Psychology, 2010, 1: 194-198
doi:10.4236/psych.2010.13026 Published Online August 2010 (
Copyright © 2010 SciRes. PSYCH
Using Generalizability Theory to Evaluate the
Applicability of a Serial Bayes Model in Estimating
the Positive Predictive Value of Multiple
Psychological or Medical Tests
Clarence D. Kreiter
Office of Consultation and Research in Medical Education, Department of Family Medicine, University of Iowa, Iowa City, USA.
Received May 10th, 2010; revised June 14th, 2010; accepted June 16th, 2010.
Introduction: It is a common finding that despite high levels of specificity and sensitivity, many medical tests are not
highly effective in diagnosing diseases exhibiting a low prevalence within a clinical population. What is not widely
known or appreciated is how the results of retesting a patient using the same or a different medical or psychological
test impacts the estimated probability that a patient has a particular disease. In the absence of a ‘gold standard’ special
techniques are required to understand the error structure of a medical test. Generalizability can provide guidance as to
whether a serial Bayes model accurately updates the positive predictive value of multiple test results. Methods: In or-
der to understand how sources of error impact a test’s outcome, test results should be sampled across the testing condi-
tions that may contribute to error. A generalizability analysis of appropriately sampled test results should allow re-
searchers to estimate the influence of each error source as a variance component. These results can then be used to
determine whether, or under what conditions, the assumption of test independence can be approximately satisfied, and
whether Bayes theorem accurately updates probabilities upon retesting. Results: Four hypothetical generalizability
study outcomes are displayed as variance component patterns. Each pattern has a different practical implication re-
lated to achieving independence between test results and deriving an enhanced PPV through retesting an individual
patient. Discussion: The techniques demonstrated in this article can play an important role in achieving an enhanced
positive predictive value in medical and psychological diagnostic testing and can help ensure greater confidence in a
wide range of testing contexts.
Keywords: Generalizability Theory, Bayes, Serial Bayes Estimation, Positive Predictive Value, Psychological Testing,
Serial Medical Testing
1. Introduction
When a medical disease’s prevalence and a medical
test’s specificity and sensitivity are known, an equations
based on Bayes Theorem provides useful information
related to the diagnostic power of a medical test. It is a
common finding that despite high levels of specificity
and sensitivity, many medical tests are not highly effec-
tive in diagnosing diseases with a low prevalence within
a clinical population [1]. Since a large number of dis-
eases occur only in a small proportion of the population
(i.e. have low prevalence), the low positive predictive
value (PPV) of medically diagnostic tests is of obvious
concern to physicians attempting to identify the presence
of a low prevalence disease. To provide an example, let’s
suppose a physician is attempting to determine whether a
patient has a disease that occurs in 1% of a defined pa-
tient population. When the test is performed on patients
with the disease, it yields a positive test result indicating
the presence of the disease in 90% of the patients (sensi-
tivity equals .90). When the test is performed on patients
without the disease, it correctly identifies 98% of those
patients as disease free (specificity equals .98). An equa-
tion based on Bayes Theorem can be used to calculate
the probability that a patient with a positive test result
actually has the disease. The simple equation for calcu-
lating this probability is:
P (A | B) = P (B | A) * P (A) / P (B) (1)
Equation (1) describes the probability that a patient
Using Generalizability Theory to Evaluate the Applicability of a Serial Bayes Model in Estimating the 195
Positive Predictive Value of Multiple Psychological or Medical Tests
has the disease given a positive test result [P (A | B)], and
equals the probability of a positive test result given the
patient has the disease [P (B | A) - sensitivity] multiplied
by the probability of the disease [P (A) - prevalence]
divided by the overall probability of a positive test result
within the population [P (B)]. The denominator in Equa-
tion (1), the overall prior probability of a positive test
result, is derived as shown in Equation (2), where j is 1,
2… and takes on as many values as there are hypotheses.
In the case being discussed in this example problem,
there are just two possible hypotheses (Ho1: the patient
has the disease – Ho2: the patient does not have the dis-
ease) and hence in this example the sum is taken over
just two levels. Hence, the overall probability of a posi-
tive test result is the sum of the probabilities of a positive
test in those with (sensitivity) and without (1 – specificity)
the disease each multiplied by their prevalence in the
P ( B ) = [Σ j P ( B | A j ) P ( A j )] (2)
Equation (3) displays the calculation using the levels
of specificity, sensitivity and prevalence discussed in our
example. Despite high levels of specificity and sensitiv-
ity, the patient with a positive test result has only a 31%
chance of actually having the disease. This is a common
and well known type of finding related to medical testing
designed to detect low prevalence diseases.
P ( A | B ) = .90 * .01 / ((.90 * .01) + (.02 * .99)) = .31
What is not widely known or appreciated is how the
results of retesting a patient using the same or different
test will impact the estimated probability that the patient
has the disease. There is little guidance in the medical or
psychological literature regarding whether or how the
results from serial testing improve the ability to diagnosis
disease when the structure or cause of the dependence
between tests is uncertain. However, it is clearly impor-
tant for clinicians to understand how the PPV changes
when a patient is administered a second or third medical
or psychological test. When the assumption of test inde-
pendence applies, a serial Bayes model may provide
guidance within contexts like those presented in the ex-
ample just discussed.
When probabilities from a previous Bayes calculation
are used to update estimates of the prior probability [P
(A)], and when independence is confirmed, we can use a
Bayes serial calculation to derive the probability that a
patient has the disease given a second test result. Equa-
tion (4) presents the next step in the context of our ex-
ample using a Bayes serial calculation for a second con-
secutive positive test under the assumption that the two
tests are independent. With a second positive result, the
probability of having the disease goes from .31 to .95,
and our confidence in the diagnosis appears to improve
dramatically. It should be noted that under the assump-
tion of independence, parallel testing may also yield an
outcome similar to serial testing. So, although the focus
of this paper is on sequential or serially administered
tests, when time or the occasion of the test is not an im-
portant factor in determining test independence, what is
reported and discussed here may also apply to parallel
P ( A | B ) = .90 * .31 / ((.90 * .31) + (.02 * .69)) = .95
From the outcome presented in Equation (4), it appears
that the PPV of tests used to detect low prevalence dis-
eases may be dramatically improved simply by adminis-
tering the test a second or third time. However as men-
tioned, such positive outcomes rely on an independence
assumption that is critical to the valid application of the
serial Bayes probability model and implies that the error
rate for each test is independent. Therefore, to determine
whether an enhancement of PPV can be achieved by re-
testing, it is necessary to first establish the primary
source(s) of test error and whether, or under what condi-
tions, each medical test can be regarded as independent.
When a “gold standard” is available for determining
the accuracy of a fallible test, establishing the independ-
ence between two test administrations is straight forward.
One needs simply to twice calculate the specificity and
sensitivity for the second test administration, once for the
group of patients who test positive on the first test and
once for the group of patients who tested negative on the
first test. If the two calculations are in close agreement,
the assumption of independence is satisfied. Unfortu-
nately, a “gold standard” method for checking test accu-
racy is often not available, and other procedures are re-
Independence between test results can be achieved
when clinicians randomly sample from the test-related
variables that contribute to error and when each disease
positive patient is equally likely to display a false nega-
tive test result and when each disease negative patient is
equally likely to display a false positive test result. In-
deed, when the conditions leading to test independence
are understood, the utility of testing in a low prevalence
disease context can often be dramatically enhanced by a
simple random replication of a testing process that sam-
ples from the variables contributing to error. To ascertain
under what conditions an independence assumption is
satisfied, researchers must first investigate and under-
stand the error structure of medical or psychological test
outcomes. Given the potential for dramatically enhanced
diagnostic accuracy, such research is critically important
in improving the utility of certain tests with low PPV.
Within many testing contexts, it is often not possible
to establish the accuracy of a fallible test by comparing it
Copyright © 2010 SciRes. PSYCH
Using Generalizability Theory to Evaluate the Applicability of a Serial Bayes Model in Estimating the
Positive Predictive Value of Multiple Psychological or Medical Tests
to a more accurate “gold standard” testing methodology
[2,3]. Although methods for estimating disease preva-
lence with the use of multiple tests, none of which are
gold standard, have been developed [4], and latent class
analysis has been used to estimate prevalence, specificity
and sensitivity in the absense of a gold standard [5-7],
there is little guidance for revising diagnostic predictions
for a specific patient when evaluating multiple fallible
test results. When a “gold standard” test procedure is
unavailable, too risky, too invasive, and/or a violation of
ethical research standards, an alternate and efficient
method for establishing a test’s error structure and the
appropriateness of a serial Bayes-based revision of dis-
ease probability can be achieved using Generalizability
(G) analysis [8]. The strength of G theory analysis is that
it requires only a record of the fallible test outcomes and
does not require “gold standard” testing. When outcomes
for the fallible test are appropriately sampled and ana-
lyzed, precise quantification of relevant sources of error
can be achieved.
Generalizability analysis is by far the most powerful
method for quantifying sources of measurement error. In
order to determine if, or under what conditions, a serial
Bayes calculation is appropriate, a G study can analyze
sampled test results and quantify and describes the error
structure of a test. For example, in the context of medical
testing, sources of error might be attributable to the
laboratory, the clinician administering the test (e.g. psy-
chiatric diagnosis), the occasion on which the test was
administered, or some unobservable but consistent at-
tribute of the patient. Each of these error sources can
potentially lead to dependence between two tests results
performed on a single patient and hence can be a source
of dependent error leading to a violation of the inde-
pendence assumption on which a Bayes serial testing
model depends. To conduct a G study analysis, it is nec-
essary to first collect test outcomes for randomly sam-
pled administrations of the test. It is important to ran-
domly sample across variables which naturally vary in
the administration of a specific test and which might
contribute to error in the test results. Such studies allow
researchers to estimate each specified error source and
establish whether, or under what conditions, a serial
Bayes probability model appropriately updates patient
probabilities upon retesting.
2. Methods
To illustrate how a G theory-based analysis might im-
prove testing accuracy, let’s further develop our example
of the hypothetical medical test. Suppose that the medical
test in the example problem involves a laboratory analy-
sis of a specimen provided by a patient. Suppose further
that a team of expert medical researchers identify three
variables or potential sources of error over which the
collection of test results tend to vary and which might be
relevant to the outcome of the medical test in question.
The first identified potential source of error concerns the
occasion on which the patient is tested. Specifically, the
researchers suspect that short-term fluctuations in patient
values may lead to inconsistent test results. Hence, the
test outcome may depend on when the patient was tested.
For purposes of this illustration, we will designate this
type of error as “Error Type 1”. The second hypothesized
source of error (Error Type 2) relates to an unobservable
and temporally stable patient attribute that causes certain
patients to be consistently more or less likely to generate
false positive or false negative test results. The third
identified error source (Error Type 3) is related to labo-
ratory processing. In particular, the researchers hypothe-
sized that variation in laboratory procedure may contrib-
ute to the generation of false negative or false positive
test results.
In order to understand how these sources of error in-
fluence the test’s outcome, the researchers design an ex-
periment that samples test results from across the vari-
ables that tend to vary in the real world administration of
the test within the population and that are hypothesized
to contribute to error. The experiment draws a large ran-
dom sample of patients from the clinical population of
interest. Each patient in the sample is scheduled for mul-
tiple random appointment times at a clinic where speci-
mens are collected. After each clinic visit, the collected
specimen is divided into sub samples and sent for proc-
essing at multiple randomly selected laboratories. In G
study terminology, the experiment’s object of measure-
ment is patient (p), and the two study variables over
which sampling occurred, usually referred to as facets,
are occasion (o) and laboratory (l). For purposes of
analysis, the test’s outcomes are analyzed using analysis
of variance (ANOVA) with each cell containing the re-
sult of a single test outcome (i.e. either positive or nega-
tive, 0/1, or a continuous variable with or without a
threshold value). Equation (5) displays the G study model
for the decomposition of the total observed score vari-
ance σ2(Xpol) into seven variance components that are
estimated using ANOVA-based mean squares to derive
estimates of the quantitative effects that compose a single
test outcome (Xpol).
σ2(Xpol) = σ2(p) + σ2(o) + σ2(l) + σ2(po) + σ2(pl) + σ2(ol)
+ σ2(pol) (5)
The ANOVA-based research model is a fully crossed
person (p)-by-occasion (o)-by-laboratory (l) [p × o × l]
random model. However, unlike typical ANOVA appli-
cations which focus on F tests and follow-up significance
testing of certain mean effects within the model, G stud-
ies estimate variance components (VCs) for a single
outcome and quantify all sources of variance. (It should
Copyright © 2010 SciRes. PSYCH
Using Generalizability Theory to Evaluate the Applicability of a Serial Bayes Model in Estimating the 197
Positive Predictive Value of Multiple Psychological or Medical Tests
be noted that in some medical testing applications both
ANOVA-based significance testing and VC estimation
might prove useful.) This G study model estimates seven
VCs. There are three main effects: p, o and l, and four
interactions: po, pl, ol, and pol. It is useful here to con-
sider what each VC conveys about the test results. The
VCs can be verbally described as follows:
p – the degree to which test results tend to yield a con-
sistent outcome for patients across occasions and labora-
tories (may contain Error Type 2),
o – the degree to which certain sequential occasions
are more or less likely to detect a positive or negative
result (contributes to Error Type 1, but in this example it
should logically be zero),
l – the degree to which certain laboratories are more or
less likely to detect positive or negative results (contrib-
utes to Error Type 3),
po – the degree to which a patient’s test status tends to
change depending on the occasion on which the sample
was collected (contributes to Error Type 1),
pl – the degree to which a patient’s test status tends to
change depending on the particular lab to which the
specimen was sent (contributes to Error Type 3),
ol – the degree to which the probability of a positive
test result for a particular occasion tends to vary depend-
ing on the laboratory processing the specimen (this
should logically be zero),
pol – the degree to which the patient’s test status de-
pends on the occasion / laboratory combination and other
un-modeled or residual sources of error (also indicates
the degree to which the G study model fails to capture
relevant error sources).
3. Results
Interpreting the magnitude of the VCs from the G study
can determine whether, or under what conditions, the
assumption of test independence is satisfied and whether
enhanced prediction upon retesting is achieved. The total
variance in the G study model [σ2(Xpol)] is simply the
sum of the all the variance components. Suppose in our
example problem the test yields dichotomous data (nega-
tive/positive test results) and is summarized as the pro-
portion of positive test results (ρ). Therefore, model
variance is estimated as approximately: (ρ) * (1 – ρ); and
hence is equal to the proportion of positive tests observed
multiplied by the proportion of negative tests obtained
across all tests in the sample. Hence, the first result of
interest from the experimentally sampled data in our
example problem is the proportion of positive test results
observed within the sample. If the random sample is of
adequate size it should yield an accurate estimate of the
population proportion.
If, as in our example, there are established estimates of
disease prevalence, and test specificity and sensitivity the
researcher should examine the congruence between sam-
ple results and expected population values. Although a G
study can productively proceed if a sample disagrees
with established estimates of population prevalences and
the test’s specificity and sensitivity, for the purpose of
simplicity in illustrating our example problem, let’s as-
sume that the proportion of positive tests obtained from
our sample is in close agreement with the expected
population proportion. Hence, 31% of patients testing
positive in our sample reflect a patient’s true positive
disease status and 69% of the positive test results repre-
sent false positive results in a sample with a disease
prevalence of .01. The expected proportion of positive
results is .029 and the total model variance will sum
to .0279.
To further illustrate, Table 1 displays four hypotheti-
cal G study outcomes from the fully crossed G study
model. Each outcome has a different practical implica-
tion related to achieving test independence. Assuming
the sample is approximately consistent with expectations
estimated from the population, let’s focus on the VC out-
comes in Table 1. For Outcome 1, 30% of the variance is
found to be related to the patient and the largest source of
the error is attributable to a patient by occasion interac-
tion. Since the specificity, sensitivity and prevalence are
known, Equation (3) suggests that in the absence of pa-
tient Error Type 2, patient variance will account for ap-
proximately 30% of the total variance in the model. Out-
come 1 appears to agree closely with this expectation and
hence would suggest Error Type 2 does not make a major
contribution to measurement error. Further, since 60% of
the variance is related to the person-by-occasion interac-
tion (po), test results from a specimen collected on a sin-
gle occasion and submitted to multiple laboratories are
unlikely to provide independent information. The obvi-
ous recommendation resulting from Outcome 1 would be
that to maximize the information from retesting and to
insure that results exhibit test independence, specimens
should be collected on different occasions.
As another illustration, let’s suppose Outcome 2 as
reported in Table 1 was the G study result of our sam-
pling experiment. Here patient variance is significantly
higher than might be expected in the absence of Error
Type 2. With 60% of overall variance attributed to pa-
tient variance, this outcome dramatically exceeds what
one would expect in the absence of Error Type 2. In this
situation, the practical implication is that a Bayes serial
calculation would always be inappropriate even if speci-
mens were collect on multiple occasions and sent to mul-
tiple labs. This result suggests that some patients are
consistently more likely to generate false positive test
Outcome 3 in Table 1 displays a G study outcome
where most of the error is attributable to the patient-by-
Copyright © 2010 SciRes. PSYCH
Using Generalizability Theory to Evaluate the Applicability of a Serial Bayes Model in Estimating the
Positive Predictive Value of Multiple Psychological or Medical Tests
Copyright © 2010 SciRes. PSYCH
Table 1. Percent of total variance for seven variance com-
ponents for each of four hypothetical G study outcomes
VC Outcome
1 % % % %
p 0084 30 60 30 30
o .0000 0 0 0 0
l .0000 0 10 2 2
po .0167 60 2 2 2
pl .0003 1 10 50 6
ol .0006 2 8 0 0
pol .0020 7 10 16 60
TOT .0279 100 100 100 100
laboratory interaction (pl) (Error Type 3). To achieve
enhanced prediction/accuracy through the use of serial
testing, a single occasion would likely suffice as long as
the specimen was sent to multiple laboratories. For Out-
come 4, the three way interaction term (pol) explains
most of the variance and illustrates a possible failure to
specify and sample across relevant sources of error.
Since the three-way interaction contains un-modeled
error as well as the three-way interaction, this outcome
may indicate that the variables investigated are not re-
lated to observed variation in test results.
4. Discussion
Although the testing problem presented within this hy-
pothetical example focused on the interpretation of a
hypothetical diagnostic biomedical test, G theory meth-
odology coupled with Bayes serial estimations has much
broader application. For example, many concerned con-
stituents are currently attempting to assure the accurate
and fair use of tests in employment, sports eligibility, and
in making sanction decisions. In all of these contexts,
issues of fairness have arisen due to the large proportion
of false positive results and the high stakes nature of the
test results. There is considerable interest in increasing
the accuracy of test evidence for making important deci-
sion or a diagnosis. In addition, in many instances the
data for such analyses may already exist since medical
testing companies when seeking FDA approval for a par-
ticular test must submit the test to a series of trials.
It is obvious from governing board recommendations
and from published legal advice that test users are aware
that retesting might reduce error. However, recommen-
dations for retesting are usually made without statisti-
cally estimation of the utility of retesting. Suggestions
that samples be divided into multiple collection tubes, or
that the test be repeated implies an expectation of in-
creased precision with repeated testing. Unfortunately,
when the sources of error are not systematically esti-
mated, the usefulness of a particular retesting protocol is
currently unknown.
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