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12.3 Assessing thestrength ofthe evidence
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12.3.1.1 How valid are thesummary estimates?
Validity here refers to low risk of bias. The QUADAS- 2 tool and QUADAS- C (its extension
for comparative accuracy studies) are recommended for assessing risk of bias and
applicability of test accuracy studies (see Chapter8). High risk of bias caused by participant selection, conduct of the index test, conduct and nature of the reference standard
or flaws in the flow of participants, including timing of tests or missing values, may have
a negative impact on the findings of a systematic review.
Just stating that the findings may come from studies with a high risk of bias is insufficient; review authors should judge to what extent included studies with a high risk of
bias may lead to bias in the summary estimates or affect the findings of the systematic
review otherwise. For example, one or two studies at high risk of bias in a large number
of included studies will have a different impact on estimates compared to one or two
studies at high risk of bias in a review containing a small number of included studies.
One way to gain some insight into the effect of studies with high or unclear risk of bias
is to perform sensitivity analyses by excluding such studies (see Chapter9, Section9.4.9).
If the results of the sensitivity analyses indicate that such studies greatly influence the
findings of the review and thus question the robustness of the findings, then this should
be explicitly noted.
12.3.1.2
How applicable are thesummary estimates?
A study can be at low risk of bias but, because of the included participants, test or reference standard, may nevertheless not apply well to the review question. Concerns regarding the applicability of the individual studies to the review question can be assessed
using QUADAS- 2. Review authors should judge to what extent studies with some concerns regarding applicability may affect the strength of the results in the whole body of
evidence. The results of sensitivity analyses may support these judgements.
A specific concern regarding applicability may occur in comparative accuracy studies.
As stated in Chapter5, studies that directly compare two tests could have recruited a
study group that is unrepresentative of the patient population in whom the tests will be
used in clinical practice. For example, a systematic review assessed whether exercise
electrocardiography (ECG) may be replaced with computed tomography (CT) coronary
angiography to detect coronary stenosis in patients with stable angina pectoris suspected of coronary disease (Nielsen 2014). The review authors only included studies
that directly compared the two index tests. The exercise ECG test is usually done in
patients with less severe coronary disease, whereas CT coronary angiography is used in
more advanced cases. Therefore, only including studies of patients who received both
tests in the past decreases the confidence in the applicability of the resulting comparative accuracy estimates, even though the risk of bias for estimates of accuracy generated by studies that directly compared the two index tests may be low.
12.3.1.3 How heterogeneous are theindividual study estimates?
Variability in the study results may be explained by chance variation, if small and
fewstudies have been included. Alternatively, it may be explained by the recruitment
from different study populations, differences in the use and/or definition of the index
tests, the use of different thresholds for test positivity, or variation in study methods.
In Chapter 9, heterogeneity is defined as variation that goes beyond what may be
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expected by chance. Chapter9 also explains how potential sources of heterogeneity
can be investigated.
In the Discussion section of a review and in the ‘Summary of findings’ tables, review
authors should comment not only on the effect of possible sources of heterogeneity,
but also on the consequences of heterogeneity for the interpretation of the findings.
The accuracy of a test is not a fixed property; it is likely to vary across populations and
settings. Hence, heterogeneity is expected in systematic reviews of diagnostic accuracy.
Whether heterogeneity exists may be difficult to assess in systematic reviews of a single
test. If the sensitivity and specificity estimates of all studies in a review lie within a relatively narrow space in the ROC space, then one could claim little or no heterogeneity.
When the estimates of sensitivity and specificity vary between 0% and 100%, the variation is likely to go beyond chance variation. However, the situations in between are
more difficult to assess, especially when the study results are also imprecise and the
role of chance may be considerable.
One way to communicate heterogeneity is to report a prediction region. As explained
in Chapter9, a 95% prediction region represents the region within which one has 95%
confidence that the true sensitivity and specificity of any future study (resembling the
studies included in the review) should lie (Harbord 2007). The greater the betweenstudy heterogeneity, the larger this region will be.
Another way to communicate heterogeneity may be to focus on a minimally required
estimate for sensitivity, specificity or another outcome measure. One could then explain
to what extent the studies provide point estimates above this minimally required
estimate, and how the confidence intervals of the studies relate to this point.
Little evidence and guidance have been developed to assess and communicate
inconsistency when summary receiver operating characteristic (SROC) curves and odds
ratios are presented. In those situations, review authors may comment on the SROC
plots and how the study estimates are visually distinct from the summary curve.
Assessment of heterogeneity in comparative studies may be focused on the difference
in sensitivity or specificity between the tests evaluated, in case of binary test results. For
example, if some of the included studies indicate that test A has a much higher sensitivity than test B, while other studies report that test B has a higher sensitivity, then these
studies contradict one another, which may indicate heterogeneity.
12.3.1.4 How precise are thesummary estimates?
If only one small study was found that assessed the accuracy of an index test, then the
estimates of sensitivity and specificity will be very imprecise. If a systematic review
contains many large studies with a considerable number of both people with and those
without the target condition, then the summary estimates will be a more precise
reflection of the sensitivity and specificity over all the included studies.
Review authors are therefore expected to report:
●
the number of included studies;
●
the number of included participants with the target condition, which informs estima-
tion of sensitivity;
●
the number of included participants without the target condition, which informs
estimation of specificity; and
●
the statistical uncertainty around the summary estimates (e.g. 95% confidence interval).
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These may be used to provide a judgement about the precision of the estimates.
The example of only one small study versus many large studies illustrates two
extremes. The judgement of imprecision is subjective, but one could pre- specify a
required width of the confidence interval (e.g. maximum 10 percentage points wide) to
support this judgement.
Another way to address imprecision is to check whether the confidence interval
crosses a pre- specified lower or upper bound of sensitivity or specificity. For example, if
the test needs to be at least 70% sensitive and the lower 95% confidence interval is
65%, then one could decide that the point estimate was not sufficiently precise to judge
whether the test is at least 70% sensitive. Such a pre- specification requires judgement
about the potential for a test to have clinical utility based on the role of the test in the
clinical pathway.
If no meta-
analysis can be done, review authors may report the individual study
estimates of test accuracy, including information about the imprecision of these estimates (e.g. confidence intervals). These findings may be summarized by providing a
range of estimates across included studies in the ‘Summary of findings’ table. In those
situations, it should be made clear that a summary estimate cannot be provided.
12.3.1.5 How complete is thebody ofevidence?
One of the most prominent threats to the validity of a meta- analysis is the omission of
certain results from the available literature. Even with the most comprehensive search
strategy, reporting bias may occur. This could result from selective reporting of results
within a study, from selective publication of studies, or from excluding reports in specific languages.
The most obvious form of this omission manifests as publication bias: studies with
promising or favourable results are more likely to be published and thus included in a
systematic review, which may bias the summary estimates towards more favourable
numbers (Simes1986).
Chapter9, Section9.5.3, addressed investigation and handling of publication bias.
Although an appropriate test for detecting funnel plot asymmetry in systematic reviews
of test accuracy that may indicate publication bias exists, it has limitations (Deeks
2005). Therefore, review authors are encouraged to explore how mechanisms underpinning reporting bias may operate in their clinical area and to inform the reader about the
potential for reporting bias whenever possible. For example, review authors with
knowledge about clinical testing pathways may be able to comment on the potential
for selective reporting of results in studies that describe patients undergoing ‘multiple
index tests’ as part of their routine care, but report the results of only some or one of
these tests.
Some decisions made in the review methods may also lead to more favourable results
than are justified. An example is language bias, caused by a restriction to reports in
English and the phenomenon that larger and more positive studies may be more frequently published in English- language journals. Another example is missing studies
due to flawed search strategies.
More information about the effects of choices in the search strategy and about publication bias can be found in Chapter6. Review authors should judge to what extent any
of these effects may be present and, if so, what their potential effect on the summary
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estimates would be. Expressing concerns about missing evidence and explaining why
review authors think this may be problematic is more informative than merely stating
that ‘there is publication bias’.
12.3.1.6 Were index test comparisons made between or within primary studies?
Many comparative accuracy reviews include studies assessing the accuracy of only one
of the index tests in the comparison. Estimates of accuracy from these single index test
accuracy studies may be at low risk of bias, with low concerns regarding applicability,
but estimates of comparative accuracy will then be based on an indirect comparison
(see Chapter9, Section 9.1.4.3) and may be susceptible to bias. Indirect comparisons
are at higher risk of bias than direct comparisons made using studies that have compared tests head to head.
With an indirect comparison, an observed difference in the accuracy of the index tests
may be confounded by the test settings, or by the difference in participants included in
the respective studies for each of the tests. If all exercise ECG studies in the example in
Section12.3.1.2 were done in patients with less severe coronary disease and all CT angiography studies were done in patients with more severe coronary disease, then the
observed difference in accuracy between the two tests may be due to differences in
disease severity rather than a genuine difference in accuracy.
12.4 GRADE approach for assessing the certainty of evidence
The GRADE Working Group developed a comprehensive and transparent system for
grading the certainty of evidence and subsequently for grading the strength of recommendations following from evidence. As more recent GRADE guidance uses the term
‘certainty of the evidence’ instead of ‘quality of the evidence’ or ‘strength of the evidence’, we also use the term ‘certainty’ in this section. In systematic reviews, the certainty in evidence reflects the extent to which we are confident that estimates are close
to the truth. GRADE publications of particular relevance to systematic reviews of test
accuracy include Schünemann (2008), Schünemann (2020a) and Schünemann (2020b).
The GRADE approach assesses certainty of evidence using the following five domains
that are described in detail in Section12.4.1.
●
Risk of bias
●
Indirectness (applicability)
●
Inconsistency (heterogeneity)
●
Imprecision
●
Publication bias
These domains overlap and correspond with the key features presented in
Section12.3.1, but may be used and named slightly differently within the GRADE framework. The certainty of the evidence starts as high when there are appropriate test accuracy studies that recruit a group of participants with an uncertain diagnosis and who
are representative of the target population. If a reason is found for downgrading, systematic review authors should use their judgement to classify the reason as either serious (e.g. downgraded by one level for serious imprecision) or very serious (e.g.
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downgraded by two levels for very serious imprecision). The overall certainty of the evidence for a given outcome can then range from high to very low. Review authors should
be transparent about their judgements byexplicitly stating the reason for downgrading
in footnotes (labelled as ‘Explanations’ in the GRADE ‘Summary of findings’ table), so
that the reader can understand the reason for the decision. See Table 12.2.c and
Table12.2.d for examples of ‘Summary of findings’ tables using the GRADE framework.
In addition, the GRADE approach for systematic reviews of interventions uses three
reasons to upgrade the certainty of the evidence from a lower to a higher level of certainty. These reasons are (1) a large effect; (2) any plausible confounding that would
reduce the effects found; and (3) a dose- response gradient. However, these reasons are
difficult to translate to systematic reviews of test accuracy. As clear guidance for upgrading diagnostic accuracy evidence is lacking, we do not advise upgrading.
Review authors using GRADE (or any other formal method) to assess the certainty of
the evidence should make this explicit in the Methods section of the review. The description of the application of GRADE should include how judgements were made and
whether the software package GRADEpro GDT was used to build the ‘Summary of findings’ tables (GRADEpro2020).
The GRADE guidance for diagnostic accuracy questions is mainly based on summary
estimates of the sensitivity and specificity of a single test, or the summary estimates of
the difference in sensitivity and specificity of two tests (Yang 2021).
12.4.1 GRADE domains for assessing certainty of evidence for test accuracy
Using the GRADE approach, the five domains are assessed separately for participants
with the target condition (sensitivity estimates, true positive and false negative) and
those without the target condition (specificity estimates, true negative and false positive). The five domains are described in detail in the following sections (Schünemann
2008, Schünemann 2020a, Schünemann 2020b).
12.4.1.1 Risk ofbias
Review authors may downgrade the certainty of evidence by one or two levels depending on the number (percentage) of included studies considered to have a high or unclear
risk of bias for one or more of the QUADAS- 2 domains (see Section 12.3.1.1 and
Chapter 8). Moving from risk- of- bias judgements of individual studies to an overall
judgement about downgrading the body of evidence, however, can be challenging and
relies on subjective judgement.
In a systematic review on tests for identifying people with glaucoma, the review
authors downgraded the certainty of the evidence for four of the five tests they assessed
due to risk of bias. For example, the certainty of the evidence for the sensitivity and
specificity of the Oblique flashlight test was downgraded one level because 40% of
studies had a high risk of bias in one or more QUADAS- 2 domains.
12.4.1.2 Indirectness (applicability)
Indirectness can be regarded as a synonym for applicability and can be assessed by
judging whether concerns regarding applicability, as identified with QUADAS- 2, justify
downgrading the certainty of evidence.
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Review authors may downgrade the certainty of evidence for indirectness if there are
important differences between the participants in the studies and the population stated
in the review question, with respect to either prior testing, spectrum of disease or
comorbidities, or settings.
They may also downgrade if there are important differences between the characteristics of the index test(s) and/or the expertise of the people applying the test(s) in the
studies compared to the characteristics of the test(s) and/or users in real- world
settings.
They may downgrade in case of comparative questions if the index tests were not
directly compared in each of the studies included in the test comparison (i.e. indirect
comparisons).
A systematic review of tests for plague downgraded the certainty of the evidence for
specificity one level for indirectness: there was a high concern about applicability due
to exclusion of people who received antibiotics prior to sample collection. The index
test was performed in a central laboratory, which may not reflect the field conditions in
case of an outbreak (Jullien 2020).
12.4.1.3
Inconsistency (heterogeneity)
Inconsistency can be caused by identifiable clinical heterogeneity, or it may remain
unexplained. As GRADE recommends downgrading for unexplained inconsistency in
sensitivity and specificity estimates, review authors should state whether they carried
out pre- specified analyses to investigate potential sources of heterogeneity and consider downgrading when they cannot explain inconsistency in the accuracy estimates.
Downgrading is not necessary if the inconsistency can be explained, for example due to
differences in setting or population or index test execution. If that is the case, the
‘Summary of findings’ tables should report the results per subgroup.
For example, in a systematic review about Xpert MTB/RIF Ultra and Xpert MTB/RIF
assays for extrapulmonary tuberculosis and rifampicin resistance in adults (Kohli 2021),
the review authors performed sensitivity analyses based on selected QUADAS- 2items
and found that they could not explain the heterogeneity. The review authors then
downgraded one level for inconsistency.
Questions to consider are ‘Are the individual point estimates in the forest plots more
or less the same?’ and, more importantly, ‘How much do confidence intervals overlap?’
A scatter plot of sensitivity and specificity (i.e. an SROC plot) is helpful for visual assessment of heterogeneity (‘Are the sensitivity–specificity pairs clustered closely together or
are they spread all over the ROC space?’). In addition, the size of the 95% prediction
region (if presented) can assist in this assessment, particularly when there are many
studies.
Downgrading for inconsistency may also be warranted when the differences between
two tests differ too much. What is regarded to be ‘too much’ should then be specified.
For example, one could decide– based on expected consequences of testing– that test
A is chosen over test B when the sensitivity of test A is at least 10 percentage points
higher than test B. When the review then includes studies that show much larger differences, studies showing no difference and studies with a difference the other way
(i.e.test B being more sensitive than test A), this may be a reason to downgrade for
inconsistency (Hultcrantz 2020).
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12.4.1.4 Imprecision
Efforts to provide guidance on how to operationalize the assessments of imprecision for
diagnostic test accuracy are ongoing. As per GRADE for systematic reviews of interventions and the explanation in Section12.3.1.1, one could look at the width of the 95%
confidence intervals of the summary estimates of sensitivity and specificity and assess
whether they cross a certain clinically acceptable lower limit (for which we would downgrade) or not. This clinically acceptable lower limit should depend on the clinical context and the potential consequences of testing, as explained in the clinical pathway (see
Chapter5, Section5.3.1).
The systematic review of tests for plague, mentioned earlier, downgraded the certainty of the evidence for specificity one level for imprecision. The review authors based
this judgement on the 95% confidence intervals around the proportion of false positives
and the proportion of false negatives: the lower limit of these confidence intervals
would lead to a different decision than the upper limit of the confidence intervals
(Jullien 2020).
12.4.1.5
Publication bias may occur when the body of evidence is incomplete. It can lower certainty of evidence, mainly because studies with favourable results tend to be published
more often than those with less favourable results. Although test accuracy studies with
promising results about the performance of tests seem to be published more rapidly
compared to those reporting lower estimates, there is no strong evidence that specific
results, either favourable or unfavourable, end up less frequently in meta- analyses
(Korevaar 2016). Still, review authors should be aware of the possibility of publication
bias (see Section12.3.1.5).
studies, in particular if they support a pre- existing hypothesis and were funded by a
body with a vested interest in a specific test (Schünemann 2020b). In some situations,
there may be direct evidence that results of particular studies have been withheld or
that studies are reporting partial findings, for example reporting only the sensitivity or
specificity of the test, or reporting only particular subgroups of patients. All these examples are publication and reporting practices that should be reported in the review.
Alternatively, if few concerns were identified, review authors may judge that publication bias was undetected.
2021) included studies with for- profit interest and small studies, which were thought
to be an indication of potential publication bias. However, the review authors did
notdowngrade for publication bias, as the literature search was thought to be comprehensive and the review authors contacted researchers of primary studies to identify
unpublished studies.
Publication bias
Downgrading may be considered when published evidence is limited to a few small
The systematic review about Xpert MTB/RIF Ultra and Xpert MTB/RIF assays (Kohli
12.5 Summary ofmain results inthe Discussion section
The Discussion section should begin with a restatement of the clinical question or
questions that the review is attempting to answer. It should then give a summary of the
results that provide answers to these questions. As a starting point, the number of
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included studies, total number of participants with and without the target condition,
results of the risk of bias and applicability assessments, and consistency of findings
should be summarized. This summary should be consistent with what was reported
inthe ‘Summary of findings’ table and may be an elaboration or explanation of the
information in the table.
If meta- analysis was performed, appropriate summary statistics from estimation of
summary points or summary curves should be reported (see Chapter11). Although sensitivity and specificity are the most frequent measures in test accuracy studies, there is
some evidence that predictive values are better understood (Whiting 2015). We therefore recommend that review authors present potential consequences of testing by
applying the summary estimates to a hypothetical cohort of persons to be tested.
If metamary of the review findings, if possible, combined with the range of estimates of test
accuracy from the included studies. Review authors should also summarize the relevance of the findings from investigations of heterogeneity, if such analyses were feasible. This section should be consistent with and link to the ‘Summary of findings’ table(s),
including the assessment of the certainty of the body of evidence, as outlined in
Chapter13, Section 13.3, or using the GRADE approach, as outlined in Section12.4.
Many of the issues discussed in previous sections may be briefly but clearly mentioned
in the Discussion section to provide a complete picture of the evidence.
analysis was not possible, review authors should provide a narrative sum-
12.6 Strengths andweaknesses ofthe review
In this section we focus on a narrative of the strengths and weaknesses of the primary
studies, the strengths and weaknesses of the systematic review methods and the consequences these may have for the interpretation of the review’s results and conclusions. If review authors are aware of key issues that potentially limit or bias the results
of their review, such as those considered when assessing the certainty of evidence for
the ‘Summary of findings’ table, then these should be pointed out to readers. Review
authors could consider using the following subheadings, when appropriate: ‘Strengths
and weaknesses of the included studies’ (or ‘Certainty of the evidence’) and ‘Strengths
and weaknesses of the review process’.
Review authors should discuss the strengths and weaknesses of the review with
regard to accuracy estimates, not the strengths and weaknesses of the evidence with
regard to policy- making decisions, which would rely on other considerations, such as
the acceptability or feasibility of the test, the impact of the test on people- important
outcomes, and resource requirements (costs).
12.6.1 Strengths andweaknesses ofincluded studies
This section narratively summarizes assessments made about the strength or certainty
of the evidence. It is important to highlight the strength of the evidence, including its
potential limitations, not only in the ‘Summary of findings’ tables but also in the main
text of the review.
Relative strengths of the included studies may be highlighted here, without overstating the implications. For example, it may be good to know that a review contained many
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large studies with very similar results. For comparative questions, it may be important
to highlight that results were obtained from fully paired (within- participant or head- tohead) comparative accuracy studies in the relevant population, and if the superiority of
one test over another was consistent across the included studies.
Relative weaknesses of the included studies should at least be summarized with reference to each of the four QUADAS- 2 domains (patient selection, index test(s), reference
standard, and flow and timing), highlighting items particularly relevant to the review
question, as reflected by the tailoring of QUADAS- 2 or QUADAS- C to the review topic. A
detailed discussion of the types of bias that might occur in test accuracy studies, including comparative test accuracy studies, can be found in Chapter8.
Review authors should be mindful that readers of systematic reviews of test accuracy
are likely to be less familiar with the types of bias that are encountered in test accuracy
research (Zhelev 2013) and that descriptions of the mechanisms underlying important
potential sources of bias may facilitate understanding.
Assessing the impact of bias on estimates of accuracy can be challenging, especially
as an overall quality score is discouraged (Whiting 2005). Assessment should include
consideration of the relative importance of the four QUADAS-
2 domains to the review
topic and the proportion and size of studies at risk of bias. The assessment of the
strength of the evidence can be helpful for such a summary, as described in Section12.4.
12.6.2 Strengths andweaknesses ofthe review
Limitations of the review focus on the review process and may include shortcomings in
the search strategy, selection of studies, data extraction and analyses. This section
should summarize the potential implications of these limitations on the strength of the
conclusions. Review authors may not have been able to conduct their review as originally intended in the protocol. For example, paucity of studies may have precluded
planned investigations of heterogeneity. Limitations of the search strategy should be
addressed as a shortcoming and the potential for bias caused by failure to retrieve or to
translate reports should be discussed.
12.6.2.1 Strengths andweaknesses dueto thesearch andselection process
Some decisions made in the retrieval process may lead to omissions in the body of evidence that may be relevant for its interpretation; for example, excluding certain languages or only searching in a limited number of databases. The potential consequences
of these choices should be addressed in this section.
Lack of consensus in the selection of included studies is another potential limitation
of the review process. If there has been substantial disagreement between review
authors about the inclusion of studies, there is a risk of including less appropriate studies or of excluding studies from which it is more difficult to extract data. Although the
effects of these shortcomings may be limited, they are still potential sources of bias.
12.6.2.2 Strengths andweaknesses dueto methodological quality assessment
anddata extraction
Information about the characteristics of participants, setting, study design and other
key information may be missing in reports of primary studies and, therefore, limit
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assessment of the methodological quality of included studies. The potential impact of
‘unclear’ assessments of risk of bias will depend on how these ‘unclear’ assessments
have been judged or interpreted when assessing risk of bias or concerns regarding
applicability. For example, if all ‘unclear’ assessments were ignored when judging risk
of bias, then the risk of bias for the whole body of evidence may be judged to be overly
optimistic.
Although contacting study authors is not a requirement of the review process, the
successful identification of additional data can be regarded as a strength of the review
process.
12.6.2.3
Weaknesses dueto thereview analyses
Although meta- analysis in general may result in more precise estimates than analysis of
individual primary studies, a small number of included studies or small number of participants may still jeopardize the precision of the results of the review. This especially
holds when substantial heterogeneity is observed, and when sources of heterogeneity
cannot be explored, letalone explained. In the assessment of the strength of the evidence, this is taken into account when considering imprecision and inconsistency.
With respect to heterogeneity and spectrum effects, review authors should make a
priori hypotheses about possible differences in accuracy between subgroups. The
Discussion section is the place to put these differences into context. Review authors
may be able to discuss how consistent results are across different clinical settings and
in different groups of individuals. This allows readers to judge to what extent summary
estimates of test accuracy can be applied to different clinical settings.
If review authors find apparent differences in accuracy between subgroups, they
should decide whether or not these effects are credible and relevant to readers. These
differences should be considered carefully, as variation in results due to chance may
play a role. It may be important to consider whether the variable was defined a priori;
whether the difference in subgroups was seen within studies rather than between studies; and whether there is additional evidence to support the relevance of findings in a
specific subgroup. Cochrane Reviews are written for an international audience, and the
discussion should not be limited to the applicability of results to a single healthcare
organization or country.
12.6.2.4 Direct andindirect comparisons
Reviews with a comparative question may base their conclusions on direct comparisons (the studies included in the review all directly compare the accuracy of different
index tests) or on indirect comparisons (primary studies evaluated one of the index
tests) or on both. As explained in Section12.3.1.6, direct comparisons are ideal because
they are less prone to bias due to confounding.
When both direct and indirect comparisons are included in the same review, it should
be clear that they are separate analyses and review authors should discuss any differences in results. In addition to potential for bias associated with indirect comparisons
(Takwoingi 2013), review authors should acknowledge that estimates of test accuracy
derived from indirect comparisons are typically based on a greater number of studies
than direct comparisons, leading to more statistical power to detect differences
between tests, if such differences exist, and to generate more precise estimates.
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