Dropout From Trauma-Focused Treatment for PTSD in a Naturalistic Setting

Abstract

Background
Although evidence-based interventions for posttraumatic stress disorder (PTSD) are highly effective, on average about 20% of patients drop out of treatment. Despite considerable research investigating PTSD treatment dropout in randomized controlled trials (RCTs), findings in naturalistic settings remain sparse.

Objective
Therefore, the present study investigated the frequency and predictors of dropout in trauma-focused interventions for PTSD in routine clinical care.

Method
The sample included n = 195 adults with diagnosed PTSD, receiving trauma-focused, cognitive behavioral therapy in routine clinical care in three outpatient centers. We conducted a multiple logistic regression analysis with the following candidate predictors of dropout: patient variables (e.g., basic sociodemographic status and specific clinical variables) as well as therapist’s experience level and gender match between therapist and patient.

Results
Results showed a dropout rate of 15.38%. Age (higher dropout probability in younger patients) and living situation (living with parents predicted lower dropout probability compared to living alone) were significant predictors of dropout. Dropout was not significantly associated with the therapist’s experience level and gender match.

Conclusions
In conclusion, routinely assessed baseline patient variables are associated with dropout. Ultimately, this may help to identify patients who need additional attention to keep them in therapy.

Full text

Research Articles
Dropout From Trauma-Focused Treatment for PTSD in a
Naturalistic Setting
Verena Semmlinger1, Keisuke Takano2, Larissa Wolkenstein1,
Antje Krüger-Gottschalk3, Sascha Kuck3, Anne Dyer4, Andre Pittig5,
Georg W. Alpers6, Thomas Ehring1,7
[1]Department of Psychology, LMU Munich, Munich, Germany. [2]Human Informatics and Interaction Research
Institute (HIIRI), National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan. [3]Institute
of Psychology, WWU Münster, Münster, Germany. [4]Central Institute of Mental Health, ZISG Mannheim, Mannheim,
Germany. [5]Translational Psychotherapy, Institute of Psychology, Georg-August-University of Göttingen, Göttingen,
Germany. [6]Department of Psychology, School of Social Science, University of Mannheim, Mannheim, Germany.
[7]German Center for Mental Health (DZPG), Munich, Germany.
Clinical Psychology in Europe, 2025, Vol. 7(1), Article e14491, https://doi.org/10.32872/cpe.14491
Received: 2024-04-26 •Accepted: 2024-09-15 •Published (VoR): 2025-02-28
Handling Editor: Winfried Rief, Philipps-University of Marburg, Marburg, Germany
Corresponding Author: Verena Semmlinger, Department of Psychology, LMU Munich, 80802 Munich, Germany.
Phone +49 89 2180 5171; Fax: +49 89 2180 5224. E-mail: verena.semmlinger@psy.lmu.de
Supplementary Materials: Materials [see Index of Supplementary Materials]
Abstract
Background: Although evidence-based interventions for posttraumatic stress disorder (PTSD) are
highly effective, on average about 20% of patients drop out of treatment. Despite considerable
research investigating PTSD treatment dropout in randomized controlled trials (RCTs), indings in
naturalistic settings remain sparse.
Objective: Therefore, the present study investigated the frequency and predictors of dropout in
trauma-focused interventions for PTSD in routine clinical care.
Method: The sample included n = 195 adults with diagnosed PTSD, receiving trauma-focused,
cognitive behavioral therapy in routine clinical care in three outpatient centers. We conducted a
multiple logistic regression analysis with the following candidate predictors of dropout: patient
variables (e.g., basic sociodemographic status and speciic clinical variables) as well as therapist’s
experience level and gender match between therapist and patient.
Results: Results showed a dropout rate of 15.38%. Age (higher dropout probability in younger
patients) and living situation (living with parents predicted lower dropout probability compared to
This is an open access article distributed under the terms of the Creative Commons
Attribution 4.0 International License, CC BY 4.0, which permits unrestricted use,
distribution, and reproduction, provided the original work is properly cited.

living alone) were signiicant predictors of dropout. Dropout was not signiicantly associated with
the therapist’s experience level and gender match.
Conclusions: In conclusion, routinely assessed baseline patient variables are associated with
dropout. Ultimately, this may help to identify patients who need additional attention to keep them
in therapy.
Keywords
treatment dropouts, posttraumatic stress disorder, prediction, psychotherapy, clinical practice, naturalistic
setting
Highlights
• About 15% of patients receiving PTSD treatment in routine clinical care dropped out.
• This rate is lower than found in previous studies.
• Age and living situation were the only variables related to dropout.
Evidence-based interventions for posttraumatic stress disorder (PTSD) have been shown
to be highly effective (e.g., Mavranezouli et al., 2020). However, about 20% of patients
receiving an intervention for PTSD drop out of treatment (e.g., Varker et al., 2021). As
treatment dropout can lead to lower treatment effectiveness and reduced probability of
improvement (Barrett et al., 2008; Varker et al., 2021), PTSD treatment dropout is an
important clinical challenge. On a general level, dropout can be deined as termination
of an initiated treatment before the symptoms that had caused the patient to seek
treatment have been alleviated (Swift et al., 2009; Swift & Greenberg, 2012). Despite
repeated efforts to establish a common standard, there remains a lack of consensus in
the literature regarding the operationalization of dropout, resulting in different variants
being observed (e.g., Barrett et al., 2008; Imel et al., 2013). One criterion that is common
in many different operationalization methods is that dropout is a unilateral decision by
the patient without mutual agreement or discussion of the decision with the therapist
(Swift et al., 2012). In clinical practice, therapist judgement has been discussed for many
years as a preferred operationalization method (Swift & Greenberg, 2012; Wierzbicki &
Pekarik, 1993) that can be combined with an objective measure to ensure reliability and
comparability (Semmlinger & Ehring, 2022).
Previous research has focused on estimating the prevalence of dropout from psy
chological treatment in randomized controlled trials (RCTs). Across different mental
disorders, a large-scale meta-analysis found a weighted average dropout-rate of 19.7%,
95% CI [18.7, 20.7] (Swift & Greenberg, 2012). The average dropout rate reported from
evidence-based treatments for PTSD is comparable to this general dropout rate. In a
recent meta-analysis investigating dropout from guideline-recommended psychological
treatments for PTSD in RCTs, Varker et al. (2021) reported an average dropout rate of
20.9%, 95% CI [17.2, 24.9]. Similar dropout rates have been estimated by other previous
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meta-analyses that focus on a wider range of treatment orientations and settings (e.g.,
Imel et al., 2013: 18.3%, 95% CI [14.8, 21.8]; Lewis et al., 2020: 16%, 95% CI [14, 18]).
While there is a vast body of research investigating dropout in RCTs, less is known about
dropout rates from treatment for PTSD in routine clinical care. In a systematic review
investigating dropout from outpatient treatment for PTSD in a sample of veterans with
combat-related PTSD, Goetter et al. (2015) estimated a dropout rate of 36%, 95% CI [26.2,
43.9]. A recent meta-analysis including both RCTs and non-RCTs reported a weighted
average dropout rate of 41.5% from trauma-focused CBT for PTSD (Mitchell et al., 2022).
It is worth noting that, due to the focus of their analysis, Mitchell et al. (2022) only
reported the average dropout rate across all studies and did not include information on
the weighted dropout rates for RCTs and non-RCTs separately. Dropout rates for the
included non-RCT studies were 35%, 67.5%, and 72.2% (Mitchell et al., 2022).
For dropout from PTSD treatment a number of predictors have been discussed. First,
baseline PTSD symptom severity might inluence dropout, evidence however is mixed.
While Varker et al. (2021) did not ind a signiicant effect, Mitchell et al. (2022) showed
higher clinician-rated baseline PTSD symptom severity scores in patients dropping out
of treatment compared to completers (Hedge’s g = .50, 95% CI [-.95, -.04], p < .05). It is
worth noting that this effect applied only to clinician-rated but not to self-rated PTSD
severity. Zandberg et al. (2016) added to these indings by examining the inluence of
the rate of improvement on dropout as a function of symptom severity. The authors
showed that for patients with high baseline severity, high dropout rates were associated
with both very fast and very slow PTSD improvement, in contrast to patients with low
baseline severity, who showed high dropout rates only with fast improvement. The loss
of motivation and reduction in the credibility of treatment caused by slow improvement
of PTSD symptoms might result in a higher risk of dropout in patients with high PTSD
severity (Zandberg et al., 2016).
Second, comorbidity is often discussed as a possible predictor, especially comorbid
depression, generalized anxiety disorder (GAD), alcohol disorder, and borderline person
ality disorder (BPD) (e.g. Steindl et al., 2003). However, the indings are contradictory
and potential mechanisms are still unknown (e.g., Angelakis & Nixon, 2015; Mitchell et
al., 2022; Snoek et al., 2021). As possible explanations, different studies have discussed
depressed patients’ reduced ability for emotional processing (Angelakis & Nixon, 2015)
or the possible exacerbation of PTSD symptomatology and the increase of psychosocial
impairment as a result of comorbid BPD (Frías & Palma, 2015). Speciically, with regard
to dropout, a handful of studies have reported an effect of co-occurring depression (e.g.,
Zayfert et al., 2005), anxiety (e.g., McDonagh et al., 2005; van Minnen et al., 2002), or
comorbid personality disorder (e.g., McDonagh et al., 2005) on dropout. However, recent
large-scale meta-analyses did not ind a signiicant relationship between comorbidity and
dropout from PTSD treatment (Mitchell et al., 2022; Snoek et al., 2021; Varker et al., 2021).
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Third, other pretreatment clinical variables might be associated with dropout in PTSD
treatments. However, results to date are inconsistent and indings only rely on few
studies. Possible predictors are dificulties in emotional regulation (no effect: Belleau et
al., 2017; Shnaider et al., 2022; effect: Bremer-Hoeve et al., 2023; Gilmore et al., 2020),
anger (no effect: Hinton et al., 2022; van Minnen et al., 2002; mixed results: Rizvi et
al., 2009), impaired social functioning (effect: Zayfert et al., 2005), dissociative symptoms
(no effect: Hagenaars et al., 2010), and childhood trauma (effect: Miles & Thompson,
2016; mixed results: Resick et al., 2014; no effect: van Minnen et al., 2002). In addition,
the patient’s trauma response and maladaptive processing (e.g. avoidance, rumination,
overgeneralization) may be associated with dropout (Alpert et al., 2020; Shayani et al.,
2023). Alpert et al. (2020) found that more negative emotions and ruminative processing
predicted lower dropout, whereas overgeneralization was associated to higher dropout.
In contrast, Shayani et al. (2023) did not ind an effect of overgeneralization, ruminative
processing, and negative emotions, but did ind that higher levels of avoidance were
associated with higher dropout.
Concerning sociodemographic variables, only for the variable age is there a reason
able indication that younger age might be predictive for dropout in PTSD treatment
(Garcia et al., 2011; Goetter et al., 2015; Rizvi et al., 2009). However, in two recent
meta-analyses, none of the sociodemographic variables (including age) was found to be a
consistent predictor across studies (Lewis et al., 2020; Varker et al., 2021).
The majority of studies investigating dropout in PTSD treatment have used an RCT
design. Therefore, much less is known about dropout in naturalistic settings. To our
knowledge, there is only one review with a veterans sample (Goetter et al., 2015) and
few studies (Garcia et al., 2011; van Minnen et al., 2002) speciically investigating dropout
in routine clinical care. Transferring results from eficacy studies (RCTs) to naturalistic
therapeutic settings might be problematic (Leichsenring, 2004; Schindler et al., 2011).
Despite the well-known strength of RCTs it has been discussed whether randomization
in RCTs and the strict use of diagnosis speciic treatment manuals impose artiicial
conditions that do not relect the complexities of clinical practice. Therefore, naturalistic
studies are required (Leichsenring, 2004).
The aim of the present study was to investigate the frequency and predictors of
dropout in trauma-focused, guideline-recommended interventions for PTSD in routine
clinical care. Due to the lack of research on the prevalence and predictors from PTSD
treatment in naturalistic settings, our analyses followed an exploratory approach.
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Method
Participants
Data was assessed at three university-based outpatient centers providing treatment for
PTSD in Germany, located at LMU Munich (Dataset 1) as well as the University of Mün
ster and the Otto Selz Institute at the University of Mannheim (Dataset 2). The sample
consisted of 195 adult patients receiving treatment for PTSD. All data was collected
as part of effectiveness studies evaluating trauma-focused cognitive behavioral therapy
(TF-CBT) for PTSD in routine clinical care (previous, different analysis only on Dataset
2: Krüger-Gottschalk et al., 2024; Schumm et al., 2022, 2023). At pretreatment, all patients
met DSM-5 diagnostic criteria for PTSD assessed via the Clinician-Administered PTSD
Scale for DSM-5 (CAPS-5) (Weathers, Blake, et al., 2013), and were between 18 and 65
years old. Only participants who had already terminated their treatment at the respective
institution and had attended at least one treatment session were included in the study.
Exclusion criteria included current psychotic disorder, current substance dependence, or
current suicidal intent (First, Williams, Karg, & Spitzer, 2016). Sociodemographic and
clinical characteristics of the sample are presented in Table 1.
Treatment
Treatment in all outpatient centers consisted of trauma-focused cognitive behavioral
therapy following the same treatment manual. Due to the naturalistic setting of the
study, no randomization took place and there was no control condition. The treatment
manual is based on empirically tested therapy concepts (especially Ehlers & Clark’s
cognitive therapy approach, Ehlers & Wild, 2022, as well as DBT-PTSD principles, Bohus
et al., 2020) and follows a modularized phase-based approach (see also Ehring, 2019).
It includes three consecutive phases. Phase 1 can be summarized as preparation for
trauma-focused therapy, including providing a theoretical rationale, increasing treatment
motivation, or reducing risky or self-destructive behavior where needed. Phase 2 con
sisted of the trauma-focused interventions. Therapists could choose between different
trauma-focused interventions, including Prolonged Exposure, cognitive therapy, Imagery
Rescripting, trigger analyses and discrimination training, as well as cognitive interven
tions targeting dysfunctional assumptions. Phase 3 was the inal phase of treatment
and focused on improving quality of life, resuming activities, and relapse prevention.
The treatment plan was intended to take each patient through all three phases, with
the number of sessions required for each phase and the selection of modules within
each phase varying from patient to patient. Depending on the current symptomatology,
deviations from this phase structure had to be made in individual cases.
Treatment sessions were usually provided on a weekly basis, with a regular session
duration of 50 minutes. The overall average treatment length was M = 36.6 sessions
(SD = 23.4). The average treatment length for dropout cases was M = 23.3 sessions
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(SD = 17.5) and M = 39.3 (SD = 23.5) for patient who did not drop out. On average
patients underwent M = 5.0 (SD = 1.3) preparatory sessions. This is higher than typically
reported in RCTs for PTSD, whereas 12 – 16 sessions are more frequently used. In the
German healthcare system patients are permitted to receive up to 80 treatment sessions.
Therefore, the reported number of sessions used in our study is typical of the German
healthcare system. Second, PTSD treatment in RCTs is often provided in 90-100 min
sessions, which means that the treatment dose received in the current study is not that
different to typical RCT settings. The treatments were conducted by either licensed CBT
therapists (39.2%) or psychotherapists in training (60.8%) employed at the outpatient
centers. Supervision by a CBT therapist with expertise in PTSD treatment was regularly
provided, on average at every second session. Given the naturalistic nature of the study,
it was not feasible to implement formal idelity checks. The majority of the therapists
were female (86.4%).
Measures
The baseline assessment included sociodemographic data, namely age, gender, marital
status, living situation, and education. Clinical variables were assessed using clinical
interviews and psychometric questionnaires. In addition, two therapist variables, i.e.,
experience level and gender match, were coded as potential predictors of dropout. For
each patient, we revised the patient iles, analyzing the therapeutic session protocols.
Dropout
Dropout was operationalized using the therapist’s judgement, and the termination had
to be initiated by the patient, without a mutual agreement that termination was the best
choice. Therapists routinely documented this information in patient iles on a treatment
termination form. In exceptional cases, where no information was provided, we used an
elaborate ile analysis, i.e., analyzing the three last session protocols for each respective
patient, to retrieve the information needed. If no or only ambiguous information could be
obtained, the patient was excluded from the study.
Clinician-Administered PTSD Scale for DSM-5 (CAPS-5)
The CAPS-5 (Weathers, Blake, et al., 2013; German translation by Schnyder, 2013) is a
structured diagnostic interview that assesses posttraumatic stress symptoms in the past
month. Symptoms are rated on a ive-point Likert scale ranging from 0 = absent to 4 =
extreme, with a rating of 2 or higher indicating the presence of a symptom (Weathers
et al., 2018). The presence of at least one symptom per cluster “intrusive symptoms” and
“avoidance”, and at least two symptoms per cluster “changes in mood and cognition” and
“hyperarousal” indicated the presence of a PTSD diagnosis. The CAPS-5 is a gold-stand
ard clinical interview with good reliability and validity (Weathers et al., 2018).
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Structured Clinical Interview for DSM (SCID)
The SCID (First, Williams, Karg, & Spitzer, 2016; Wittchen et al., 1997) was used to assess
the presence of comorbid disorders. The SCID for personality disorders (First, Williams,
Smith Benjamin, & Spitzer, 2016; Fydrich et al., 1997) was administered to assess the pres
ence of comorbid personality disorders. The SCID is a gold-standard clinical interview to
assess diagnostic criteria according to the DSM. For each disorder, interview questions
along the DSM criteria allow the rating of diagnostic symptoms as present or absent.
PTSD-Checklist for DSM-5 (PCL-5)
The PCL-5 (Weathers, Litz, et al., 2013; German version by Krüger-Gottschalk et al.,
2017) was used to assess posttraumatic symptom severity. The assessment consists of 20
items, corresponding to the DSM-5 PTSD criteria. Distress caused by each symptom is
rated on a ive-point Likert scale ranging from 0 = not at all to 4 = extremely. Symptom
severity was obtained as a sum score of all 20 items (range 0 to 80). The German PCL-5
has demonstrated high internal consistency (Cronbach’s α = .95) (Krüger-Gottschalk et
al., 2017). In the current study, internal consistency was also high (α = .87). Please note
that Cronbach’s alpha for all analyzed questionnaires was calculated on the non-imputed
dataset.
Childhood Trauma uestionnaire (CTQ-28)
Exposure to traumatic childhood experiences was assessed with the CTQ-28 (Bernstein et
al., 2003; German version by Klinitzke et al., 2012). The CTQ-28 is a self-report question
naire consisting of 28 items, rated on a ive-point Likert scale ranging from 1 = never
true to 5 = very often true. A sum score for all items (range 25 to 128) was calculated. The
German CTQ-28 shows overall good psychometric properties. The internal consistency
for the four subscales without physical neglect is high (α ≥ .80), while the physical
neglect subscale shows weak internal consistency (α = .55) (Klinitzke et al., 2012). In the
current study, internal consistency was good (α = .95) for the total CTQ score.
Inventory of Interpersonal Problems (IIP-32)
The IIP-32 was used to assess interpersonal problems (Horowitz et al., 2000; German ver
sion by Thomas et al., 2011). The self-report questionnaire contains 32 items, assessing
interpersonal behavior that the participant either inds dificult or shows in excess. The
items are rated on a ive-point Likert scale ranging from 0 = not at all to 4 = extremely. In
the parent studies, different item versions of the questionnaire were used (IIP-127, IIP-64,
IIP-32). For the main analyses, we used the IIP-32 version and narrowed the long versions
down to the IIP-32. We calculated the IIP-32 total score as the mean of the eight scale
scores (Horowitz et al., 2000). The internal consistency of the German IIP-32 was rated as
satisfactory to good; for the individual scales it ranged from α = .60 to α = .83 (Thomas et
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al., 2011). In the current study the internal consistency for the total IIP-32 was high (α =
.90).
Dissociative Experience Scale (DES)
Dissociative symptoms were assessed with the Dissociative Experience Scale (DES)
(Bernstein & Putnam, 1986; German version by Spitzer et al., 2004, called FDS-20). The
DES is a 20-item self-report questionnaire. Items are rated on a scale ranging from 0%
(never) to 100% (all the time). We used the total mean score to determine the overall
dissociation. The DES showed good psychometric measures and the internal consistency
was α = .93 (Spitzer et al., 2004). In the current study internal consistency was high α =
.93.
Posttraumatic Cognitions Inventory (PTCI) and Interpretation of Symptoms
Inventory (IPSI)
Posttraumatic cognitions were assessed using a combined version of the PTCI (Foa et
al., 1999) and the IPSI (Dunmore et al., 1999) (German versions by Ehlers & Boos, 2000).
The self-report questionnaire assesses negative cognitions and beliefs in response to a
traumatic experience (PTCI) and to posttraumatic symptoms (IPSI). The 48 items are
rated on a seven-point Likert scale ranging from 1 = totally disagree to 7 = totally agree.
We used the total sum score for PTCI and the IPSI mean (Ehlers, 1999). The German PTCI
has demonstrated high internal consistency of α = .95 and good overall psychometric
properties (Müller et al., 2010). The internal consistency reported for the IPSI was α = .84
(Dunmore et al., 2001). In the current study internal consistency was high for PTCI (α =
.92) and IPSI (α = .92).
Dificulties in Emotion Regulation Scale (DERS)
Emotional dysregulation was assessed with the self-report questionnaire DERS (Gratz
& Roemer, 2004; German version by Ehring et al., 2008). The 36 items are rated on a
ive-point Likert scale ranging from 1 = almost never to 5 = almost always. We used
the DERS sum score (range 36 to 180) to determine possible dificulties in emotion
regulation. The German version of the DERS has an excellent internal consistency of α =
.96 (for the sum score) (Kruse et al., 2024). In the current study, internal consistency was
excellent, α = .94.
Procedure
The studies were approved by the local ethics committees at the LMU Munich, University
of Münster, and the University of Mannheim. All three outpatient centers are specialized
in the treatment of patients with trauma-related disorders. Participants referred to these
centers were screened for eligibility. If eligible, participants received detailed information
about the respective study, and written informed consent was obtained. Due to the
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naturalistic setting, participants were not randomized to different conditions but received
standard care (see treatment). After the baseline assessment had taken place, the treat
ment was initiated at the next possible date.
All candidate predictor variables were assessed at baseline. The baseline assessment
session consisted of clinical interviews (CAPS-5; SCID) as well as sociodemographic and
clinical questionnaires. As treatment was delivered in a naturalistic setting, a substantial
effort was made to prevent premature termination of treatment as part of the standard
procedure. In the case of excused absence, a new appointment offer was made; in the
case of unexcused absence, patients were called by the therapists to make a new appoint
ment. If no contact could be made after several attempts, a letter was sent asking the
patient to get in contact within a deined period of time to guarantee continued access to
treatment. If the patient clearly expressed the desire to discontinue treatment, no further
attempts to contact them were made.
Statistical Analyses
All statistical analyses were conducted using R (Version 4.2.0). Datasets from two parent
studies were merged for the current analyses. The dropout rate was calculated as the
proportion of the patients who dropped out to the total number of patients who had star
ted the treatment. There was a notable amount of missing data in some questionnaires
(M = 7%, SD = 4%, max = 27%). The missing data was assumed to be missing at random
(MAR) (Bhaskaran & Smeeth, 2014), and was imputed using the iterative procedure
of conditional multiple imputation technique on an item level, i.e., before calculating
the respective sum score. Conditional multiple imputation was realized by the ive-step
procedure proposed by Rubin (1976) and Kropko et al. (2014), using the R Multivariate
Imputation by Chained Equations (mice) package (van Buuren & Groothuis-Oudshoorn,
2011). The number of multiple imputations as well as the number of iterations were
set to ive (m = 5, maxit = 5), and we used predictive mean matching (pmm) as the
imputation method for continuous variables and the logistic regression (logreg) as the
imputation method for dichotomous variables. We conducted a sensitivity analysis to
ensure that the results were not affected by multicollinearity due to highly correlated
items in the dataset or by the use of the multiple imputed dataset for our main analysis.
First, we tested the differences in demographics and baseline symptom levels between
patients who dropped out and those who did not. Next, zero-order associations were ex
amined between dropout and the predictors of interest using point-biserial correlation on
the imputed data. We then conducted a multiple logistic regression analysis (maximum
likelihood estimation; imputed data) to investigate the unique effects of the variables on
dropout after controlling for the effect of the other variables in the model. The level
of signiicance was set as α = .05. We included the following variables as potential
predictors of dropout (all assessed at the beginning of treatment): age, gender, marital
status, living situation, education, posttraumatic symptom severity (PCL), exposure to
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traumatic childhood experiences (CTQ), interpersonal problems (IIP), overall dissociation
(DES), posttraumatic cognitions in response to the traumatic experience (PTCI) and to
posttraumatic symptoms (IPSI), emotional dysregulation (DERS), number of previous
treatments (outpatient and inpatient), number of comorbid disorders (all comorbid disor
der), comorbid personality disorder, therapist’s experience level (registered vs. in train
ing), and gender match.
Although our primary focus was on the effects of each predictor on dropout, we were
interested in how well the logistic regression model would predict dropout. We evaluated
the prediction performance using leave-one-out cross-validation on the imputed datasets.
The following three performance measures were computed (as medians across imputed
datasets): accuracy (i.e., the number of patients who were correctly identiied by the
model as dropouts or non-dropouts divided by the total number of patients), sensitivity
(i.e., the number of dropouts correctly identiied as dropouts by the model divided by
the number of dropouts), and speciicity (i.e., the number of non-dropouts correctly
identiied as non-dropouts divided by the number of non-dropouts). In addition, Receiver
Operating Characteristic (ROC) analysis was performed to evaluate the discriminatory
power of the logistic regression model. The area under the ROC curve (AUC) was calcu
lated to summarize the overall performance of the model, again as median AUC across
the multiple imputed datasets. The AUC typically ranges from 0 to 1, with 1 indicating
the perfect separation and with 0.5 meaning random separation (or poor prediction
performance).
Results
Descriptives and Demographics
The sample consisted of 195 patients, with a mean age of 36.14 years (SD = 13.02 years).
The majority of patients were female (75.9%). Ninety-six patients (56.8%) had at least
one comorbid disorder. The mean baseline PTSD symptom severity (PCL) was M =
46.2 (SD = 14.5), indicating a high severity of PTSD symptoms. Patients in the sample
experienced a variation of traumatic events, including accidental trauma, victimization,
or trauma predominantly related to death threat. There was a signiicant association
between dropout and age (see Table 1), but not with respect to the other variables
studied. The descriptive statistics for all demographic and clinical measures of the sample
are presented in Table1.
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Table 1
Descriptive Statistics of the Sample, of Dropouts, and of No Dropout at Baseline
Variable
Total Dropout No Dropout
t or χ2 (p)
n (%) / M (SD)n (%) / M (SD)n (%) / M (SD)
Gendera0.35 (.56)
Female 148 (75.9%) 21 (70.0%) 127 (77.0%)
Male 47 (24.1%) 9 (30.0%) 38 (23.0%)
Age in yearsb36.1 (13.02) 29.97 (10.11) 37.28 (13.21) 3.40 (.001)
Marital statusc0.73 (.70)
Single 112 (59,6%) 19 (65.5%) 93 (58.5%)
Married 58 (30.8%) 7 (24.1%) 51 (32.1%)
Divorced/widowed 18 (9.6%) 3 (10.4%) 15 (9.4%)
Living situationb3.90 (.27)
Alone 41 (21.9%) 7 (24.1%) 34 (21.4%)
With partner 106 (56.7%) 14 (48.3%) 92 (57.9%)
With parents 23 (12.3%) 2 (10.3%) 21 (13.2%)
Other 17 (9.1%) 5 (17.2%) 12 (7.5%)
Highest education leveld4.15 (.25)
University degree 35 (18.5%) 3 (10.0%) 32 (20.1%)
High school†35 (18.5%) 9 (30.0%) 26 (16.4%)
Secondary school‡102 (54.0%) 16 (53.3%) 86 (54.1%)
Other 17 (9.0%) 2 (6.7%) 15 (9.4%)
Previous treatmente0.63 (.43)
yes 106 (58.6%) 14 (50.0%) 92 (60.1%)
no 75 (41.4%) 14 (50.0%) 61 (39.9%)
Comorbid PDf< .001 (1.0)
yes 15 (8.6%) 2 (6.9%) 13 (8.9%)
no 160 (91.4%) 27 (93.1%) 133 (91.1%)
Number of CDg0.98 (1.1) 0.89 (0.91) 0.99 (1.13) 0.54 (.60)
Gender matchh0.02 (.89)
Match 107 (73.3%) 19 (70.4%) 88 (73.9%)
No match 39 (26.7%) 8 (29.6%) 31 (26.1%)
Approval therapisti0.02 (.89)
Licensed 56 (39.2%) 11 (42.3%) 45 (38.5%)
Non-licensed 87 (60.8%) 15 (57.7%) 72 (61.5%)
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Variable
Total Dropout No Dropout
t or χ2 (p)
n (%) / M (SD)n (%) / M (SD)n (%) / M (SD)
Clinical measuresa
PCL-5 46.2 (14.5) 47.0 (12.2) 46.1 (15.1) -0.33 (.74)
CTQ-28 55.2 (22.9) 49.6 (15.9) 56.2 (24.2) 1.46 (.15)
IIP-32 1.6 (0.6) 1.6 (0.5) 1.7 (0.7) 0.54 (.59)
DES 2.0 (1.8) 2.2 (1.5) 2.0 (1.9) -0.55 (.58)
PTCI 131.7 (36.3) 135.3 (33.2) 131.0 (37.6) -0.59 (.55)
IPSI 3.5 (1.5) 4.0 (1.2) 3.5 (1.5) -1.77 (.08)
DERS 103.8 (27.4) 103.3 (23.9) 103.9 (28.1) 0.11 (.91)
an = 195, bn = 187, cn = 188, dn = 189, en = 181, fn = 175, gn = 167, hn = 146, in = 143.
†High school: 12-13 years of schooling, according to the German school system; ‡Secondary school: 9-10 years
of schooling, according to the German school system; with partner = with partner and/or child(ren) in own
apartment; with parents = with parents/one parent; previous treatment = previous psychological treatment
(inpatient and/or outpatient); comorbid PD = comorbid personality disorder; number of CD = number of
comorbid disorders; M, SD, and t values for the clinical measures were calculated on the imputed dataset;
signiicant effects are displayed in bold.
Dropout in Trauma Focused-Treatment for PTSD
A total of 30 out of 195 patients (15.38%) were classiied as dropouts according to our
criteria.
Analysis of Dropout Prediction
Association Between Dropout and Predictor Variables
Point-biserial correlations were calculated on the imputed dataset to examine the zero-
order associations between dropout and the predictor variables. Results revealed a signif
icant positive correlation between dropout and age (r = -.19, p = .02) but not between
dropout and any other variable. See Supplementary Materials, Table S.1 for a complete
correlation matrix of all variables studied.
Prediction of Dropout
To examine the unique inluence of the variables of interest on dropout (0 = no dropout,
1 = dropout), a multiple logistic regression was performed on the imputed data. The
results indicated that age (β = - 0.07, p = .04) and living situation (β = -2.16, p = .04) were
signiicant predictors of dropout (see Table 2). Results showed that younger individuals
were more likely to drop out of treatment, with an OR of 0.94. Patients who lived with
their parents were at lower risk of dropout compared to those who lived alone (OR =
0.12).
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Table 2
Results of the Logistic Regression Analysis
Variable β SE t OR LL UL p
Intercept -0.46 2.13 -0.22 0.63 0.01 47.31 .82
Gender (Ref. = female) 0.87 0.65 1.34 2.39 0.67 8.59 .18
Age -0.07 0.03 -2.12 0.94 0.88 1.00 .04
Marital status (Ref. = single)
Married -0.33 0.67 -0.49 0.72 0.19 2.73 .63
Divorced/widowed 0.35 0.91 0.39 1.42 0.23 8.79 .70
Living situation (Ref. = alone)
With partner -0.11 0.68 -0.17 0.89 0.23 3.42 .87
With parents -2.16 1.02 -2.11 0.12 0.02 0.88 .04
Other 0.05 0.83 0.06 1.05 0.20 5.41 .95
Highest education level (Ref. = uni. degree)
High school 1.12 0.83 1.35 3.07 0.59 15.90 .18
Secondary school 0.45 0.79 0.57 1.57 0.33 7.43 .57
Other 0.99 1.10 0.90 2.68 0.31 23.41 .37
Previous treatment (Ref. = no) -0.39 0.54 -0.73 0.68 0.23 1.97 .47
Comorbid PD (Ref. = yes) 0.92 0.93 0.99 2.52 0.39 16.30 .33
Number of CD 0.03 0.31 0.08 1.03 0.52 2.02 .93
Gender match (Ref. = match) -0.20 0.62 -0.33 0.82 0.24 2.80 .74
Approval therapist (Ref. = licensed) -0.02 0.51 -0.05 0.98 0.36 2.66 .96
Clinical measures
PCL-5 -0.01 0.02 -0.34 0.99 0.95 1.04 .73
CTQ-28 -0.01 0.01 -0.81 0.99 0.96 1.02 .41
IIP-32 0.02 0.58 0.04 1.02 0.32 3.26 .97
DES -0.04 0.19 -0.22 0.96 0.66 1.40 .83
PTCI 0.01 0.01 0.66 1.01 0.99 1.03 .51
IPSI 0.47 0.27 1.72 1.60 0.93 2.76 .09
DERS -0.02 0.02 -1.12 0.98 0.95 1.01 .26
Note. Ref. = reference category; with partner = with partner and/or child(ren) in own apartment; with parents=
with parents/one parent; uni. degree = university degree; previous treatment = previous psychological treat
ment (inpatient and/or outpatient); comorbid PD = comorbid personality disorder; number of CD = number of
comorbid disorders; OR = Odds ratio; lower and upper CI refer to the corresponding 95% conidence intervals of
the OR; signiicant effects are displayed in bold.
Prediction Performance
Using leave-one-out cross-validation on the imputed datasets, we evaluated the predic
tion performance of the logistic regression model in distinguishing between people who
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dropped out vs. those who did not dropout from the treatment. The model showed an
accuracy of 80.5%. This accuracy score should be interpreted carefully as the data was
not balanced between dropout (15.38%) and no dropout (84.62%). Indeed, the speciicity
was excellent (95.2%) although the sensitivity was poor (3.3%), meaning that the model is
not good at identifying dropouts. ROC analysis showed an AUC value of 0.58, indicating
the marginal discriminatory power of the logistic regression model.
Discussion
The irst aim of the present study was to investigate the frequency of dropout in trauma-
focused, guideline-recommended interventions for PTSD in routine clinical care. 15.38%
of patients unilaterally decided to prematurely terminate a started PTSD treatment. The
dropout rate found in our study was considerably lower than previous estimates in
routine clinical care. This applies for a sample of veterans (e.g., 36%, Goetter et al., 2015),
as well as for a joint consideration of trauma-focused treatments for PTSD in RCTs and
non-RCTs (e.g., 41.5%, Mitchell et al., 2022). The present indings are further accentuated
by the fact that the estimated dropout rate is comparable or even slightly lower than
mean dropout rates reported in meta-analyses of highly standardized RCTs, e.g., 16%
for a wide range of PTSD treatments (Lewis et al., 2020) and 20.9% from guideline-recom
mended PTSD treatment (Varker et al., 2021). This inding on the low dropout rate is
of particular importance as in clinical practice it is a major therapeutic goal to develop
not only effective but also acceptable and feasible treatments. A number of possible
explanations for the low dropout rate in our study are conceivable. First treatment
was delivered in university-based outpatient centers which provide a well-structured
treatment approach along with close supervision, while also allowing for some lexibility
in treatment provision. Thus, it could be argued that the present setting combines the
strengths of both, RCTs and a naturalistic setting. Note, however, that in RCTs across
disorders higher dropout rates were found in university-based institutions (Swift &
Greenberg, 2012). Second, therapists in training might invest more time and effort to
tailor treatment to their patients’ needs than it is usually observed in regular care. Third,
the manualized TF-CBT provided as a treatment may have been a particularly suitable
form of treatment for the PTSD patients who participated in the current study. Conceiva
ble explanations include the modularized phase-based approach with high lexibility in
the selected modules per phase and lexibility in the sessions provided per module and
phase. It is further conceivable that the specialization of the outpatient centers in PTSD
treatment has an additional effect. Forth, we used well deined criteria to operationalize
dropout (therapist decision combined with patient-initiated dropout).
The second aim of the study was to investigate predictors of dropout in trauma-fo
cused, guideline-recommended interventions for PTSD in routine clinical care. A multi
ple logistic regression revealed age and living situation to be signiicant predictors, with
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higher risk of dropout in younger individuals and lower risk of dropout in patients who
lived with their parents as opposed to living alone. The inding of younger age being
predictive for dropout adds to previous indings on predictors of dropout in the general
and PTSD-speciic literature (Goetter et al., 2015; Swift & Greenberg, 2012), with only
few studies not replicating these indings (e.g., Varker et al., 2021). Note, that all patients
in the study were adults (between 18 and 65 years). Possible explanations include the
fact that young patients may have more competing time demands (Goetter et al., 2015),
treatment may not suficiently match their needs, or young patients may face a lack of
stability in their living environments (de Soet et al., 2024). In addition, it is conceivable
that young adults have not yet experienced that PTSD symptoms in most cases do not
simply disappear on their own over time (Morina et al., 2014).
To our knowledge, no previous study has investigated the inluence of living situation
on premature termination of treatment. Note that although patients living with their
parents probably tend to be younger, the signiicant indings on lower risk of dropout in
patients who lived with their parents compared to living alone had a unique effect, i.e.,
when controlling for the inluence of age. To explain our indings, it appears important
to address the inluence of parental support on treatment outcomes. In their review of
dropout in adolescents, de Soet et al. (2024) showed that parental approval, participation,
and support were associated with lower risk of dropout. Therefore, young patients living
with their parents might perceive more parental support and thus dropout becomes less
likely than if these patients were living alone. However, more research is needed to
understand the inluence of living situation on premature termination of treatment.
We also examined the possible role of several clinical variables as predictors of
dropout. Results showed that baseline symptom levels and associated clinical variables
were overall not predictive of dropout. This is in line with earlier indings (mostly based
on data collected using RCT designs) showing that e.g., symptom severity (Varker et al.,
2021) or comorbidity (Mitchell et al., 2022; Snoek et al., 2021; Varker et al., 2021) were
not predictive of dropout. A notable exception is a study by Mitchell et al. (2022), which
did ind higher PTSD symptom severity at baseline predicted dropout; however, this was
only the case for clinician-rated PTSD severity and not for self-rated PTSD scores. Thus,
the role of baseline PTSD symptom severity on dropout needs to be examined in further
research focused on a possible role of methodological variables.
With regard to the impact of therapist characteristics on dropout our indings in
dicate that neither the experience level nor the gender match of the therapist has a
signiicant inluence on the dropout rate. This contradicts previous indings on treatment
dropout across disorders. There is substantial evidence for the so-called therapist effect,
which states that differences between therapist inluence dropout rates (Deisenhofer
et al., 2024; Saxon et al., 2017; Zimmermann et al., 2017). In addition, research has
indicated an effect of therapist experience level on dropout (Roos & Werbart, 2013; Swift
& Greenberg, 2012). For PTSD treatment in particular, evidence is sparse, with initial
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evidence for a therapist effect on dropout (Sayer et al., 2022). In the present study
possible inluences of therapist characteristics might have been minimized by the fact
that patients were treated in a highly specialized service with close supervision, and
the fact that most therapist were at an early-career stage. Therefore, the variability of
therapist characteristics may have been rather low in the current study. In line with
this reasoning, Deisenhofer et al. (2024) found that the therapist effect on dropout was
signiicantly reduced by such institution effects.
Although it was not the primary focus of the current study, we additionally tested
how well the logistic regression model would predict dropout. Taking the given imbal
ance between dropout and no dropout into account, the model comprising different
pretreatment variables was not successful in predicting whether a patient who just
started treatment would dropout during the course of treatment. Our results are in line
with Vöhringer et al. (2020) who reported poor results on the discriminative power
of pretreatment variables to distinguish between dropouts and completers. However,
Bremer-Hoeve et al. (2023) were able to predict dropout in PTSD treatment using ma
chine learning techniques.
In sum, only very few variables assessed in the current study were signiicant predic
tors of dropout, and the overall model could not predict dropout to a practically useful
level. This is broadly in line with the majority of earlier indings. Thus, therapists and
researchers should be cautious about making conident predictions about retention based
on baseline data.
Limitations
This study has a number of important strengths. One major strength is the naturalistic
setting of the study, which allows for lexibility and variance in the trauma-focused,
guideline-recommended treatment provided. In addition, the naturalistic setting contrib
utes signiicantly to an increase in external validity and generalizability of the results
to clinical practice. Nevertheless, there are a number of noteworthy limitations. First,
the number of participants included in the analysis was limited, potentially leading to
reduced statistical power. Even though we combined data from three outpatient centers,
we had to exclude a substantial number of participants. This was due to the strict
inclusion criteria regarding PTSD diagnosis and missing data for the assessment of
dropout despite extensive ile analysis. Second, treatments were not standardized but
allowed for some lexibility based on a manual delineating key treatment principle. On
the one hand, this can be regarded as a strength of the study as it is typical for routine
clinical practice, where manuals are usually less strictly applied than in RCT research.
On the other hand, however, we cannot rule out the possibility that the variability in the
composition and timing of the use of different treatment modules may have obscured
effects of certain variables in predicting dropout, as therapists may have counter-acted
these variables in treatment. Third, results could be limited by the method used to
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operationalize dropout. Forth, the uncontrolled study design allows a more naturalistic
investigation of dropout. However, in contrast to an RCT design the internal validity of
effects of different variables on dropout is low. Speciically, it remains unclear whether
confounding variables that were not controlled may have inluenced on the occurrence
of dropout. Last, although we examined a wide range of variables, potentially important
aspects are missing in our dataset. These include type of trauma experienced, treatment
characteristics (e.g., session frequency), and patterns of change during treatment (e.g.,
rate of improvement).
Conclusion and Future Directions
In conclusion, this study provides important knowledge about the dropout rate and
predictors of dropout in trauma-focused, guideline-recommended interventions for PTSD
in routine clinical care. Results show that the dropout rate in this naturalistic study
was comparable to dropout rates found in RCTs. In addition, two baseline predictors
of dropout were identiied, suggesting that young adults with PTSD may need close,
supportive care, especially when they are no longer living with their parents. Therapists
can act as supportive guides, build and strengthen hope (Swift & Greenberg, 2012), and
be aware of urgent crises and the social needs of their young patients.
Possibly most importantly, however, our indings replicate earlier results showing
that identifying patients at risk of dropping out of treatment early-on by baseline varia
bles is challenging and currently not possible at a practically useful level. A number
of implications can be drawn from this inding. First, from an applied perspective,
these indings contradict widespread clinical beliefs about trauma-focused interventions
being less acceptable to patients with high symptom severities, high comorbidity, or
complex symptom presentations (e.g., emotion dysregulation, dissociation, interpersonal
dificulties). Neither earlier research nor our current indings suggest that patients with
these particularly severe and/or complex presentations are more likely to drop out of
treatment. However, larger samples may provide more power and enable us to examine
even a broader scope of potential predictor variables with modern machine learning ap
proaches (see Taubitz et al., 2022). Second, the cumulated indings may suggest that it is
necessary to look beyond pretreatment factors when predicting dropout and to addition
ally include variables investigating processes occurring in the course of treatment. For
example, Zandberg et al. (2016) found that the rates of symptom change had a signiicant
inluence on dropout in patients with comorbid PTSD and alcohol dependence. Patients
with low baseline symptom severity showed low risk for dropout in slow improvement
and higher risk in fast improvement. When baseline symptom severity was high, the
effect was u-shaped, with high risk of dropout in both slow and fast improvement
(Zandberg et al., 2016). Third, further research should focus on investigating additional
variables characterizing the treatment process, in particular the frequency of sessions
provided. In a large-scale meta-analysis Hoppen et al. (2023) showed lower dropout
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rates for trauma-focused treatments delivered in high intensity. These indings are in
line with Levinson et al.’s (2022) meta-analytical indings on dropout from PE provided
in an outpatient setting. Finally, as earlier evidence has been inconsistent, we followed
an exploratory research approach. Therefore, further studies are needed to test speciic
hypotheses based on theory. In addition, it appears recommendable to systematically
assess subjective reasons from the patients’ perspective (Vöhringer et al., 2020).
Expanding research into dropout from PTSD treatment in these ways appears highly
relevant since dropout continues to be an important clinical challenge preventing a con
siderable subgroup of treatment-seeking PTSD sufferers from receiving effective treat
ment. A better understanding of predictors of – and ultimately causal factors involved
in – dropout may ultimately help to develop preventive strategies to reduce dropout and
keep patients with severe symptoms in effective treatment.
Funding: This research received no speciic grant from any funding agency, commercial or not-for-proit sectors.
Acknowledgments: The authors have no additional (i.e., non-inancial) support to report.
Competing Interests: The authors report there are no competing interests to declare.
Ethics Statement: The studies were approved by the local ethics committees at the LMU Munich, University of
Münster, and the University of Mannheim. Written informed consent was obtained for all participants.
Reporting Guidelines: We report how we determined our sample size, all data exclusions (if any), all
manipulations, and all measures in the study, and we follow JARS.
Related Versions: This publication forms part of the doctoral thesis of Verena Semmlinger at the LMU Munich.
Semmlinger, V. (2024). The complexity of treatment failure – Prevalence and predictors of dropout and non-response in
psychological treatment for traumatized populations [Doctoral dissertation, LMU Munich]. Electronic Theses
Repository of LMU Munich. https://doi.org/10.5282/edoc.34387
Preregistration: This study’s design and its analysis were not pre-registered.
Social Media Accounts: @thomasehring.bsky.social
Data Availability: The authors have no permission to share the data. The code is available upon reasonable request.
Supplementary Materials
The Supplementary Materials contain a full correlation matrix of all variables studied (see
Semmlinger et al., 2025S).
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Index of Supplementary Materials
Semmlinger, V., Takano, K., Wolkenstein, L., Krüger-Gottschalk, A., Kuck, S., Dyer, A., Pittig, A.,
Alpers, G. W., & Ehring, T. (2025S). Supplementary materials to "Dropout from trauma-focused
treatment for PTSD in a naturalistic setting" [Full correlation matrix of all variables studied].
PsychOpen GOLD. https://doi.org/10.23668/psycharchives.15955
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