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8
Design, Statistical and Methodological
Considerations: Comorbidity
Einar Thorsteinsson and Rhonda Brown
8.1 Methodological Approaches
In this section, several methodological issues related to the measurement
of disorders and symptoms will be discussed in detail. In the scientific lit-
erature, disease comorbidity is typically evidenced by high co-prevalence
estimates between the different diagnoses, whereas symptom concurrence
is evidenced by moderate to high correlations between two or more com-
posite scoremeasures (e.g. total constructscores), usingvalidated question-
naires [1]. Study methodology issues related to the comorbidity between
different disorders will be dealt with first and then symptom concurrence
will be examined. To illustrate the extent of overlap between disorders, we
will briefly revisit the diagnostic criteria for generalised anxiety disorder
E. Thorsteinsson (
B
)
University of New England, Armidale, NSW, Australia
e-mail: ethorste@une.edu.au
R. Brown
Australian National University, Canberra, ACT, Australia
e-mail: rhonda.brown@anu.edu.au
© The Author(s) 2020
R. Brown and E. Thorsteinsson (eds.), Comorbidity,
https://doi.org/10.1007/978-3-030-32545-9_8
221
222 E. Thorsteinsson and R. Brown
(GAD), major depressive disorder (MDD), insomnia disorder (ID), and
chronic fatigue syndrome (CFS). A GAD diagnosis is based on excessive
anxieties related to various life issues, which lasts more than 6 months, and
with symptoms that include feeling on edge, irritability, difficulty concen-
trating, tiredness/fatigue and sleep problems [2]. MDD is characterised
by a loss of interest/pleasure in all activities lasting for at least 2 weeks,
which is associated with five or more other symptoms including somatic
symptoms (e.g. changes in appetite, sleep, fatigue), neuropsychological
changes (e.g. difficulty thinking, concentrating, making decisions), and
affective/cognitive changes (e.g. feelings of worthlessness/guilt, suicidal
ideation) [2]. ID can be diagnosed in people who experience prolonged
impairment of sleep quality, including difficultly falling asleep and/orstay-
ing asleep, and feelings of fatigue, irritability, and tiredness [2]. In con-
trast, CFS is diagnosed on the basis of chronic fatigue that is associated
with impaired sleep, muscle or joint pain, enlarged lymph nodes, and/or
impaired concentration [3, 4].Thus, it is evident that there is considerable
overlap between the different disorders, including the occurrence of sleep
problems, fatigue, and impaired concentration.
To separately illustrate the range of methodological issues pertaining to
the measurement of overlapping symptoms, the discussion will be limited
to phenomenological and statistical overlap between anxiety, depression,
fatigue, and highly concurrent symptoms. As detailed above, anxiety and
depressive disorders are especially highly comorbid and they also share a
number of symptoms in common, including impaired sleep, fatigue, and
gastrointestinal (GIT; e.g. change in appetite) symptoms; and some symp-
toms of fatiguing illness also overlap with anxiety and depression symp-
toms [2]. As discussed in the next paragraph, in some cases, there are clear
published protocols for the removal of symptom overlap when assessing
the symptoms together. For example, protocols that outline which somatic
items (e.g. sleep changes) should be removed from the depression measure
to reduce the extent of artefactual overlap between depression and fatigue,
which occurs as a result of double counting the symptoms as belonging
to both depression and fatigue. Such an approach will tend to reduce the
extent of the over-inflation of the observed relationship between depres-
sion and fatigue, which will permit a clearer understanding of the nature
of the relationship, as discussed below.
8 Design, Statistical and Methodological Considerations … 223
8.1.1 Removal of Overlapping Scale Items
A correlation between two concepts can be inflated if the two concepts are
measured using scales that contain similar (i.e. shared) items, including
shared items on depressionand fatiguescales. For example,the Depression,
Anxiety, Stress Scales (DASS) contains a depression item ‘I was unable
to become enthusiastic about anything’ which is similar to the Fatigue
Severity Scale (FSS) item ‘My motivation is lower when I am fatigued’.
Thus, if the DASS and FSS are used in the same study, at the least, it might
be necessary to examine the inter-scale item correlations between the scale
scores. Additionally, to reduce the risk of inflated correlations between the
related concepts, the shared items could be removed from each (or one)
scale to generate new scales, with new total scores, to reduce the extent of
potential over-inflation of the correlation coefficients for the total scores.
Similarly, it is apparent that when sleep problems, depression sympto-
mology, and fatigue are examined together in the same study, the strength
of the association between the measures could be overestimated [5]. As
mentioned above, in some instances, there are clear protocols for the
removal of shared items; for example, the Beck Depression Inventory-II
[6] has a protocol for removing somatic depression symptoms so that they
are not counted twice in studies examining depression and co-occurring
fatigue and impaired sleep. That is to say, researchers can calculate a mea-
sure of ‘cognitive’ depression minus the somatic symptoms of depressed
mood, which can then be related to somatic symptoms such as fatigue.
Such accommodations are especially important when examining mental
health symptoms in patients with chronic illness (e.g. multiple sclerosis).
However, as detailed below, few other published protocols exist for the
removal of the shared symptoms of the disorders (e.g. impaired sleep and
anxiety).
Similarly, maladaptive coping and behavioural measures can tend to
assess different elements of the same behaviour, potentially resulting in
an overinflated correlation between them. As an example, one adolescent
coping scale called the Measure of Adolescent Coping Strategies [7, 8]
includes a maladaptive sub-scale (acting out) which assesses drug use. It
asks adolescents to rate each statement using 4-point scales from 1 (Idid
not use )to4(I used almost all the time) in regard to statements such as:
224 E. Thorsteinsson and R. Brown
‘I tried to make myself feel better by drinking alcohol, smoking, u sing
drugs or medications’ [7, 8]. Thus, if a researcher examines ‘acting out’ in
regard to behavioural drug use, the association between it and drug use
will likely be inflated. However, in this case, there is no clear protocol for
the removal of the overlapping items. As a consequence, a researcher will
need to examine the statistical effects of theoverlap and potentially control
for it, or alternately make other accommodations (e.g. item removal), if
necessary.
Thus, there are scale-based methods that can assist researchers in reduc-
ing the extent of artefactual overlap between different ‘symptom groups’,
but only for some symptoms/disorders (e.g. depression and fatigue).
Therefore, there is a clear need to develop other evidence-based proto-
cols for the removal of overlap between symptoms (e.g. impaired sleep
and fatigue) that are commonly assessed together. However, alternately,
there could be a theoretical reason not to make adjustments for the extent
of the overlap, as any adjustment runs the risk of affecting the reliabil-
ity and validity of the scales being used and potentially underestimates
the extent of the relationship, which may have implications for examin-
ing possible common causal pathways. For example, if impaired sleep is
causally linked to fatigue via tiredness, then removing the overlap between
the measurement of sleep and fatigue, may obscure the assessment of the
putative causal relationship. However, at the least, there is a need to more
clearly operationalise the different disorders and their symptoms, espe-
cially in regard to their shared symptoms. If the reader wants to read more
about some of these approaches in greater detail, there is a plethora of
appropriate available books on the topic [9–11].
In the case that a published (item removal) protocol is not available, it
might be tempting for a researcher to remove several overlapping items,
especially inthe case that there area large number of questionnairesadmin-
istered to the participants. For example, it might be tempting to remove
items from an anxiety scale if they overlap with items in the fatigue scale.
However, this may be problematic as it will be impossible to re-include the
items later in the study, which may result in the inability to: (a) compare
the fatigue and depression/anxiety scores to results from other studies; (b)
assess the validity and reliability of the scales; and (c) merge your dataset
with another dataset. After all, if you make sure to include all the items
8 Design, Statistical and Methodological Considerations … 225
to begin with, you can always remove them from your calculations and
analyses at any time, but you cannot add the items back.
An additional issue in presenting similarly worded items in different
questionnaires is that the participants may easily tire of the repetition.
Thus, a short statement at the start of the questionnaire might need to
explain to them that there is a degree of repetition in the questions, but
to answer all of the items to the best of their ability. As a side note, if a
researcher is presenting an online questionnaire using a good ‘question-
naire delivery system’, then it may be useful to consider randomising the
order of scales. However, that said, to reduce the risk of the participants
dropping out of the study, it may be best to place the distressing items
towards the end of the questionnaire battery. Furthermore, researchers
may like to put key measures (e.g. dependent variables) in the early part
of the questionnaire, in case the participants drop out halfway through
thus, leaving the researchers with some useful information.
Thus, a strongargument could be mounted to present the full version of
the original scales (i.e. with all items included) to study participants, which
would enable the researcher to examine the scale scores (e.g. depression)
with and without the different items removed (i.e. raw vs. raw-items-
removed). The inclusion of the full-scale measures should prevent any
post-sampling regrets,for example, if the researcher wished to calculate the
total depression score to compare with the normed values for depression
on that scale, which would be impossible if all the scale items had not
been included.
Finally, an alternate approach could be used to examine the relationship
between different ‘symptom groups’ (e.g. anxiety, depression) [1]. Specif-
ically, as each symptom group is comprised of a subset of symptoms, each
could be conceptualised as separate but related sub-entities [1]. Such an
assertion was made by Schmittmann and colleagues [12], who have con-
ceptualised psychological attributes (e.g. anxiety) as ‘networks of directly
related observables’. That is, a single construct (e.g. anxiety) could be con-
ceptualised as a network of related variables that includes somatic arousal,
cognitive arousal, and fear cognitions, which overlap with other constructs
(e.g. depression) to some degree. That is to say, rather than being a single
cohesive symptom group, anxiety might be better conceptualised as a clin-
ical phenomenon that is comprised of somatic arousal, cognitive arousal,
226 E. Thorsteinsson and R. Brown
and fear cognitions, whereas depression could be comprised of negative
affect, negativecognitions, andgeneralised (e.g. fatigue) and organ-specific
(e.g. GIT) somatic symptoms.
Structural equation modelling might be an especially useful approach
in testing such a complex model, comprised of different constructs, that
each has separate (but overlapping) subsets of symptoms. For example,
a dependent variable (anxiety) could be comprised of a latent variable
that was comprised of somatic arousal, cognitive arousal, and fear cog-
nitions. Such an analytic approach has obvious utility—it could permit
the modelling of different risk factors in regard to the separate aspects
of each construct, with the expectation that there will be different risk
factor–outcome relationships for different aspects of the construct. For
example, ruminative thinking may more strongly predict anxiety-related
fear cognitions than anxiety-related somatic arousal.
However, to the author’s knowledge, no empirical studies have used
such an analytic approach to model potential causal relationships between
variables. Nevertheless, in recent years, the potentially separable aspects
of the anxiety construct have been assessed in regard to anxiety disorder
causation; for example, anxiety-sensitivity (i.e. fear of anxiety symptoms)
and intolerance of uncertainty have been examined as separate (but over-
lapping) cognitive entities that are integral to (but separable from) state
anxiety and the presence of anxiety disorders.
8.1.2 Longitudinal and Experimental Studies
In this book, we have sought to present, where possible, meta-analytic
results of prospective longitudinal studies, or if not, results from large
prospective longitudinal studies. That is, we have provided evidence
obtained from the pooled results derived from multiple studies (i.e. meta-
analysis), or at the least, large studies that have examined the temporal
relationship (over time) between the risk factors and later presence of the
symptoms/disorders. As stated in Chapter 1, our current conception of
causality typically requires that the cause of an event must precede its onset
in time; thus, only longitudinal (and experimental) study results can fulfil
that criterion, to a greater or lesser degree.
8 Design, Statistical and Methodological Considerations … 227
At the least, it is necessary to look beyond correlational (cross-sectional)
study results in an attempt to disentangle possible causal pathways that
underpin the development of the disorders and symptoms. A longitudinal
study design is also ideal for examining models that incorporate statistical
mediation [10] and moderation analyses, which enables a researcher to use
independent variables measured at Time 1 to predict mediators measured
at Time 2 that, in turn, predict the dependent variable/s at Time 3. If
the dependent variables are measured at Times 1–3, they can be used as
covariates in the analyses; that is, their effects can be statistically controlled.
Such an analysis would greatly assist a researcher in testing the potential
mechanism/s that underpins a disorder or symptom, and understanding
how the relationship evolves over time. As detailed in the next section,
the approach would facilitate the testing of individual risk factors (or
composite risk factors) as potential mechanisms over time, and also the
likely indirect linkages between them and the symptoms (or disorders)
over time.
Nonetheless, some of the drawbacks of longitudinal studies include that
the dropoutrate istypically high,especially foronline survey-based studies,
especially in the case that sufficient time is required to pass to enable a
significant change in the variables of interest. Without a significant change
in the dependent variable over time, there will be insufficient change-
related variance that can be properly examined and predicted in the risk
factor analyses. However, in contrast, this issue needs to be reconciled with
the likely temporal effect of the risk factor, for example, whether it is short
acting or is prolonged in its effects. This and other statistical approaches
will be discussed in greater detail below.
Experimental studies (e.g. randomised controlled trials; RCT) are
required to properly establish causation. However, longitudinal and cor-
relational studies do contribute to the scientific literature. In the first
instance, they can be used to map out the extent of a relationship between
two variables, as a prelude to conducting an experimental study. Addi-
tionally, study designs including longitudinal field studies which attempt
to capture data that is reflective of ‘real life’ (rather than which is captured
in an experimental lab environment) are also required, to interrogate the
likely ‘causal pathways’ that have been established in experimental studies.
For example, mindfulness-based therapies (e.g. mindfulness-meditation)
228 E. Thorsteinsson and R. Brown
have consistently been shown to improve mood in people with anxiety
and/or depression symptoms. However, given the complexity of the ther-
apy (e.g. various exercises tap awareness of bodily sensations, emotions
and thinking), it has been difficult to pinpoint the exact mechanism/s
that underpins the improvement.
For example, some researchers suggest that the clinical improvement
seen in clients on the therapy is due to their greater focus and reflection
upon their ruminative thinking [13], whereas others suggest it is due to a
greater focus on peripheral bodily sensations (e.g. in the case of progres-
sive musclerelaxation), and various other mechanismshave been suggested
[14].Thus, mindfulness-meditation has obvious utility in improving men-
tal health in at least some individuals, but it is unclearexactly how it works.
In this case, a longitudinal study, which compares a number of competing
potential mechanisms, would have obvious utility in helping to pinpoint
the most likely mechanisms underpinning the observed improvement as
a result of the therapy.
8.2 Statistical Approaches
The most important aspect of any statistical analysis is to be clear and
transparent in documenting the statistical approach that has been taken.
Thus, in a dataset care must be taken to document any manipulation
of the variables (e.g. data transformation) in the syntax, variable labels,
and value labels. Furthermore, when writing up the findings, researchers
should make sure to clearly explain the statistical approach that was used
in the method section of the paper, and if possible, provide open access
to the dataset, so that readers can run different analyses using a different
approach for comparison.
Statistically, in analysing the relationship between two or more symp-
toms/states, it is typical to use one or more of the following analyses,
including Pearson’s correlation coefficients (or Spearman’s rho); partial
correlations; multiple regression analyses, including hierarchical multiple
regression analyses; and structural equation modelling or path analysis.
8 Design, Statistical and Methodological Considerations … 229
In regression analysis, residual scores capture the difference between the
sample’s mean or best fitting regression line, as an example, and the indi-
viduals’ scores, thus capturing what has been left ‘unexplained’ by the
regression line. These residual scores can be saved and used in further
statistical analysis. For example, a study of depression and fatigue could
run a regression analysis with depression as the predictor and fatigue as the
outcome variable; and saving the residual score for fatigue would permit
the researchers to create a fatigue variable, in which the variance explained
by depression has been partialled out (removed). However, it could be
argued that fatigue without any overlapping depression symptoms is not
really fatigue at all, but a sub-component of fatigue. Similarly, as dis-
cussed above, partialling out the effects of fatigue on depression can give
rise to a pure measure of ‘cognitive depression’, but this is only a sub-
component of depressed mood. As a result, any analyses conducted on
the sub-components of depression and fatigue may not be replicated in
future studies, which use the full versions of the depression and fatigue
scales. Thus, it may be impossible to compare the study results with find-
ings from previous studies that have used the complete scales. Similarly,
a researcher might employ hierarchical regression analysis or partial cor-
relations to enable the researcher to control for one variable and to get
the correlation between the remaining variables. Thus, a researcher might
be interested in the correlation between depression and anxiety, whilst
controlling for fatigue, and this will work similarly to use residuals in
regression analysis.
However, instead of removing (or controlling for) the shared somatic
symptoms ofdepression and fatigue, an alternate statistical approach could
be to combine the depression and fatigue scores together into a latent
variable, which, for example, might be used to test a model examining the
effects of stressful life events on sleep quality. That is to say, if depression
and fatigue were somewhat peripheral to the main research question (i.e.
does stress predict impaired sleep?), then their scores could be combined
and used as a covariate in the analysis. For example, the researcher may
want to combine depression andfatigue using structural equation modelling
(SEM) to create a latent variable that combines the two measures into a
single latent construct (comprised of depression and fatigue) that captures