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230 E. Thorsteinsson and R. Brown
the effects of depression and fatigue on health. The latent variable could
then be used in the SEM to test the relationship between stress and sleep.
Finally, longitudinal (and cross-sectional) study designs can enable a
researcher to model multiple different symptoms and risk factors together
(e.g. multiple structural equation models). This can be especially useful
when the phenomenon under investigation, comorbidity, is most likely to
be described in terms of (potentially) circular causal pathways, including
the presence of bidirectional relationships and dysfunction in several bio-
logical systems. For example, in Chapter 3, we examined the premise that
a night-eating-related increase in body temperature can parsimoniously
explain the impaired sleep observed in people who eat late at night. As
detailed below, the modelling of multiple different symptoms/disorders
and risk factors together could be used to determine whether late-night
eating precedes a change in body temperature or vice versa, and it might
be used to test competing theories (e.g. impaired sleep was instead the
result of the lighting that was used to prepare the night-time meal). That
is, the models could test whether a light-induced change in melatonin
secretion is most likely to explain the impaired sleep or a change in body
temperature.
Standard time-series modelling techniques can be employed to examine
whether cause, X, correlates more strongly with an effect, Y,atalater
time than Y correlates with X following on from Y.Suchanapproach
would permit a determination of whether a change in body temperature
followed late-night eating or vice versa. If late-night eating preceded the
increase in body temperature more often than elevated body temperature
preceded late-night snacking, this would suggest that late-night eating (or
its causal antecedent/s) may have caused the elevated body temperature.
In this example, there is also the question of latency: if late-night snacking
affects body temperature how long does it take for the effect to take place
and how long and strong will the effect tend to be? Standard time-series
analytical techniques exist for ascertaining this lag and duration, and the
more fine-grained the time-series data, the more accurate the estimates of
lag and duration will be.
However, as mentioned in the above section, it can be difficult to estab-
lish the optimal time interval between the various assessment points, in a
longitudinal study design. For example, is 3-months sufficient follow-up
8 Design, Statistical and Methodological Considerations … 231
time to allow changes to occurred in one or more of the outcome vari-
ables? Furthermore, the time it takes for a substantial change to occur
in depression may be more (or less) than the time that is required for a
significant change to occur in sleep. In the case that the two sets of symp-
toms have a different temporal evolution, problems can potentially arise
in the study analyses. For example, if only a small change in sleep quality
occurs over 3 months, then there will be little variance that might be pre-
dicted by late-night eating. Similarly, in an RCT (experimental) study, it
is necessary to establish the optimal magnitude and duration of the ther-
apy/intervention, as well as the magnitude and duration of any expected
changes, to determine when it is best to observe the follow-up effects. For
example, in an RCT that seeks to minimise late-night eating, the optimal
components and duration of the therapy will need to be established, in
regard to pinpointing the optimal effects on sleep.
That said, this does not prove that causality exists, as it is always pos-
sible that a third variable, Z, is causally linked to both X and Y in such
a way as to produce the cross-lagged differences in correlations. Therefore,
it is still useful to employ ‘real life’ longitudinal field studies to iden-
tify potential causal relationships and then later test them in experimental
studies to establish causality. Furthermore, a predictor variable (e.g. night-
eating) may also affect other aspects of the data series being modelled (e.g.
sleep over time); for example, night-eating may alter melatonin secretion
and/or change body temperature. However, such an assessment may affect
the autocorrelation structure of the body temperature series thus, poten-
tially requiring a more sophisticated modelling and assessment approach.
Autocorrelation (or serial correlation) is effectively a type of Pearson’s cor-
relation coefficient that can be used to capture repeating patterns such as
the circadian rhythm of body temperature. That is, for example, the cir-
cadian rhythm of body temperature (or the sleep–wake cycle) may require
a more complex analytic approach, including the use of cosinor analysis,
in which an algorithm that describes the cycle is examined [15].
Finally, in regard to the assessment of body temperature, a variety of
covariates may need to be taken into account in the analyses, more so than
if modelling other less complicated concepts. Specifically, and at the least,
there is a need to consider controlling for various factors that are known to
alter body temperature such as: ambient temperature [16], alcohol intake
232 E. Thorsteinsson and R. Brown
[17], age [18], caloric restriction [19, 20], dr ug use (e.g. mood stabiliser)
[21, 22], caffeine use [23], and menopausal status [24].
8.3 Overlapping Risk and Protective Factors
As detailedabove, overlapping (or shared) symptoms can result in measure-
ment problems in studies that examine two or more comorbid disorders
or two or more concurrent symptoms. However, additionally, many of
the disorders and conditions mentioned in this book also share overlap-
ping risk (and protective) factors, which may further complicate the analytic
approach. Such factors may therefore need to be considered when design-
ing the study, as discussed in detail in Chapter 9.
As an example, using adaptive coping skills (e.g. seeking social sup-
port) may tend to buffer against the extent to which a stressor results
in anxiety; thus, it might be regarded as a protective factor for anxiety,
whereas maladaptive coping (e.g. escape-avoidance) tends to be regarded
as a risk factor for anxiety disorder development. Furthermore, seeking
out social support may also protect against the development of depres-
sion and sleep problems, possibly via a reduction in anxiety, but also by
other possible mechanisms. Thus, for example, a statistical model may
need to address the likely causal pathways between social support coping
and anxiety, depression and sleep, and also possible interactions between
them. Somewhat differently, as discussed in Chapter 7 , electronic device
use is a known risk factor for and likely precipitant of sleep problems
(e.g. insomnia), mood disorders (e.g. depressed mood), weight gain, and
overweight/obesity, although it is unclear whether similar (or different)
mechanisms are involved in each case and if the disorders are also causally
related to each other.
However, irrespective of the mechanism/s involved in each case, there
is a clear need to better integrate these overlapping risk factors together
in more complex real-world models of disease causation. Furthermore,
as some symptoms and disorders (e.g. anxiety) are also risk factors for
other disorders (e.g. depression), there is a need to concurrently model
the overlapping risk factors in regard to multiple related disorders. That
is to say, if a risk factor is only considered in regard to a single disorder,
8 Design, Statistical and Methodological Considerations … 233
then its real-world effects on overall health will be underestimated. In
contrast, the putative effects of protective factors (e.g. seeking out social
support) may in the same way be underestimated. Such a situation is likely
to have clinical implications: for example, some risk factors (e.g. night-
eating, impaired sleep) may be excluded or overlooked (in place of other
better known risk factors such as binge-eating) in an intervention to treat
overweight/obesity, despite the known relationship between night-eating,
binge-eating, impaired sleep, and obesity status and weight gain over time
[4, 25], note Chapter 3, Sect. 3.2. That is, night-eating and sleep hygiene
practices could be included to good effect in the intervention to treat
overweight/obesity, along with a focus on binge-eating, but this tends not
to occur in reality, due to the aforementioned tendency of researchers to
examine single disorders and limited risk factors.
In regard to this example, the state of affairs is likely due to the strong
focus on binge-eating behaviour in the clinical obesity literatures. How-
ever, more broadly, it is possible that a variety o f different risk and protec-
tive factors will tend to interact with each other, to augment or attenuate
any effects on different aspects of health. As detailed in Chapter 3,itis
already appreciated that binge-eating tends to coexist with night-eating,
impaired sleep, affective distress, and overweight/obesity, and each of the
risk factors, including the symptoms, is in turn known to be related to
each other. Thus, it is clearly time that researchers seek to model the extent
of the real-world complexity that exists between known risk factors, pro-
tective factors, and related symptoms and disorders.
As detailed in Chapter 9, most prior risk analysis studies have computed
the odds ratios (or hazard ratios) individually for each separate risk factor,
for each condition of interest; but they have rarely been computed for
highly linked clusters of risk factors in regard to multiple comorbid con-
ditions. Additionally, few prior longitudinal risk studies have evaluated a
large pool of potential risk factors to determine which of them provides
the most coherent explanation of the disorder/symptom. As a result, we
know little about the way in which different (or related) risk factors inter-
act with each other to contribute to two or more comorbid conditions.
Thus, it is clear that in the future, we need to conduct complex risk anal-
yses on clusters of linked risk factors and multiple conditions, rather than
on single risk factors and single conditions.
234 E. Thorsteinsson and R. Brown
However, it is unclear what is the most appropriate analytic approach to
use when a researcher wishes to test a complex real-world model, such as
the one described. Nonetheless, the interactions among the different risk
and protective factors and multiple outcomes are perhaps best incorpo-
rated into theoretical models that are tested using moderation type analyses
[10]andSEM [11, 26], and also, causal models could be captured using
mediation type analyses [10]andSEM [11, 26]. In all cases, a researcher
will first need to interpret the findings carefully and consider exactly how
they want to present the findings, including the reporting of odds ratio
(OR), adjusted odds ratio (AOR) when other overlapping variables (e.g.
confounding) are adjusted for, R
2
, or other indices.
8.4 Other Research and Data-Handling
Approaches
Several other relevant issuesare briefly described here, but not in anydetail.
First, given the recent advent of big data, many researchers can now access
large datasets that include multiple related measures that are repeated over
time, potentially including risk factor, symptom/disorder, and biological
process data. Given our current ‘embarrassment of riches’, it might be use-
ful to consider a ‘data mining’ approach to explore novel potential causal
pathways, or novel ways of viewing the linkages between the different risk
factors and outcome measures. Machine learning algorithms or artificial
intelligence approaches could potentially be used to explore the data in
this way.
Such an approach will certainly be facilitated by the extensive access
we researchers currently have to open-access datasets. Aside from using
the datasets to conduct systematic analyses (e.g. meta-analysis), they
could alternately be used to test a particular complex real-world model
(of interacting risk factors and symptoms/disorders) in multiple datasets
or in computer simulations of the data. However, as detailed in Chapter 2,
few prior studies have sought to use such an analytic approach.
Second, in regard to experimental trials (e.g. clinical RCTs), in most
jurisdictions, it is first necessary to preregister the trial (e.g. Australian
8 Design, Statistical and Methodological Considerations … 235
New Zealand Clinical Trials Registr y; http://www.anzctr.org.au/). Sim-
ilarly, CONSORT (Consolidated Standards of Reporting Trials, http://
www.consort-statement.org/) providesa checklist and a flow diagram tem-
plate for reporting on the progress of randomised controlled trials. The
checklist focuses on reporting the way in which the trial is designed, anal-
ysed, and interpreted. The registration process is required to ensure (and
regulate) the quality of the research trial, minimise any harm to the par-
ticipants involved in the trial, and provide oversight of possible ethical
implications of the trial. Such a high degree of oversight of clinical RCTs
has certainly led to an improvement in the quality of the research and it
has ensured that any relevant patient considerations and ethical issues are
properly addressed at the beginning.
Unfortunately, for the most part, the same frameworks do not yet exist
to assist researchers (and their participants) in optimising the design of
non-experimental studies and the analysis and reporting of the study
results. Nonetheless, a recently developed website AsPredicted (https://
aspredicted.org/) enables researchers to preregister their hypotheses, thus
preventing them from changing the hypotheses to fit the particular study’s
findings.
Similarly, some journals are now encouraging researchers to submit
results-free research articles (e.g. BMC Psychology) as part of a pilot pro-
gramme, withthe expectationthat the results will bereported downstream.
Such a submission process means that the journal editors and reviewers
can evaluate the manuscript, blinded t o the study results, at least in the
initial stages of the review process. It is possible that this type of sub-
mission process may encourage journal reviewers to focus more on the
quality of the research, rather than on the study results. Potentially, they
could also offer editorial advice about the study design, if the feedback was
provided in a timely manner. However, more broadly, such an approach
has obvious merits in terms of providing better oversight of the methods
that are used in research studies; and potentially, it could even maximise
the research study’s potential. For example, a journal editor might sug-
gest to a researcher that they include an additional explanatory variable
in the study or choose a standardised measure of a construct, instead of
(or in addition to) a novel measure, so that the results can be more easily
compared to the results of other studies. Finally, de-identified datasets are
236 E. Thorsteinsson and R. Brown
increasingly beingshared by researchersand research students in the public
arena via websites such as figshare (https://figshare.com/). However, such
an approach has yet to be standardised.
Thus, in truth, there is a long way to go before we can fully understand
the phenomenon of disease comorbidity, because at present, we have yet
to put in place optimal frameworks and processes that might facilitate a
fuller understanding of the phenomenon. As detailed in this chapter, such
an understanding will only be achieved if we can better link the work
of different researchers together in a more meaningful way, in particular,
by improving access to more datasets that have been obtained from well-
conducted studies.
8.5 Summary
As detailed in this chapter, sophisticated methodological and statistical
considerations (andapproaches) are required in the examination of comor-
bidity between different disorders, and concurrence between different
symptoms, especially if examining the utility of complex causal models.
With this in mind, researchers may need to make difficult decisions about
the best way to deal with overlapping items on different symptom scales,
which were designed without reference to different-but-related constructs
(e.g. depression and fatigue). Further, this decision-making will need to be
concurrent with any decision-making about preferred statistical approach
as thewrong study methodology decision couldpreclude orlimit the statis-
tical approaches that might be available to the researchers. Furthermore, it
is likely that the different mechanisms underpinning the comorbidity will
require different methodological and/or statistical approaches. Of course,
this decision-making will become even more complex if temporal (e.g.
follow-up interval) or o ther considerations (e.g. duration of effect of the
risk factor, interaction between risk factors) need to be addressed.
Thus, practically speaking, it is difficult to optimally assess the potential
linkages that likely exist between different comorbid symptoms/disorders
and their risk factors, especially if one has to take into consideration all
of the aforementioned methodological and statistical issues. In particular,
it is difficult to optimally measure and evaluate the linkages that likely
8 Design, Statistical and Methodological Considerations … 237
exist between the different phenomena, especially if one or more of them
are causally linked to each other, as is asserted in this book, and also by
Borsboom and colleagues [1]. Thus, aside from utilising (or developing)
protocols forthe removalof overlap between the measuresof different-but-
related constructs, it might also be necessary to use an alternate statistical
modelling approach, to optimise the data analysis process.
References
1. Borsboom D, Cramer AOJ, SchmittmannVD, EpskampS, Waldorp LJ.The
small world of psychopathology. PLoS One. 2011;6(11):e27407. https://doi.
org/10.1371/journal.pone.0027407.
2. American Psychiatric Association. Diagnostic and statistical manual of mental
disorders (DSM-5). 5th ed. Washington, DC: American Psychiatric Associa-
tion; 2013.
3. Mayo Clinic. Chronic fatigue syndrome. 2018; https://www.mayoclinic.
org/diseases-conditions/chronic-fatigue-syndrome/symptoms-causes/syc-
20360490. Accessed 31 May 2019.
4. Centre for Disease Control and Prevention [CDC]. Chronic fatigue syn-
drome. 2012; http://www.cdc.gov/cfs/. Accessed 31 May 2019.
5. Demyttenaere K, De Fruyt J, Stahl SM. The many faces of fatigue in major
depressive disorder. The International Journal of Neuropsychopharmacology.
2005;8(1):93–105. https://doi.org/10.1017/s1461145704004729.
6. Aikens JE, Reinecke MA, Pliskin NH, et al. Assessing depressive symptoms
in multiple sclerosis: Is it necessary to omit items from the original Beck
Depression Inventory? Journal of BehavioralMedicine. 1999;22(2):127–142.
https://doi.org/10.1023/a:1018731415172.
7. Sveinbjornsdottir S, Thorsteinsson EB. Psychometric properties of the Mea-
sure of Adolescent Coping Strategies (MACS). Psychology. 2014;5(2):142–
147. https://doi.org/10.4236/psych.2014.52022.
8. Sveinbjornsdottir S, Thorsteinsson EB, Lingam GI. Model fit and compar-
isons for the Measure of Adolescent Coping Strategies (MACS): Fiji, Iceland,
and Australia. Journal ofPacific Rim Psychology. 2017;11:e12. https://doi.org/
10.1017/prp.2017.20.
238 E. Thorsteinsson and R. Brown
9. Little TD, Schnabel KU, Jurgen Baumert J, eds. Modeling longitudinal and
multilevel data: Practical issues, applied approaches, and specific examples. Lon-
don, UK: Lawrence Erlbaum Associates; 2000.
10. Hayes AF. Introductionto mediation, moderation andconditional process analy-
sis: A regression-based approach. 2nd ed. New York, NY: Guilford Press; 2018.
11. Kline RB. Principles and practice of structural equation modeling.3rded.New
York, NY: Guilford Press; 2011.
12. Schmittmann VD, Cramer AOJ, Waldorp LJ, Epskamp S, Kievit RA, Bors-
boom D. Deconstructing the construct: A network perspective on psycho-
logical phenomena. New Ideas in Psychology. 2013;31(1):43–53. https://doi.
org/10.1016/j.newideapsych.2011.02.007.
13. Shahar B, Britton WB, Sbarra DA, Figueredo AJ, Bootzin RR. Mechanisms
of change in mindfulness-based cognitive therapy for depression: Prelimi-
nary evidence from a randomized controlled trial. International Journal of
Cognitive Therapy. 2010;3(4):402–418. https://doi.org/10.1521/ijct.2010.
3.4.402.
14. Gu J, Strauss C, Bond R, Cavanagh K. How do mindfulness-based cognitive
therapy and mindfulness-based stress reduction improve mental health and
wellbeing? A systematic review and meta-analysis of mediation studies. Clin-
ical Psychology Review. 2015;37:1–12. https://doi.org/10.1016/j.cpr.2015.
01.006.
15. Kelly G. Body temperature variability (Part 1): A review of the history of
body temperature and its variability due to site selection, biological rhythms,
fitness, and aging. Alternative Medicine Review. 2006;11(4):278–293.
16. Lu S-H, Dai Y-T. Normal body temperature and the effects of age, sex,
ambient temperature and body mass index on normal oral temperature:
A prospective, comparative study. International Journal of Nursing Studies.
2009;46(5):661–668. https://doi.org/10.1016/j.ijnurstu.2008.11.006.
17. Danel T, Libersa C, Touitou Y. The effect of alcohol consumption on the
circadian control of human core body temperature is time dependent. Amer-
ican Journal of Physiology-Regulatory, Integrative and Comparative Physiology.
2001;281(1):R52–R55.
18. Roth GS, Lane MA, Ingram DK, et al. Biomarkers of caloric restriction may
predict longevity in humans. Science. 2002;297(5582):811. https://doi.org/
10.1126/science.1071851.
19. Redman LM, Martin CK, Williamson DA, Ravussin E. Effect of caloric
restriction in non-obese humans on physiological, psychological and behav-
ioral outcomes. Physiology & Behavior. 2008;94(5):643–648. https://doi.
org/10.1016/j.physbeh.2008.04.017.
8 Design, Statistical and Methodological Considerations … 239
20. Soare A, Cangemi R, Omodei D, Holloszy JO, Fontana L. Long-term calorie
restriction, but not endurance exercise, lowers core body temperature in
humans. Aging. 2011;3(4):374–379.
21. Kwok JSS, Chan TYK. Recurrent heat-related illnesses during antipsychotic
treatment. Annals of Pharmacotherapy. 2005;39(11):1940–1942. https://doi.
org/10.1345/aph.1g130.
22. Mellerup ET, Widding A, Wildschiødtz G, Rafaelsen OJ. Lithium effect
on temperature rhythm in psychiatric patients. Acta Pharmacologica et Toxi-
cologica. 1978;42(2):125–129. https://doi.org/10.1111/j.1600-0773.1978.
tb02179.x.
23. Goldstein ER, Ziegenfuss T, Kalman D, et al. International society of sports
nutrition position stand: Caffeine and performance. Journal of the Interna-
tional Society of Sports Nutrition. 2010;7(5). https://doi.org/10.1186/1550-
2783-7-5.
24. Molnar GW. Body temperatures during menopausal hot flashes. Journal of
Applied Physiology. 1975;38(3):499–503.
25. Carmelli D,Swan GE, BliwiseDL. Relationship of30-year changes inobesity
to sleep-disordered breathing in the western collaborative group study. Obe-
sity Research. 2000;8(9):632–637. https://doi.org/10.1038/oby.2000.81.
26. Byrne BM. Structural equation modelling with AMOS: Basic concepts, appli-
cations, and programming. 2nd ed. New York, NY: Routledge; 2010.