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Posttraumatic Stress Disorder (PTSD) is an anxiety disorder that
can develop after exposure to a terrifying event or series of events, in which
physical harm occurred or was threatened, causing feeling of intense fear or
helplessness (Collie et al., 2006). PTSD is characterized by three groups of
symptoms: intrusive re-experiencing, avoidance of reminders and triggers, and
hyperarousal (American Psychiatric Association, 2013). It is typical for people
with PTSD to repeatedly re-experience the terrifying event or events in the
form of flashback episodes, nightmares, memories, or frightening thoughts
(Collie et al., 2006).

Approximately 30% of individuals who have been in war zones
develop PTSD, however it is thought to be under-reported in veterans to avoid
stigmatization (Hoge et al., 2004). With new generations of veterans returning
home, there is an obligation to improve care for PTSD. A promising treatment
option is art therapy and although it has received little attention, art therapists
have reported remarkable results from work with combat veterans (Campbell et al.,
2016).
It has been recognised that randomized controlled trials (RCT) are a high
priority for the art therapy field, in order to test the effectiveness of art therapy
on combat-related PTSD (Kaiser & Deaver, 2013).

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The RCT is often classed as the
most scientifically rigorous method of hypothesis testing available and is
regarded as the gold standard trial for evaluating the effectiveness of
interventions (McGovern, 2001). RCTs involve the random assignment of participants into one of two or more clinical interventions (Bondemark
& Ruf, 2015). Its main purpose is to prevent bias by distributing the
characteristics of patients randomly between the groups, so that any difference
in outcome can be explained by the treatment only (Roberts & Torgesson, 1998).

Whilst RCTs allows investigators to control many types of bias, it
does not mean that that bias is completely eliminated. Thus adequate knowledge
of the different types of bias that may distort RCT results and how to avoid
them is vital for researchers striving to conduct research of high validity
(Abdelhamid, 2005). In order to design an RCT to test the effectiveness of a
newly developed art therapy for combat-related PTSD, it is important to
understand what bias is, the different types of biases and how to reduce them.

Bias can be simply defined as ‘the deviation from the truth”
(Abdelhamid, 2005), or in scientific terms “any factor or process that
tends to deviate the results or conclusions of a trial systematically away from
the truth” (Sackett, 1979). Bias can occur during any part of a study, mainly
due to inadequate design, misconduct of the research methodology or the
inadequate analysis of data (Abdelhamid, 2005).

Bias can be
introduced during the initial planning phase of a RCT. Design bias can involve
collecting extensive preliminary data to design studies with a high likelihood
of being positive (Fries & Krishnan, 2004). Similarly, hidden agenda
bias occurs when a trial is mounted, not in order to answer a question, but to
demonstrate a pre-required answer (Alejandro et al., 2007). Alejandro et al., (2007) suggested there is an unspoken converse of
‘don’t do a trial if it won’t show you what you want to find’. To try and combat these types of biases in
this RCT, it is important to design the trial based on the concept of
equipoise. Clinical equipoise is the assumption
that there is not one ‘better’ intervention present during the design of a RCT
(Cook & Sheets, 2011). Clinical equipoise provides the principled basis for
RCTs and is considered a necessary feature to ethically enrol patients into
clinical trials (Troug, 1999). It is somewhat naïve to assume that all RCTs are
investigated in a state of equipoise, with some arguing that an environment in
a complete state of equipoise cannot exist (Sackett, 2000). However, this assumption has been recognised
as the central ethical principle for human experimentation and a lack of
equipoise can lead to bias (Cook & Sheets, 2011). The design of this study will be founded on this assumption and
designed on the basis of a strong clinical and personal equipoise.

This RCT will be
conducted to test the effectiveness of a newly developed art therapy for
combat-related PTSD. It will be an Individually
RCT with a parallel design. Participants suffering with combat-related PTSD
will be randomised into either the experimental group; the newly developed art
therapy or the ‘treatment as usual’ control group; cognitive behavioural
therapy (CBT). The null hypothesis for this study
will assume that there will be no statistical difference between these two
groups, there will be no assumption that one intervention is more effective
than the other. CBT will be used as the control group because it is regarded as a first-line approach for PTSD by a range of
authoritative sources (Forbes et al, 2007)
and considered to currently have the strongest evidence for reducing the
symptoms (Sloan et al., 2015).

Choosing the right population when designing this study is crucial
because several biases can be introduced at this stage (Kendall, 2003). A
limited sample can cause biases such as gender, age and severity of illness,
which will undermine the generalisability of the study (Bowling, 1997). The
inclusion and exclusion criteria should be appropriate to the research
hypothesis, but also realistic (Kendall, 2003). Therefore, this study will have
no restrictions on age, gender, era of combat experience, or branch of service.
Whilst the majority of veterans are male (Campbell et al., 2016), this study
also will look to include female participants. Ideally the sample recruited
will representative the veteran population suffering with combat-related PTDS,
however, realistically this may not be possible. Any sample limitations will be
outlined in the final report and suggestions will be made for further research.

The participant’s severity of PTSD symptoms will be measured using
the PTSD Checklist–Military Version (PCL-M) questionnaire. Prior research has excluded
individuals with a PCL-M score lower than 50 (Weathers et al, 1993). However,
other subsequent studies with different populations have suggested that lower
cut off score of 30 more accurately identifies individuals with PTSD
respectively (Dobie, et al., 2002; Yeager et al., 2007). Therefore, this
study’s inclusion criteria will accept individuals with a PCL-M score of 30 or
higher, this will increase the study’s generalisability to those suffering with
less severe symptoms.

Another important factor to consider
when designing the inclusion and exclusion criteria is the rate of comorbidity
among those with PTSD. Many veterans present with comorbid medical conditions
(David et al., 2004), comorbid Axis I or II disorders (Rosen
et al., 2004) and other characteristics that may complicate treatment.
Clinically, it is appropriate to carefully consider the potential impact of
each treatment method for individuals with co-occurring diagnoses (Van Minnen et al., 2002). There is large overlap between substance use disorders and PTSD (Stirmanm,
2008), however prior studies comely exclude individuals with substance use
disorders because of its interference with exposure-based treatments (Van
Minnen et al., 2002). This presents a serious limitation in the existing
research, it is important to develop treatments that can address clinical issues
that are common in this population. In recent years, more researcher have begun
to conduct such studies (Stirmanm, 2008) and this RCT will follow their lead.
Individuals who have a co-occurring diagnoses of substance use disorder will be
invited to participate in this study.

Selection bias is considered the main type of bias in clinical
trials (Berger & Bears, 2003), therefore it is important to address the
ways in which RCTs can be designed to reduce this bias. Selection bias can
arise during the process of dividing patients into groups, it occurs when
baseline characteristics are unevenly distributed between the experimental
group and the control group (Gluud, 2006). This discrepancy is caused by interferences
from researchers or recruiters selectively
enrolling patients into the trial based on what the next treatment allocation
is likely to be (Kahan et al., 2015).

Selection Bias can be minimised by implementing methods such as simple
randomisation (Abdelhamid, 2005). This technique
maintains complete randomness of the assignment of a subject to a particular
group, the most common and basic example is flipping a coin (Suresh, 2011). In large clinical
research, simple randomization can generate similar numbers of subjects in each
group. However, in studies with relatively small sample randomization results
could be problematic, resulting in an unequal number of participants among
groups (Suresh, 2011). To
overcome this problem smaller studies can use block randomization, it is
designed to randomize subjects into groups of equal sample sizes (Frane, 1998).
Whilst balance in sample size may be achieved with this method, groups may
be generated that are not comparable in terms of certain covariates. This
unbalance of covariates could confound the data and may negatively influence
the results of the trial (Suresh, 2011).

This study has
important covariates that need to be evenly distributed between the control and
experimental group, for example co-occurring diagnoses and severity of
symptoms. Therefore, stratified randomisation will be used. Stratified
randomisation is a method that address the need to control and balance the
influence of covariates (Kernan et al., 1999). Specific
covariates must be identified by the researcher, then a separate block is
generated for each combination of covariates, and subjects are assigned to the
appropriate block. Simple randomization is then performed within each block to
assign subjects to one of the groups (Suresh,
2011). This method only works when all
participants and their covariates have been identified before group assignment,
therefore this study will be unable to enrol participants on a continuous
basis.

Unfortunately, using a perfect randomization method alone
does eliminate selection bias due to human interference. Without adequate
allocation concealment, random and unpredictable assignment sequences can be
undermined (Schulz & Grimes, 2002). Allocation concealment refers to
preventing the next assignment in the clinical trail from being known. A system
needs to be in place to ensure that investigators, participants and involved
health care providers do not know to which group a participant will be
allocated before the study commences (Schulz & Grimes, 2002). Research has
shown that trials using unclear or inadequate allocation concealment produced
up to 40% larger estimates of effect, compared with those that used adequate concealment
(Jüni et al., 2001). There are different methods of allocation concealment that
can be used: sequentially numbered, opaque, sealed envelopes; pharmacy
controlled; numbered or coded containers; central randomisation (Schulz &
Grimes, 2002). Whilst opaque, sealed envelopes is
generally considered acceptable some argue it may be susceptible to
manipulation (Bhandari et al., 2001),
therefore in this study will use central randomisation. In this technique the individual recruiting the patient contacts a
central methods centre via telephone or a secure computer after the patient is
enrolled (Dettori, 2010).

Blinding, also known as ‘Masking’, is another method that
can be used to reduce bias in RCT, it involves keeping key individuals, such as
health care providers, participants and outcome assessors, unaware of the
treatment administered or what the true hypothesis of the trial is (Montori et
al., 2002). Blinding of health care providers and participants can
prevent performance bias that occurs if a therapeutic intervention is viewed more
or less favourably than the other comparison groups (Altman et al, 2001). Outcome
assessors can be blinded to minimise the risk of detection bias (Altman et al,
2001) and data analysis can be blinded to prevent any influence on the choice
of analytical strategies and methods (Altman et al, 2001). However, blinding,
unlike allocation concealment, may not always be appropriate or possible.
Binding is less frequently reported in RCTs that assess non pharmacological
treatments such as behavioural interventions and psychotherapy. This is mainly
due to the difficulty in achieving and maintain it (Boutron
et al., 2004).

In this study participants
could easily recognise whether they were in the CBT or art therapy group,
therefore it will not be possible to blind the participants. This can be
problematic as participants could potentially drop out of the study if they are
not randomized into group they would prefer (Friedberg et al., 2010). To try
and overcome this participants can be blinded to the study hypotheses and the
other intervention groups. Ultimately, not all levels of blinding may be
possible or practical in this situation. It is important to identify the areas
where blinding was not achieved and incorporate the results into the report’s
findings.

This study would be also be unable to blind the counsellors
to the participants’ treatment assignment since they would be delivering the
CBT or art therapy. If blinding is unavailable, then care needs to be taken to
blind them to the outcomes and the study hypothheses, as well as maintaining a
clinical and personal equipoise (Johnson & Remien, 2003). Operating
procedures, standardized treatment manuals and continuous assessment of
treatment fidelity can also be used to minimize the counselors’ biases (Rains
& Penzien, 2005).

Data analysis is one of the later stages of a RCT but it is
still susceptible to bias. Ideally, all the participants in the trial should complete
the study, follow the protocol and provide data on all the outcomes of
interest. However in reality, most trials have missing data and inappropriate
handling of this information can lead to bias (Alejandro et al., 2007). There are
a few strategies that can minimise bias in these circumstances, with intent-to-treat
analysis (ITT) considered as the gold standard for RCT (Brody, 2016). ITT
analysis includes all the participants that were randomised into groups,
regardless of whether they withdrew for treatment or deviated from protocol (Gupta, 2011). ITT analysis avoids overoptimistic estimates of intervention efficacy
that is caused by removal of non-compliers (Heritier et al., 2003).  Whist ITT has received a considerable
amount of criticism, the application of ITT can be improved if the complete
outcome data is available from all the participants (Gupta, 2011). Therefore, in this study care will be taken to minimise
missing responses and follow ups will be done on those who withdrew from
treatment. 

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