (e.g. primary outcome is
not clearly indicated) and impede the extraction of treat-
ment effects in a useful manner (e.g. only statements on
statistical significance without effect measures and confi-
dence intervals). Thus, bad reporting can increase the
risk of omission and varying interpretation.
Second, the level of necessary statistical expertise might
vary by study. Also reviewers who are familiar with a var-
iety of statistical methods may not be aware of more ad-
vanced statistical methods (e.g. hierarchical regression
models) or recently developed methods (e.g. mean cumu-
lative function). Data extraction is particularly challenging
when very different statistical methods (which can result
in different effect estimates) are used across articles.
Third, different backgrounds (e.g. clinicians, epidemi-
ologists) and levels of expertise might also play a role.
However, drawing conclusions for practice seems diffi-
cult without an approach to differentiate between “easy”
and “difficult”studies beforehand (e.g. tools to classify
statistical complexity). Furthermore, it should be ac-
knowledged that the complexity of data extraction not
only depends on the data items in included studies but
also on its aim. For example, to support data extraction
a guide for complex meta-analysis (DECiMAL) has been
recently published .
Findings are consistent across our included studies.
Moreover, for short cuts in other process steps (e.g.
quality assessment) that can have influence on the risk
of bias in systematic reviews, similar results have been
observed. For example, a study on the influence of
searching multiple databases found only a weak impact
of reducing the number of searched databases (because
of not searching additional databases) on the results of
meta-analysis and review conclusions [18, 25]. There-
fore, also for other process steps, research on extraction
methods seems to be needed. Future research should
consider the influence on the risk of bias as well as
the impact on scientific resources.
Although the included studies are probably not free
from bias we did not perform a formal risk of bias as-
sessment using a checklist because there is no
Mathes et al. BMC Medical Research Methodology (2017) 17:152 Page 6 of 8
established risk of bias tool for methodological studies.
A source for risk of bias (correct extraction) is probably
the imperfect reference standard in the included studies.
For example, in the study by Jones et al.  only one
statistician performed data extraction of results. Carroll
et al.  considered their own systematic review as
the reference standard. However, a perfect gold standard
might be hardly achievable in such studies. Moreover,
most studies considered only a certain research question
regarding patients, intervention and comparison. The
generalizability of the results is therefore unclear.
The search terms describing our research question
were very unspecific (e.g. data collection). Because of this
reason we used field limitations to balance the specificity and
sensitivity. Therefore, we might not have identified all rele-
We did not investigate novel computer-aided ap-
proaches for data extraction. Computer-aided data ex-
traction can result in more efficiency and accuracy .
Although such approaches are only in their infancy, it
can be expected that they will become more common in
the future. Biomedical natural language processing tech-
niques will be developed further in the near future .
There is a high prevalence of extraction errors. This
might cause relevant bias in effect estimates [8, 15–17].
However, there are only a few studies on the influence of
different data extraction methods, reviewer characteris-
tics and reviewer training on data extraction quality.
Thus, the evidence base for the established standards of
data extraction seems sparse because the actual benefit
of a certain extraction method (e.g. independent data ex-
traction) or the composition of extraction team (e.g. ex-
perience) is not sufficiently proven. This is surprising
given that data extraction is a very crucial step in conduct-
ing a systematic review. More comparative studies are
needed to get deeper insights into the influence of differ-
ent extraction methods. In particular, studies investigating
training for data extraction are needed because there is no
such analysis, to date. Similar studies were recently pub-
lished for risk of bias assessment . The application of
methods that require less effort without threating the in-
ternal validity would result in a more efficient utilization
of scientific manpower. Increasing the knowledge base
would also help to design effective training strategies for
new reviewers and students in the future.
Additional file 1: Search strategies. (DOCX 13 kb)
SR: Systematic review
This research did not receive any specific funding.
Availability of data and materials
TM: idea for the review, study selection, data extraction, interpretation of
results, writing of manuscript. PK: study selection. DP: idea for the review,
study selection, verification of data extraction, interpretation of results. All
authors read and approved the final manuscript.
Ethics approval and consent to participate
Not applicable. This work contains no human data.
Consent for publication
All authors reviewed the final manuscript and consented for publication.
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Received: 17 July 2017 Accepted: 15 November 2017
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