Customize your JAMA Network experience by selecting one or more topics from the list below.
Lijmer JG, Mol BW, Heisterkamp S, et al. Empirical Evidence of Design-Related Bias in Studies of Diagnostic Tests. JAMA. 1999;282(11):1061–1066. doi:10.1001/jama.282.11.1061
Author Affiliations: Department of Clinical Epidemiology and Biostatistics, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands.
Context The literature contains a large number of potential biases in the evaluation
of diagnostic tests. Strict application of appropriate methodological criteria
would invalidate the clinical application of most study results.
Objective To empirically determine the quantitative effect of study design shortcomings
on estimates of diagnostic accuracy.
Design and Setting Observational study of the methodological features of 184 original studies
evaluating 218 diagnostic tests. Meta-analyses on diagnostic tests were identified
through a systematic search of the literature using MEDLINE, EMBASE, and DARE
databases and the Cochrane Library (1996-1997). Associations between study
characteristics and estimates of diagnostic accuracy were evaluated with a
Main Outcome Measures Relative diagnostic odds ratio (RDOR), which compared the diagnostic
odds ratios of studies of a given test that lacked a particular methodological
feature with those without the corresponding shortcomings in design.
Results Fifteen (6.8%) of 218 evaluations met all 8 criteria; 64 (30%) met 6
or more. Studies evaluating tests in a diseased population and a separate
control group overestimated the diagnostic performance compared with studies
that used a clinical population (RDOR, 3.0; 95% confidence interval [CI],
2.0-4.5). Studies in which different reference tests were used for positive
and negative results of the test under study overestimated the diagnostic
performance compared with studies using a single reference test for all patients
(RDOR, 2.2; 95% CI, 1.5-3.3). Diagnostic performance was also overestimated
when the reference test was interpreted with knowledge of the test result
(RDOR, 1.3; 95% CI, 1.0-1.9), when no criteria for the test were described
(RDOR, 1.7; 95% CI, 1.1-2.5), and when no description of the population under
study was provided (RDOR, 1.4; 95% CI, 1.1-1.7).
Conclusion These data provide empirical evidence that diagnostic studies with methodological
shortcomings may overestimate the accuracy of a diagnostic test, particularly
those including nonrepresentative patients or applying different reference
During recent decades, the number of available diagnostic tests has
been rapidly increasing. As for all new medical technologies, new diagnostic
tests should be thoroughly evaluated prior to their introduction into daily
practice. The number of test evaluations in the literature is increasing but
the methodological quality of these studies is on average poor. A survey of
the diagnostic literature (1990-1993) showed that only 18% of the studies
satisfied 5 of the 7 methodological standards examined.1
Different guidelines have been written to help physicians with the critical
appraisal of the diagnostic literature consisting of lists of criteria for
the assessment of study quality.2-4
Criteria enable readers to check whether studies fulfill methodological criteria
on study design, data collection, and methods of reporting the results.
As few diagnostic studies meet all of the methodological criteria, physicians
and reviewers are faced with a difficult choice. Strict application of the
methodological criteria would imply that only a small minority of the available
data can be used in clinical practice. Alternatively, inclusion of a wider
range of imperfect studies would require weighting of the evidence according
to the relative importance of the criteria that such studies failed to satisfy.
One article has reported such weights for methodological criteria. Unfortunately,
these weights were established through a consensus procedure in a general
internal medicine division at an academic medical center, rather than on empirical
data.5 A data-driven approach had been previously
used by Schulz et al6 when evaluating the influence
of study design features on estimates of treatment effects in randomized controlled
The purpose of our study was to assess empirically the impact of shortcomings
in design, data collection, and reporting on the estimates of diagnostic accuracy.
We compared estimates of diagnostic accuracy for a given test reported in
studies with lower quality with estimates for the same test from studies without
these shortcomings. We hypothesized that estimates of diagnostic accuracy
would be exaggerated in studies that failed to meet methodological standards.
An electronic search of the literature was performed to identify meta-analyses
summarizing the accuracy of diagnostic tests. We focused on meta-analyses
because they enabled us to identify a large number of studies on a single
diagnostic problem. We concentrated on recent meta-analyses as we expected
these meta-analyses to include both older studies, using suboptimal designs,
as well as recent studies, applying a more up-to-date approach that lives
up to current methodological standards. To be included, a meta-analysis had
to be based on a systematic search of the literature, had to include at least
5 studies, and had to report sensitivities and specificities of included studies.
The latter criterion was introduced to assure that from each reviewed study
sensitivity and specificity were available and to allow for easy replication
of our work.
The MEDLINE and EMBASE databases were searched (January 1996 to December
1997) using combinations of the words meta-analysis; diagnostic
imaging; diagnostic tests, routine; sensitivity and
specificity; and review, publication type.
In addition, the Cochrane Library and the DARE database of the NHS Centre
for Reviews and Dissemination were examined for relevant abstracts.
We retrieved 26 articles that included 5 or more studies. Fifteen articles
had to be excluded: 7 were not based on a systematic literature search and
8 reported no list of sensitivities and specificities. A list of excluded
articles is available from the authors.
For the 11 remaining articles, all original papers included in the analyses
were retrieved (Table 1). The
characteristics of these studies were extracted on a standard form by 1 of
the authors (J.G.L.). The set of characteristics on the standard form was
based on a synthesis from different lists of criteria for study quality.2,4,5 All studies were independently
scored a second time by a second reviewer (B.W.M., P.M.M.B.). Disagreement
was resolved by consensus, if necessary the judgment of a third reviewer was
The optimal design for assessing the accuracy of a diagnostic test is
considered to be a prospective blind comparison of the test and the reference
test in a consecutive series of patients from a relevant clinical population.2,7 A relevant clinical population is a
group of patients covering the spectrum of disease that is likely to be encountered
in the current or future use of the test. There are several threats to the
validity of a diagnostic study. Diagnostic accuracy can be overestimated if
the test is evaluated in a group of patients already known to have the disease
and a separate group of normal patients, rather than in a relevant clinical
population.8 This will be referred to as a
Selection bias can be present when not all patients presenting with
the relevant condition are included in order of entry (consecutive) into the
study, and when this selection is not random. If it was not clear from the
text that a consecutive series of patients was included or a random subset,
the corresponding study was scored as nonconsecutive.
Verification bias looms if the decision to perform the reference test
is based on the result of the test under examination. In many diagnostic studies
with an invasive reference test, most of the positive test results and only
a small part of the negative test results are verified. Alternatively, negative
test results are verified by a different, often less thorough, standard, for
example follow-up. We will refer to these 2 forms of verification bias as
partial verification bias and differential reference standard bias, respectively.
In cases in which more than 10% of the study group was not subjected to the
reference test, the study was scored as applying partial verification; in
cases in which different reference tests were used, the study was scored as
differential reference standard. All other cases were scored as complete verification.
Interpreting the reference test with knowledge of the results of the
test under study can lead to an overestimation of a test's accuracy, especially
if the reference test is open to subjective interpretation. If the sequence
of testing is reversed, it is important that the results of the test under
study are interpreted without knowledge of the reference test. If it was not
clear from the text that the interpretation of both tests was done while investigators
were blinded the study was scored as not blinded.
In addition to characteristics of the study design, we also looked at
methods of data collection and reporting. The data collection was categorized
as either prospective or retrospective. In case of doubt, the method of data
collection was scored as unknown. The reference test, the test under study,
and the study population should be described with sufficient detail to allow
for replication, validation, and generalization of the study.2
Descriptions of the tests were scored as sufficient if clear definitions of
positive and negative test results were mentioned in the text. Description
of the study population was sufficient if 2 of the following characteristics
were described: age of participants, female to male ratio, or distribution
The effect of study characteristics was examined with a regression model
that is adapted from the summary receiver operating characteristic curve model,
developed for meta-analyses of diagnostic tests.13-15
The basic model contains the logarithm of the diagnostic odds ratio computed
for a single study as a dependent variable and 2 explaining parameters, 1
for the intercept and 1 for the slope of the curve, for each meta-analysis.
The intercept can be interpreted as the common DOR of the corresponding test
and the parameter for the slope expresses variation of the DOR across individual
studies due to threshold differences.
We added covariates to this model to examine whether, on average, studies
that failed to meet the methodological criteria yielded different DORs. The
resulting parameter estimates of the covariates can be interpreted after antilogarithm
transformation as relative DORs (RDORs). They indicate the diagnostic performance
of a test in studies failing to satisfy the methodological criterion, relative
to its performance in studies with the corresponding feature. If the RDOR
is larger than 1, studies not satisfying the criterion yield larger estimates
of the DOR than studies with this corresponding feature.
In summary, the dependent variable of the model was the logarithm DOR.
Explaining variables were 2 parameters for each meta-analysis (the common
DOR and the threshold parameter) and 9 covariates to examine the effect of
the different study characteristics, 1 for each feature. All study characteristics
were evaluated simultaneously in a multivariate model.
A weighted linear regression analysis was used, with weights proportional
to the reciprocal of the variance of the log DOR. This weighted linear regression
assumes fixed effects. In case of zero entries, the DOR is not defined. This
problem was solved by adding 0.5 to all cells of the 2×2 table for all
studies in a meta-analysis.14-16
The model was fitted using maximum likelihood estimation, and programmed using
statistical software (S-plus 4.5, Mathsoft Inc, Cambridge, Mass).
The subjects of the 11 included articles and the reviewed tests are
summarized in Table 1. Two articles17,18 reviewed 3 tests, 3 articles19-21 reviewed 2 tests,
and 6 articles22-27
reviewed 1 test. This resulted in a total of 18 separate meta-analyses for
this analysis. These 18 meta-analyses summarized the results of 193 published
studies. Nine studies could not be used in the final analysis because only
abstracts were available for 4 and 2×2 table calculations were not possible
for 5. Of the 184 studies remaining, some evaluated multiple diagnostic tests.
A total of 218 diagnostic test evaluations were available for analysis.
The overall results of the quality assessment of the included studies
are listed in Table 2. Most studies
used a clinical cohort and described the cut-off that the test evaluated (98%
and 89%, respectively). Only 15 (6.8%) of the 218 studies satisfied all 8
criteria used. Sixty-four (30%) of the 218 studies satisfied 6 or more criteria.
The results from the regression analysis are presented in Figure 1. Studies using a case-control design tended to overestimate
the DOR 3-fold compared with studies with a clinical cohort (RDOR, 3.0). Studies
using different reference tests for positive and negative test results had
an RDOR of 2.2, showing approximately a 2-fold overestimation of the DOR compared
with studies that used 1 reference test. Studies verifying only part of the
population had on average the same DOR as studies that subjected all patients
to the reference test (RDOR, 1.0). There were no studies verifying only part
of the population and in conjunction using different reference standards.
Interpretation of the reference test with knowledge of the outcomes of the
test under study resulted in a RDOR of 1.3, causing an overestimation of the
DOR by approximately 30% compared with studies with adequate blinding. Selective
inclusion of patients into the study did not change the estimation of diagnostic
Retrospective data collection was not associated with an overestimation
or underestimation of diagnostic accuracy in comparison with studies with
prospective or unknown data collection. In a univariate analysis (data not
shown) the RDOR of studies with unknown data collection was nearest to that
of prospective studies, we therefore collapsed these 2 categories. The DORs
in articles without a sufficient description of the test under study or the
study population were, respectively, about 70% and 40% higher than estimates
in articles reporting sufficient details. Studies reporting no details of
the cut-off of the reference test had DORs that were approximately 30% smaller
than studies reporting the details of the reference tests.
This study describes the quantitative effects of characteristics of
study design on estimates of diagnostic accuracy. By collecting data from
studies in published diagnostic meta-analyses, we were able to examine the
effect of study characteristics on diagnostic accuracy. Our analysis shows
that studies of lower methodological quality, particularly those including
nonrepresentative patients or applying different reference standards, tend
to overestimate the diagnostic performance of a test.
The largest effect on the estimation of diagnostic accuracy was generated
by studies using cases and controls, also labeled as spectrum bias.28 Often, mild cases that are difficult to diagnose
are omitted from case-control studies, causing an overestimation of sensitivity
as well as specificity. Another large effect was seen in studies that used
different reference tests for the verification of positive and negative test
results. The effect of this differential verification depends on the quality
of the different reference tests used. Using a "gold" reference test for the
positive test results and a poor reference test for the negative results can
lead to an overestimation of both sensitivity and specificity of a test.29 For example, some studies evaluating the diagnostic
performance of C-reactive protein (CRP) for the diagnosis of acute appendicitis
used surgery and pathology as a reference test for patients with a high CRP.
Patients with a low CRP were not operated on and clinical follow-up determined
whether they were classified as having acute appendicitis. As low-grade infections
with low CRPs can resolve spontaneously, this verification strategy fails
to identify all false-negative test results. This way the diagnostic performance
of CRP will be overestimated. If the poor reference standard fails to identify
true-negative test results, the use of different reference standards can lead
to an underestimation of the diagnostic performance of a test.
The terms verification bias or workup bias are sometimes used when not all patients are subjected
to the reference test.29,30 We
prefer the term partial verification bias to differentiate
this situation from the situation in which different reference standards were
used for verification. In theory, verifying more positive test results than
negative test results will lead to an overestimation of sensitivity and an
underestimation of specificity, resulting in a either an increase or a decrease
of the DOR.29 In the analysis reported here,
partial verification resulted in DORs comparable with those from studies with
complete verification. The absence of an association with estimation of diagnostic
accuracy could be caused by the definition we used. Partial verification will
only lead to bias if systematically more abnormal than normal (or more normal
than abnormal) test results are subjected to the reference standard. In many
studies it was not clear why some patients were not subjected to the reference
standard and if this was related to the test under study. Therefore, we scored
studies as partial verification when 10% of the patients were not verified,
including studies in which patients were not verified due to a random error.
This could weaken the possible effect of partial verification in our analysis.
Some other studies also have shown no effect of partial verification on the
overall diagnostic accuracy of a test. In these studies only a shift of threshold
values along the receiver operating characteristic curve was observed.31,32
The average effect of inappropriate blinding was small. The studies
included in this analysis used many different reference standards ranging
from diagnostic imaging to histology. In case the reference standard is objective,
no effect is to be expected. Thus, in clinical situations with a subjective
reference standard, the effect of not blinding could be larger.
Given the distinction made between case-control and cohort studies,
we did not observe an influence of nonconsecutive sampling of patients. Retrospective
collection did not generate different results than studies with prospective
data collection (when corrected for all other methodological flaws).
When looking at the criteria for the methods of reporting, we found
a sufficient description of the test and a sufficient description of the population
associated with an overestimation of diagnostic accuracy in case of their
absence. As these criteria are not directly related to the study design, it
is unclear how they lead to an overestimation of diagnostic accuracy. Somehow
they seem to be predictors of methodological flaws in studies. In contrast
with these findings, studies with an insufficient description of the reference
standard generated less optimistic results compared with studies with an adequate
description. An explanation could be that these studies possibly had a large
variation in the interpretation of the reference standard. Large interobserver
variation is associated with a poor diagnostic accuracy, leading to an underclassification
of diseased persons.33 However, the same argument
could be used for lack of description of the test under study. Many times
we had difficulties in deciding whether the reference test was described with
enough detail. How much detail is needed if the reference standard is histology?
The definition was largely dependent on the clinical situation under study.
The scores of 20 studies changed after the consensus reading in comparison
with the first reading.
Four meta-analyses have examined the quantitative effect of study characteristics
on diagnostic performance. These analyses were published before 1996 and,
hence, not included in our sample. All were limited to a single test.34-37 Some
of these studies found that partial verification and absence of blinding affected
the estimates of diagnostic accuracy,33,34,36
while others found no effect of these characteristics.35
As in the first 3 meta-analyses, we found an overestimation of diagnostic
accuracy in studies without appropriate blinding, as in the latter we found
no effect of partial verification. One of these meta-analyses also looked
at the reporting method of the test and the reference test in combination
with other criteria and also found that insufficient description was associated
with overestimation of the diagnostic accuracy.37
The decision to limit our analysis to data from recent meta-analyses
could have affected our results. Extending our sample to older meta-analyses
would most likely change the relative frequency of the study characteristics,
but not necessarily the relative size of their effects on diagnostic accuracy.
Publication bias also has to be taken into account since only diagnostic
studies published in scientific journals were included in the analysis.38 One can speculate that studies have a higher likelihood
of being published when they are either of good quality or when they show
encouraging results. Such a selective publication policy could lead to an
inflation of the associations we found. It is difficult to examine the effect
of publication bias since there is no registration of unpublished diagnostic
studies. For future research in this field and for reviewers of diagnostic
tests, such a central registration of diagnostic research protocols would
When reading the results of a single diagnostic study, it is difficult
to weigh the methodological flaws against the available evidence. How large
is the possible overestimation and will it have clinical consequences? In
a study with different reference standards, without blinding and lacking a
description of the test, the DOR would on average be overestimated by 5-fold
based on the results of our analysis. This is equal to reporting a sensitivity
and specificity of about 84% when in fact both should be 70%. Differences
will be smaller if sensitivity and specificity are higher and if only a few
minor criteria are not fulfilled.
Our results stress the importance of adequate methodology and the need
for complete and reliable reporting of research. Assessment of quality is
only feasible in the light of complete clarity on the methodology. Authors
should therefore describe explicitly their methods of patient selection, methods
of disease verification, and criteria for interpretation of the test and the
This study shows that shortcomings in design, data collection, and reporting
affect estimates of diagnostic accuracy. Investigators should be aware of
this when designing their studies and readers should be aware of this when
interpreting the results. Our results can be of help in determining the merits
of the available evidence when appraising literature. Greater editorial vigilance
could help make researchers aware of current methodological standards and
thereby decrease the potential for bias in future diagnostic studies.