ArticlePDF AvailableLiterature Review

Remission from post-traumatic stress disorder in adults: A systematic review and meta-analysis of long term outcome studies



Content may be subject to copyright.
Remission from post-traumatic stress disorder in adults: A systematic
review and meta-analysis of long term outcome studies
Nexhmedin Morina
, Jelte M. Wicherts
, Jakob Lobbrecht
, Stefan Priebe
Department of Clinical Psychology, University of Amsterdam, Amsterdam, The Netherlands
Department of Methodology and Statistics, Tilburg University, The Netherlands
Unit for Social and Community Psychiatry, Queen Mary University of London, UK
We conducted a meta-analysis on spontaneous long-term remission from PTSD.
Remission was dened as reporting PTSD at baseline and not after at least ten months.
42 studies and 81,642 participants were included.
Overall, 44.0% of participants remitted from PTSD after a mean of 40 months.
abstractarticle info
Article history:
Received 16 November 2013
Received in revised form 7 February 2014
Accepted 7 March 2014
Available online 14 March 2014
Post-traumatic stress disorder
Prospective studies
Posttraumatic stress disorder (PTSD) is a frequent mental disorder associated with signicant distress and high
costs. We conducted the rst systematic review and meta-analysis on spontaneous long-term remission rates,
i. e., without specic treatment. Data sources were searches of databases, hand searches, and contact with
authors. Remission estimates were obtained from observational prospective studies of PTSD without specic
treatment. Remission was dened as the actual percentage of PTSD cases at baseline who are non-cases after a
minimum of ten months. Forty-two studies with a total of 81,642 participants were included. The mean observa-
tion period was40 months. Acrossall studies, an average of 44.0% of individuals with PTSD at baseline were non-
cases at follow-up. Remission varied between 8 and 89%. In studies with the baseline within the rst ve months
following trauma the remission rate was 51.7% as compared to 36.9% in studies with the baseline later than ve
months following trauma. Publications on PTSD related to natural disaster reported the highest mean of remis-
sion rate (60.0%), whereas those on PTSD related to physical disease reported the lowest mean of remission
rate from PTSD (31.4%). When publications on natural disaster were used as a reference group, the only type
of traumatic events to differ from natural disaster was physical disease. No other measured predictors were
associated with remission from PTSD. Long-term remission from PTSD without specic treatment varies widely
and is higher in studies with the baseline within ve months following trauma.
© 2014 Elsevier Ltd. All rights reserved.
1. Introduction
Epidemiological research indicates that most people experience at
least one potentially traumatic event during their lifetime (Breslau,
Davis, Andreski, & Peterson, 1991; Creamer, Burgess, & McFarlane,
2001; Kessler, Sonnega, Bromet, Hughes, & Nelson, 1995; Perkonigg,
Kessler, Storz, & Wittchen, 2000). In many parts of the world, individ-
uals are exposed to large-scale traumatic events, such as wars or natural
disasters (Neria, Nandi, & Galea, 2008; Priebe et al., 2010). Whilst
traumatic experiences can lead to a range of mental health problems,
post-traumatic stress disorder (PTSD) is the most documented disorder
following trauma. The diagnostic criteria for PTSD require the onset of
characteristic symptoms following exposure to a traumatic event that
must be present for more than one month (American Psychiatric Asso-
ciation, 2000). The prevalence estimates of PTSD are high. For example,
in the European and US general population the 12-month prevalence of
PTSD has been estimated between 2.0 and 3.5% (Kessler, Chiu, Demler,
Merikangas, & Walters, 2005; Wittchen et al., 2011). PTSD is associated
with signicant mental and physical distress (Nemeroff et al., 2006)as
well as high economic burden (Kessler, 2000; Sabes-Figuera et al.,
2012; Wittchen et al., 2011).
There is good empirical evidence for the moderate efcacy of
trauma-focused psychological interventions (Ehlers et al., 2010) and
Clinical Psychology Review 34 (2014) 249255
Correspondingauthor at: Universityof Amsterdam, Department of Clinical Psychology,
Weesperplein 4, 1018 XA Amsterdam, The Netherlands. Tel.: +31 20 525 8607; fax: + 31
20 525 6810.
E-mail address: (N. Morina).
0272-7358/© 2014 Elsevier Ltd. All rights reserved.
Contents lists available at ScienceDirect
Clinical Psychology Review
to a lesser degree for pharmacotherapy (Stein, Ipser, & McAnda, 2009).
Yet, a signicant number of individuals with PTSD do not seek treat-
ment for their complaints (Gavrilovic, Schützwohl, Fazel, & Priebe,
2005), or fail to receive treatment, e.g., when they live in areas with lim-
ited or no access to mental health services after war or natural disasters
(Morina, Rushiti, Salihu, & Ford, 2010). The question arises as to how
important it is to expand the provision of treatment to all those people
with PTSD who are currently without treatment. This can only be
assessed based on data about the long-term outcomes of PTSD without
treatment. Available prospective studies on the course of PTSD indicate
different trajectories in different populations. Differences across studies
are presumed to occur due to the different nature of traumatic events,
methodological differences, current living conditions, and psychological
factors (Brewin, Andrews, & Valentine, 2000; Ozer, Best, Lipsey, &
Weiss, 2003; Schnurr, Lunney, & Sengupta, 2004). There is lack of a pub-
lished systematic review on the remission rate of PTSD without specic
treatment. Accordingly, we conducted a systematic review and meta-
analysis of prospective studies to assess the remission rate of PTSD
without specic treatment. Furthermore, the study aimed at identifying
variables that explain variations in remission estimates across studies.
2. Method
Observational prospective studies on the natural course of PTSD
published since 1980 (i.e., since the introduction of PTSD in DSM-III)
(American Psychiatric Association, 1980) were located in the following
computerized bibliographic databases: PUBMED, PsycINFO, and the
PILOTS database managed by the US National Center for PTSD. The
followingsearch terms were used: post-traumatic stress disorder or post-
traumatic stress disorder or PTSD AND long* or prognos* or follow-up or
prospect* or cohort* or endur* or prolong* or persist* or ongoing or contin*
or durable or outcome study or natural history or clinical course.Inaddi-
tion, a hand search of the following journals assumedto be likely to pub-
lish relevant articles was conducted: American Journal of Psychiatry,
Archives of General Psychiatry, British Journal of Psychiatry, Journal of
Nervous and Mental Disease, and Journal of Traumatic Stress. Finally, an
iterative bibliography search was performed on citations published of
all articles included in the review. The last search was conducted in
March 2013.
Publications had to meet the following criteria: 1) use of a prospec-
tive design, 2) a sample size of at least 40 participants at the rst assess-
ment; 3) at least 80% of participants older than 17 years; 4) report of
remission rates of PTSD, 5) use of a validated PTSD measurement (diag-
nostic interview or self-report) that was based on either DSM or ICD
criteria for PTSD, 6) follow-up conducted at least ten months after the
rst assessment, 7) report of response and drop-out rates, and 8) the
majority of participants were not treated for PTSD during the study
(i.e., intervention studies were excluded as well as studies reporting
that the majority of participants had received PTSD-related treatment
during the observation period of the study). We decided to include
studies with a follow-up conducted at least ten months after the rst
assessment in order to examine the long term course of the diagnosis
of PTSD. If a publication provided information on more than one
follow-up being conducted at least ten months after the rst assess-
ment, we used the data from the last follow-up period if this provided
the necessary information on remission from PTSD.
Relevant data from relevant publications were extracted using a
construed coding protocol. In studies with more than two measurement
points, the rst valid assessment and the last assessment were used. Age
was entered as a mean for each single study. If age was reported in cat-
egories, mean age was attained by multiplying number of participants
with the median age in the respective category. The median of the
category 65 + was 75 years. The marital status of participants was
determined in percentages per sample, with cohabiting and married
participants being the same category. The remaining participants were
coded as not living together. The response rate at baseline was the
percentage of participants who were included at baseline compared to
all those contacted who met study criteria. Number of drop-outs was
attained by subtracting the number of study completers from the total
of included participants at baseline.
The following variables were used as study-level predictors: type of
sampling (population based vs. critical population), type of instrument
used to assess PTSD (self-report measurement vs. diagnostic interview),
nature of assessment (face-to-face vs. via telephone), gender (propor-
tion men), age at study baseline, partnership at baseline, employment
status at baseline, country where study conducted (Western vs. non-
Western), treatment between baseline and follow-up, time between
trauma and baseline (bsix months vs. ), time between assessments
(months between baseline and last assessment), drop-out rate at
follow-up, type of traumatic exposure (abbreviated vs. extended), co-
morbid depression or anxiety disorders at baseline, and nature of the
trauma (separately analyzed using dichotomous variables, e.g. natural
disaster vs. accidental injury). In four studies, the time between trauma
and baselinecould not be estimated due to missing information. Comor-
bid depression or anxiety disorders at baseline were used as predictors
if the authors had reported co-occurring depressionor anxiety disorders
at baseline among individuals with PTSD. The temporal component of
trauma events was dummy coded according to Terr's proposition
(Terr, 1991). Type 1 corresponded to a relatively abbreviated exposure
such as a motor vehicle accident. Prolonged exposure to traumatic
events such as exposure to war-related events was dened as Type 2.
The nature of thetraumatic event was classied as one of eleven catego-
ries: natural disaster, war-related events among veterans, war-related
events among civilians, accidental injury, re/explosion, terrorist attack,
interpersonal violence, imprisonment, disease, death of signicant
others, or as a combination of the categories above. In total, no more
than two studies were based on war veterans, re/explosion, imprison-
ment, and death of signicant others, respectively, therefore these cate-
gories were not included in the analyses. However, the two studies
conducted among war veterans were combined with the studies con-
ducted with civilian war survivors as part of the category war survivors.
With regard to the examination of the association between nature of
traumatic events and remission from PTSD, we decided to use natural
disaster as the reference group. The decision was based on the ndings
that exposure to naturaldisasters is associated with lowerlevels of PTSD
than exposure to other traumatic events (Neria et al., 2008). According-
ly, we expected that remission from PTSD will be highest in studies
following exposure to natural disaster.
2.1. Statistical analysis
uals meeting criteria for PTSD at baseline and not at follow-up (remis-
sion). The relevant frequencies were transformed using the Freeman
Tukey transformation to allow for pooling of the samples. To account
for the differences in sample sizes, an inverse-variance weighted effect-
size was computed for the included studies (Borenstein, Hedges,
Higgins, & Rothstein, 2009). As larger sample sizes yield more accurate
estimates of the effect sizes found in the population compared to smaller
sample sizes, they were attributed a larger weight.
Because a preliminary analysis indicated substantial heterogeneity
in the data (Q
= 1250.66, p b0.001, I
= 97%), we used random-
effects model to estimate the mean and variance of remission and
mixed effects models in the subsequent meta-regression analyses
(Raudenbush, 2009). In addition, prospective studies are prone to high
dropout rates, which could obscure an accurate estimate of the course.
To account for the inuence of dropout, an estimate of the number of
PTSD cases among the dropouts was calculated using available data
regarding PTSD cases lost during follow-up. Four large-scale studies in-
cluded in the meta-analysis were reported on these data (Berninger
et al., 2010; Brackbill et al., 2009; Hedtke et al., 2008; Koren, Arnon, &
Klein, 2001). The estimate revealed that individuals with PTSD at
250 N. Morina et al. / Clinical Psychology Review 34 (2014) 249255
baselinewere 1.3 times morelikely to drop out than individuals without
PTSD. This estimate was applied when the relevant data was missing by
multiplying the number of dropouts in the study by the estimate. This
was inversed to correct for dropout regarding new cases.
2.2. Meta-regression analysis
Mixed-model meta-regression models were used to explain the
amount of heterogeneity in the data by extracted variables of interest
(Borenstein et al., 2009; Raudenbush, 2009). First, the inuence of dif-
ferences in study methodology and sample characteristics was exam-
ined. Subsequently, the predictive power of the main predictors was
examined: time between exposure to traumatic events and rst assess-
ment, time between assessments,type of trauma, and nature of trauma.
Comparable to a regression procedure, the change in predictive power
of the model using the variables was evaluated, i.e., the change in
Cochran's heterogeneity Q-statistic and the p-value of that statistic.
Meta-regressions were conducted for each predictor separately. In
light of the relatively low power associated with the current set of 42
samples, we used p = .10 as the nominal signicance level (Hedges &
Pigott, 2001).
Analyses were performed using the metafor package in R
(Viechtbauer, 2010). Reported results are expressed in the Free-
manTukey effect size and based on Restricted Maximum Likelihood
(Raudenbush, 2009). Percentages presented below were back-
transformed (Miller, 1978). We used a funnel plot analysis to assess
the likelihood of publication bias.
3. Results
3.1. Selection and inclusion of studies
The initial search identied 21.029 potential hits (see Fig. 1 for the
ow diagram). The rst review resulted in a total of 99 publications
eligible for inclusion. After contacting authors regarding missing infor-
mation related to the prevalence of PTSD at both assessment points in
potentially relevant articles, 49 publications were excluded because of
missing information and eight publications were identied as double
publications of already included studies. A total of 42 studies were nal-
ly included. Data were extracted by the rst and the third author. An
inter-rater reliability analysis regarding the coded information from
the included articles and using the kappa statistic for dichotomous
variables and intraclass correlation (ICC) for continuous variables was
performed to determine consistency among raters. This resulted in an
excellent inter-rater reliability of R = 0.90. Disagreements were jointly
discussed until an agreement was reached. Of all publications, 41 were
in English and one in German.
3.2. Characteristics of included studies
The included studies provided data from an aggregate sample of
81,642 participants from ve continents. Their mean age was 42.3
(SD = 11.7) and 48.8% of participants were male.
The rst assessment took place after a mean of 43.1 months (SD =
106.2, range: 1593, median = 7.5) following trauma (N-weighted
M = 32.9, SD = 35.6, median = 30). The follow-up took place
after a weighed mean of 40.0 months (SD = 43.3 range: 10204;
median = 24) after the rst assessment (N-weighted M = 29.4, SD =
15.6, median = 30). Study and sample characteristics, main outcome
variables, and remission rates for each included study are summarized
in the online Supplementary material.
3.3. Remission from PTSD
The funnel plot in Fig. 2 depicts the effect sizes for remission from
PTSD against studies' standard error, along with the type of PTSD
found in each included study. The funnel plot did not appear asymmet-
ric:Z= 0.39, p = 0.69. Overall, in a random effects model, the mean ef-
fect size for remission from PTSD was 0.75 (95% CI = 0.680.83, k = 42,
= 97%). Stated in percentages, 44.0% of participants remitted from
PTSD after a mean of 40 months. Remission rates among all studies varied
substantially, ranging from 8% to 89%. Fig. 3 shows the attributed weight,
effect size, and type of PTSD (i.e., Type 1 or 2) across included studies.
3.4. Meta-analytic regression
The association between study characteristics and methodologyand
remission rate was analyzed by separate meta-analytic regressions. Due
to the small number of studies reporting specic treatment, the treat-
ment categories concerning psychological treatment were collapsed
into one category treatment. The results are presented in Table 1.
Studies with the baseline conducted within the rst ve months
(a total of eleven studies) were more likely to have reported higher
remission rates. Of individuals with PTSD at baseline in these studies,
51.7% (ES = 0.84) did not meet criteria for PTSD at follow-up as com-
pared to 36.9% (ES = 0.68) of those with the baseline later than ve
months following trauma (a total of 27 studies). None of the other
investigated study characteristics was signicantly associated with
remission from PTSD.
Publications with participants with PTSD following exposure to a nat-
ural disaster reported the highest mean of remission rate (60.0%), where-
as publications with participants with PTSD associated with a physical
disease (such as myocardial infarction, subarachnoid hemorrhage,
or acute coronary syndrome) reported the lowest mean of remission
rate from PTSD (31.4%). Table 2 presents the results of the univariate re-
gression analysis on the nature of traumatic events with natural disaster
as the reference group. The mean remission rate in studies on PTSD relat-
ed to a disease was signicantly lower than the mean remission rate in
studies on PTSD following exposure to a natural disaster (p = 0.045).
The other types of traumatic events did not signicantly differ from the
category of natural disaster (see Table 2).
3.5. Sensitivity analyses
Given that the samplein the study by Brackbill et al. (2009)wa s larg-
er than the number of the remaining studies combined, all analyses
were repeated excluding the study by Brackbill. The mean effect size
for remission from PTSD among the 41 remaining studies was 0.75
(95% CI = 0.680.83; I
= 95%). Stated in percentages, 44.4% of partic-
ipants remitted from PTSD after a mean of 40 months. Finally, all meta-
analytic regression analysis reported above were re-conducted without
the study by Brackbill. Similarly to the main analyses, time between
trauma and baseline was the only variable associated with remission
from PTSD (R
= 0.10, p = 0.09). The study by Brackbill was conducted
later than ve months after trauma. When this study was removed from
the analysis, 37.0% (ES = 0.69) of participants in the remaining studies
with the baseline later than ve months following trauma were remit-
ted from PTSD at follow-up as compared to 51.7% (ES = 0.84) of
those with a baseline within the rst ve months following trauma.
No other variable about characteristics of the study or study methodol-
ogy was signicantly associated with remission rates of PTSD.
4. Discussion
The meta-analysis shows that the number of participants remitting
from PTSD after at least ten months varies greatly. On average nearly
half of participants diagnosed with PTSD remit from this disorder after
a mean of more than three years. Studies with the rst assessment of
PTSD within the rst ve months following trauma reported a higher
remission rate of PTSD than those with a later rst assessment of
PTSD. Further, participants with PTSD in relation to physical disease re-
ported a lower remission rate than participants with PTSD following a
251N. Morina et al. / Clinical Psychology Review 34 (2014) 249255
natural disaster. No other assessed variable was associated with remis-
sion from PTSD.
Strengths of the meta-analysis include the large amount of partici-
pants from 42 prospective studies as well as the fact that the studies
were conducted in different contexts, following exposure to a variety
of traumatic events and in different continents. Different factors were
tested as moderators, and sensitivity analyses were conducted while ex-
cluding the survey with the largest sample size (Brackbill et al., 2009).
The meta-analysis has also limitations. Most importantly, the number
of assessed potential factors to be associated with remission from
Freeman-Tukey effect size
Standard Error
0.316 0.237 0.158 0.079 0.000
0.00 0.50 1.00 1.50
Fig. 2. Funnel plot of the included studies (n = 42). Note: open circles represent short-
lived traumatic events (Type 1).
50 100 150 200
0.4 0.6 0.8 1.0 1.2
Time between T1 and follow-up (in months)
Freeman-Tukey effect size
Fig. 3. Effect size, attributed weight, and type of trauma across included studies (n = 42).
Note: open circles represent short-lived traumatic events (Type 1).
20,288 records identified through
electronic databases 16 records identified through iterative
bibliography search and
725 records identified through hand-search
21,029 records screened
for possible inclusion
99 full-text publications
42 studies included in meta-
20,930 publications
excluded due to not
meeting inclusion criteria
57 publications excluded:
- Missing information
after contacting
authors (n=49)
- Double publications
Fig. 1. Flowchart of study selection.
252 N. Morina et al. / Clinical Psychology Review 34 (2014) 249255
PTSD was limited. For instance, only ve and four publications reported
comorbidity rates of depression or anxiety, respectively, among partici-
pants with PTSD at baseline.
Research on the development of PTSD has suggested several factors
to be associated with PTSD, such as characteristics of traumatic events,
peri-traumatic psychological reactions, occurrence of acute PTSD, per-
sonality factors, additional life stress, lack of social support, and demo-
graphic variables (Brewin et al., 2000; Ehlers, Mayou, & Bryant, 1998;
Ozer et al., 2003). Most of these factors were not assessed in the studies
included in the current meta-analysis. From the factors included (see
Tables 1 and 2), time of baseline and PTSD in relation to disease were
the only factors associated with remission from PTSD. The signicant
difference in remission rates from PTSD in studies with the rst assess-
ment within the rst ve months following trauma as compared to
those with a later rst assessment (51.7% vs. 36.9%) indicates that
PTSD assessed after ve months following trauma is somewhat more
likely to be chronic. The studies with a baseline within ve months
following trauma included a range of traumatic events, such as disease,
accidental injury,terrorist attack, interpersonal violence, or mixed trau-
ma (see Supplementary material). This reects the nature of traumatic
events reported in the studies with a baseline later than ve months
following trauma. Furthermore, none of the included studies assessed
PTSD in relation to childhood traumatic events. It should be noted, how-
ever, that regardless of the time of rst assessment, the number of indi-
viduals still suffering from PTSD more than three years after the initial
assessment of this diagnosis is very high indicating a chronic character
of PTSD. Accordingly, our results point towards a need for concerted
action at different levels, including increased funding for clinical and
public health research to identify effective strategies for prevention
and early treatment for PTSD following exposure to traumatic events.
Early prevention programs as well as early interventions for those
with chronic PTSD would not only decrease subjective distress but
also overall societal burden due to high health care costs and other
costs associated with PTSD (Sabes-Figuera et al., 2012). Over the last
decades, several specic interventions for PTSD have been developed
and trauma-focused psychological interventions have demonstrated
effectiveness in rigorous clinical trials (Bisson et al., 2007; Ehlers et al.,
2010; Foa, Keane, Friedman, & Cohen, 2009). For example, the meta-
analysis by Bisson et al. (2007) yielded that trauma focused cognitive
behavior interventions are signicantly more effective than waiting
lists regarding both clinician-rated (standardized mean differences
[SMD] = 1.40) and self-rated PTSD symptoms (SMD = 1.70). A SMD
of 1.40 or higher indicates that more than 90% of patients receiving trau-
ma focused interventions had signicantly less symptoms of PTSD than
the average patient on the waiting list. Established effective treatments
need to be made widely available for individuals with PTSD, whilst re-
search should develop effective prevention interventions for individuals
exposed to traumatic events.
The highest remission rate was reported in studies on PTSD among
survivors of naturaldisaster. This nding is in line with studies revealing
that the PTSD prevalence rate in survivors of natural disasters is some-
what lower than in survivors of other forms of traumatic events
(Neria et al., 2008). However, it is rather surprising that the remission
rate of PTSD in the course of physical disease was the only one to signif-
icantly differ from the remission rate of PTSDamong survivors ofnatural
disaster. This may be explained by the fact that the remission rate of
Table 1
Univariate meta-regression analysis of study characteristics.
Variable Remission from PTSD
Population-based sampling 0.813** 0.103 0.069 0.112 0.000 42
Diagnosticinterview 0.706** 0.051 0.117 0.079 0.041 42
Face-to-face assessment 0.711** 0.071 0.064 0.086 0.000 42
Gender: male vs. female 0.858** 0.095 0.002 0.002 0.001 22
Age (baseline) 0.699* 0.276 0.001 0.007 0.002 16
Partner 0.918** 0.230 0.003 0.003 0.000 10
Employment 0.737** 0.162 0.001 0.002 0.000 10
Western country 0.682** 0.084 0.093 0.095 0.000 42
Treatment 1.077** 0.296 0.009 0 .006 0.129 7
Baseline 5 months 0.839** 0. 077 0.156+ 0.090 0.054 38
Baseline until follow-up 0.725** 0.054 0.001 0.001 0.000 42
Drop-out at follow-up 0.745** 0.076 0.000 0.002 0.000 42
Comorbid depression at baseline 0.666 0.439 0.002 0.009 0.000 5
Comorbid anxiety disorders at baseline 0.759** 0.085 0.003 0.002 NA 4
Note: ** = p b.01; * = p b.05; + = p b.10; For a predictor to be signicant, B
must be signicant; B = regression coefcients; SE = standard error; k = number of studi es;
NA = not applicable because no heterogeneity observed in this subset.
Table 2
Univariate meta-regression analysis regarding the nature of traumatic events.
Remission from PTSD
Variable B
Natural disaster (k = 5) [Reference] [Reference] [Reference] [Reference] [Reference]
War survivors (k = 7) (veterans and civilians) 0.890** 0.110 0.228 0.144 0.135
Civilian war survivors (k = 5) 0.890** 0.119 0.255 0.167 0.134
Accidental injury (k = 5) 0.890** 0.118 0.226 0.175 0.075
Terrorist attack (k = 10) 0.890** 0.111 0.055 0.135 0.000
Interpersonal violence (k = 4) 0.889** 0.132 0.080 0.203 0.000
Physical disease (k = 5) 0.890** 0.098 0.287* 0.144 0.266
Notes: Reference group : natural disaster; ** = p b.01; * p b.05; k is the number of studies; for a predictor to be signicant, B
must be signicant; B = regression coefcient;
SE = standard error.
253N. Morina et al. / Clinical Psychology Review 34 (2014) 249255
PTSD varied fundamentally in the studies within the single categories of
traumatic events (see Supplementary material). For example, the re-
mission rate in the studies on PTSD among survivors of accidental injury
varied between 70.2% (Blanchard et al., 1996)and13%(Bryant &
Harvey, 2002). The nding that individuals with PTSD due to a physical
illness reported the lowest remission from PTSD might be explained by
the notion that these individuals carry in them the illness that caused
the disorder and thus may not only be constantly reminded about it
but also be physically less capable of coping with it.
The other assessed factors were not signicantly associated with
remission from PTSD. Perhaps mostsurprising is the nding that remis-
sion from PTSD does not increase with longer observation periods. This
nding may be surprising, considering the large range of the follow-up
that extended between 10 and 204 months, with a weighed mean of
40 months after the rst assessment. The reviewed studies do not
support the notion that time heals all wounds. However, there is a
large variability of the remission rate between all studies, regardless of
the time period between baseline and follow-up (see Supplementary
material). For example, in studies with a longer follow-up than
100 months, the rate of remission from PTSD rages from 27.3%
(Maercker, Gaebler, & Schuetzwohl, 2013) to 75.0% (Perkonigg et al.,
2005). This large variability in the rate of remission from PTSD cannot
be explained by the factors assessed in the reviewed studies. Future
research should identify more relevant predictors of remission from
PTSD. Such research should include pre-traumatic, peri-traumatic as
well as post-traumatic conditions, as potential predictors.
One important issue is whether the instruments used to establish
PTSD were valid. Instruments with poor validity may overestimate
the prevalence of PTSD at baseline or at follow-up. Yet, the use of
more valid diagnostic interviews was not associated with a different
remission rate as compared to self-report measurements. PTSD in the
included studies was diagnosed based on symptoms in the past
month. Given that PTSD symptoms can uctuate, remission rates re-
ported in the studies might be rather positive as patients not meeting
PTSD criteria in the previous month, but before or after that month,
would be considered as a remission. This indicates that some of the in-
dividuals not meeting criteria for PTSD at follow-up might not have
been in permanent remission from PTSD.Accordingly,thissuggests
that spontaneous long-term permanent remission from PTSD is
likely to be even lower than the reported average remission rate of
The high variability across studies makes prognoses in specicsam-
ples and contexts difcult. Future research on remission from PTSD
should assess different potential factors that might explain the wide
variability in remission from PTSD, such as details of social support
which has been shown to impact on the development of PTSD and
might also be relevant for overcoming it (Brewin et al., 2000; Ozer
et al., 2003). Reporting of these factors should be provided separately
for individuals with PTSD at baseline, if appropriate. Most of the studies
included in the current review reported for example on employment
rates for the whole sample, yet failed to provide this information for in-
dividuals with PTSD at baseline separately. Knowledge about factors
inuencing remission from PTSD might help improve preventing PTSD
as well as treatment of PTSD.
Overall, PTSD tends to remit in only about half of individuals after a
period of more than three years, and the prognosis deteriorates if PTSD
is diagnosedlater than ve monthsfollowing trauma. This indicates that
effective treatmentshould be widely provided for individuals with PTSD
to avoid long term distress and reduce the associated costs for the af-
fected individuals, their families, and society at large.
Appendix A. Supplementary data
Supplementary data to this article can be found online at http://dx.
American Psychiatric Association (1980). Diagnostic and stat istical manual of mental
disorders (3rd ed.) Washington, D.C. American Psychiatric Association.
American Psychiatric Association (2000). Diagnostic and stat istical manual of mental
disorders DSM-IV-TR fourth edition (text revision). Washington, D.C. American Psychi-
atric Association.
Berninger, A., Webber, M. P., Niles, J. K., Gustave, J., Lee, R., Cohen, H. W., et al. (2010).
Longitudinal study of probable post-traumatic stress disorder in reghters exposed
to the World Trade Center disaster. American Journal of Industrial Medicine,53(12),
Bisson, J. I., Ehlers, A.,Matthews, R., Pilling, S., Richards, D., & Turner, S. (2007). Psycholog-
ical treatments for chronic post-traumatic stress disorder Systematic review and
meta-analysis. British Journal of Psychiatry,190,97104.
Blanchard, E. B., Hickling, E. J., Buckley, T. C., Taylor, A. E., Vollmer, A., & Loos, W. R. (1996).
Psychophysiology posttraumatic stress disorder related to motor vehicle accidents:
Replication an d extension. Journal of Consulting and Clinical Psycho logy,64(4),
Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to
meta-analysis. Chichester, UK John Wiley & Sons, Ltd.
Brackbill, R. M., Hadler, J. L., DiGrande, L., Ekenga, C. C., Farfel, M. R., Friedman, S., et al.
(2009). Asthma and posttraumatic stress symptoms 5 to 6 years following exposure
to the world trade center terrorist attack. JAMA Journal of the American Medical
Association,302(5), 502516.
Breslau,N., Davis, G. C., Andreski, P., & Peterson, E. (1991).Traumatic events and posttrau-
matic stress disorder in an urban population of young adults. Archives of General
Psychiatry,48(3), 216222.
Brewin, C., Andrews, B., & Valentine, J. (2000). Meta-analysis of risk factors for posttrau-
matic stress disorder in trauma-exposed adul ts. Journal of Consulting and Clinical
Psychology,68(5), 748766.
Bryant, R. A., & Harvey, A. G. (2 002). Delayed-onset posttraumatic stress disorder: A
prospective evaluation. Australian and New Zealand Journal of Psychiatry,36(2),
Creamer, M., Burgess, P., & McFarlane, A. (2001). Post-traumatic stress disorder: Findings
from the Australian National Survey of Mental Health and Well-Being. Psychological
Medicine,31(7), 12371247.
Ehlers, A., Bisson, J., Clark, D. M., Creamer, M., Pilling, S., Richards, D., et al. (2010). Do all
psychological treatments really work the same in posttraum atic stress disorder?
Clinical Psychology Review,30(2), 269276.
Ehlers, A., Mayou, R. A., & Bryant, B. (1998). Psychological predictors of chronic posttrau-
matic stress disorder after motor vehicle accidents. Journal of Abnormal Psychology,
107(3), 508519.
Foa, E. B., Keane, T. M., Friedman, M. J., & Cohen, J. A. (2009). Effective treatments for PTSD:
Practice guidelines from the International Society for Traumatic Stress Studies (2nd ed.)
New York Guilford Press.
Gavrilovic, J. J., Schützwohl, M., Fazel, M., & Priebe, S. (2005). Who seeks treatment after a
traumatic event and who does not? A review of ndings on mental health service
utilization. Journal of Traumatic Stress,18(6), 595605.
Hedges, L. V., & P igott, T. D. (200 1). The power of statistical tests in meta-analysis.
Psychological Methods,6(3), 203217.
Hedtke, K. A., Ruggiero,K. J., Fitzgerald, M. M.,Zinzow, H. M., Saunders, B. E., Resnick, H. S.,
et al. (2008). A longitudinal investigation of interpe rsonal violence in relation to
mental health an d substance use. Journal of Consu lting and Clinical Psychology,
76(4), 633647.
Kessler, R. (2000). Posttraumatic stress disorder: The burden to the individual and to
society. Journal of Clinical Psychiatry,61,414.
Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the
National Comorbidity Survey Replication. (vol 62, pg 617, 2005). Archives of
General Psychiatry,62(7), 709.
Kessler, R. C., Sonnega, A., Bromet, E., Hughes, M., & Nelson, C. B. (1995). Posttraumatic-
stress-disorder in the national comorbidity survey. Archives of General Psychiatry,
52(12), 10481060.
Koren, D., Arnon, I., & Klein, E. (2001). Long term course of chronic posttraumatic stress
disorder in tra fc accident victi ms: A three-year p rospective foll ow-up study.
Behaviour Research and Therapy,39(12), 144914 58. http://dx.d /
Maercker, A., Gaebler, I., & Schuetzwohl, M. (2013). Course of trauma sequelae in ex-
political prisoners in the GDR. A 15-year follow-up study. Nervenarzt,84(1), 7278.
Miller, J. (1978). Invers e of FreemanTukey double arcsine transformation. American
Statistician,32(4), 138.
Morina, N., Rushiti, F., Salihu, M., & Ford, J. D. (2010). Psychopathology and well-being in
civilian survivors of war seeking treatment: A follow-up study. Clinical Psychology &
Psychotherapy,17(2), 7986.
Nemeroff, C. B., Bremner, J. D., Foa, E. B., Mayberg, H. S., North, C. S., & Stein, M. B. (2006).
Posttraumatic stress disorder: A state-of-the-science review. Journal of Psychiatric
Research,40(1), 121.
Neria, Y., Nandi, A., & Galea, S. (2008). Post-traumatic stress disorder following disasters:
A systematic review. Psychological Medicine,38(4), 467480.
1017/S003329170 7001353.
Ozer, E., Best, S., Lipsey, T., & Weiss, D. (2003). Predictors of posttraumatic stress disorder
and symptoms in adults: A meta-analysis. Psychological Bulletin ,129(1), 5273.
254 N. Morina et al. / Clinical Psychology Review 34 (2014) 249255
Perkonigg, A., Kessler, R., Storz, S., & Wittchen, H. (2000). Traumatic events and post-
traumaticstress disorder in the community: Prevalence, riskfactors and comorbidity.
Acta Psychiatrica Scandinavica,101(1), 4659.
Perkonigg, A., Pster, H., Stein, M. B., Hoer, M., Lieb, R., Maercker, A., et al. (2005).
Longitudinal course of posttraumatic stress disorder and posttraumatic stress
disorder symptoms in a community sample of adolescents and young adults.
American Journal of Psychiatry,162(7), 13201327.
Priebe, S., Bogic, M., Ashcroft, R., Franciskovic, T., Gal eazzi, G. M., Kucukalic, A., et al.
(2010). Experience of human rights violations and subsequent mental disorders
A study following the war in the Balk ans. Social Science & Medicine,71 (12),
Raudenbush, S. W. ( 2009). Analyzing effect sizes: Random effects models. In H. Cooper, L.
V. Hedges, & J. C. Valentine (Eds.), The handbook of research synthesis and meta-
analysis (pp. 295315) (2nd ed.). New York Russell Sage Foundation.
Sabes-Figuera, R., McCrone, P., Bogic, M., Ajdukovic, D., Franciskovic, T., Colombini, N.,
et al. (2012). Long-term impact of war on healthcare costs: An eight-country study.
PLoS One,7(1), e29603.
Schnurr, P., Lunney, C., & Sengupta, A. (2004). Risk factors for the development versus
maintenance of p osttraumatic st ress disorder. Journal of Traumatic Stress,17(2),
Stein, D. J., Ipser, J., & McAnda, N. (2009). Pharmacotherapy of posttraumatic stress disorder:
A review of meta-analyses and treatment guidelines. CNS Spectrums,14(1), 2531.
Terr, L. (1991). Childhood traumas An outl ine and overview. American Journal of
Psychiatry,148(1), 1020.
Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package.Journal
of Statistical Software,36(3), 148.
Wittchen, H. U., Jacobi, F., Rehm, J., Gustavsson, A., Svensson, M., Jonsson, B., et al. (2011).
The size and burden of mental disorders and other disorders of the brain in Europe
2010. European N europsychopharmacology,21(9), 655679.
255N. Morina et al. / Clinical Psychology Review 34 (2014) 249255
... For some individuals, trauma symptoms (e.g., PTSD) can improve without mental health treatment. Morina et al. (2014) showed that for others, trauma symptoms persisted (e.g., when assessed at two time points), and mental health treatment was needed. Following trauma symptoms longitudinally to link individuals whose symptoms persist is important, given that the link between traumatic stress severity and future revictimization such as ADV can be high (Perez et al., 2012). ...
... We hypothesized that early childhood maltreatment would be associated with more severe traumatic stress, which would increase substance use, previously explained as the need to numb symptoms and substance misuse, placing youth at risk for subsequent revictimization or a higher prevalence of ADV (Ullman et al., 2009). We also hypothesized that more maltreatment would be associated with more severe traumatic stress 4 months later and, given evidence that traumatic stress often remits without treatment intervention (Morina et al., 2014) and the link between traumatic stress severity and future revictimization being high (Perez et al., 2012), we tested if continued traumatic stress (more severe traumatic stress at Timepoint 2 and then Timepoint 3), predicted a higher prevalence of ADV at Timepoint 5 (see Figure 4: For gender differences, given evidence that ADV prevalence often varies by gender identity, with female and nonbinary or transgender youth experiencing higher rates compared to cisgender males, we examined whether female and nonbinary or transgender identity was associated with ADV. We hypothesized that minoritized gender identity (i.e., female, nonbinary, transgender) would be associated with a higher prevalence of ADV (see Figure 5: Female → ADV [Path 11], gender minority → ADV [Path 12]). ...
Court-involved youth are more likely to report early childhood maltreatment histories, and these maltreatment histories can lead to subsequent risk behaviors such as adolescent dating violence (ADV). We used longitudinal data from the Epidemiological Project Involving Children in the Court on youth (N = 192) at first contact with the juvenile court to examine early childhood maltreatment with subsequent ADV, assessing pathways of alcohol, cannabis use, and traumatic stress. Using structural equation modeling, we found that early childhood maltreatment increased the risk for experiencing future ADV, traumatic stress, and alcohol use among youth in first-time contact with the legal system. Transgender youth were at greater risk of experiencing traumatic events, including ADV. Interventions to address traumatic stress and alcohol use among youth with ADV histories at the front door of system contact could reduce ADV likelihood over time. Such interventions should also consider the specific heightened needs of transgender youth, for whom available options are few.
... Ein spontanes Abklingen der Symptome tritt nach fünf Monaten bei weniger als der Hälfte der Betroffenen auf. Die Entwicklung einer chronischen Störung wird damit wahrscheinlich (Morina, Wicherts, Lobbrecht & Priebe, 2014;Perkonigg et al., 2005). Überlebende von Gewalt haben zudem ein vielfach erhöhtes Risiko einer erneuten Viktimisierung (Morina et al., 2014). ...
... Die Entwicklung einer chronischen Störung wird damit wahrscheinlich (Morina, Wicherts, Lobbrecht & Priebe, 2014;Perkonigg et al., 2005). Überlebende von Gewalt haben zudem ein vielfach erhöhtes Risiko einer erneuten Viktimisierung (Morina et al., 2014). Auch bei einer (Teil-)Remission der PTBS-Symptomatik verbleibt eine anhaltende Beeinträchtigung im Alltag (Westphal et al., 2011). ...
... Price et al. (2020) and Schell et al. (2004) showed that symptoms appear in an identifiable pattern and may have distinct courses over time (Price et al., 2020;Schell et al., 2004). In a systematic review with meta-analysis, Morina et al. (2014) found a remission rate of about 44% over time. It is possible that for a group of participants, an intense response occurred at first, but this acute symptomatic response gave way to a chronic average pattern over time. ...
Introduction Pandemics have the potential to be considered traumatic event, increasing the risk of developing post-traumatic stress symptoms (PTSS) in HealthCare Workers (HCW). However, few longitudinal studies have evaluated the impact of prolonged exposure to the risk imposed by COVID-19. Our aim was to identify subgroups of HCW with profiles of PTSS, how this profile changed during the pandemic and which variables were related to these changes. Methods We evaluated the levels of PTSS and psychological distress in a Brazilian HealthCare Workers' sample (n = 1398) in three waves of assessment: from May to June 2020 (Wave 1), December 2020 to February 2021 (Wave 2) and May to August 2021 (Wave 3), using Latent Profile Analysis (LPA) to identify subgroups with different profiles of symptms, and then, Latent Transition Analysis (LTA) was applied to examine changes in symptom profiles over time, including gender, psychiatric diagnosis history, and pandemic-related fears as covariates. Results two profiles were identified: high-PTSS profile (Wave 1–23%; Wave 2–64% and Wave 3–73%) and a low-PTSS (Wave 1–77%; Wave 2–36% and Wave 3–27%). Being female, fear of contamination, and fearing financial problems were strong predictors of changes in the profile. In addition, the participants had a high probability of being in the high-PTSS in the long run. Conclusion These results suggests that targeted interventions can mitigate the impact of pandemic. Providing financial support, and psychological support can be beneficial for those with psychiatric diagnoses and experiencing bereavement.
... In addition, the presence of PTSD in ABI patients is associated with lower return to work (Garrelfs et al., 2015;Glozier et al., 2008;Hedlund et al., 2011;Stein et al., 2018). As only a minority of people with PTSD spontaneously remits within months after onset (Kessler et al., 2017;Morina et al., 2014), awareness of the possible presence of PTSD in ABI patients is important, as well is treatment of PTSD. ...
Full-text available
Background: Posttraumatic stress disorder (PTSD) is prevalent in people with acquired brain injury (ABI). Despite the established efficacy of eye movement desensitization and reprocessing (EMDR) for PTSD in general, evaluation studies on EMDR in ABI patients with PTSD are limited. Objective: The aim of this study is to explore clinical features, treatment characteristics, feasibility and first indications of efficacy of EMDR in adult ABI patients with PTSD. Method: This retrospective consecutive case series included ABI patients, who received at least one session of EMDR for PTSD between January 2013 and September 2020. PTSD symptoms were measured using the Impact of Event Scale (IES) pre- and post-treatment. Affective distress was measured using the Subjective Units of Distress (SUD) pre- and post-treatment of the first target. Results: Sixteen ABI patients (median age 46 years, 50% males), with predominantly moderate or severe TBI (50%) or stroke (25%) were included. Treatment duration was a median of seven sessions. Post-treatment IES scores were significantly lower than pre-treatment scores (p < .001). In 81% of the cases there was an individual statistically and clinically relevant change in IES score. Mean SUD scores of the first target were significantly lower at the end of treatment compared to scores at the start of treatment (p < .001). In 88% of the patients full desensitization to a SUD of 0–1 of the first target was accomplished. Only few adjustments to the standard EMDR protocol were necessary. Conclusions: Findings suggest that EMDR is a feasible, well tolerated and potentially effective treatment for PTSD in ABI patients. For clinical practice in working with ABI patients, it is advised to consider EMDR as a treatment option.
Objective: This study aimed to identify the factors affecting posttraumatic stress disorder (PTSD) symptom remission prospectively through a 1-year follow-up of sexual assault (SA) victims. Methods: A total 65 female SA victims who visited the crisis intervention center were included. Self-administered questionnaires regarding PTSD symptoms and PTSD related prognostic factors were conducted at both recruitment (T1) and 1 year after recruitment (T2). The multivariate analyses were used to determine the significant predictors of PTSD remission/non-remission state 1 year after SA. Results: In logistic regression analysis, both anxiety and secondary victimization were identified as significant factors explaining the results on PTSD remission/non-remission state at T2 (Beck's Anxiety Inventory [BAI], p=0.003; Secondary Victimization Questionnaire, p=0.024). In a linear mixed analysis, both depression and anxiety were found to be significant variables leading to changes in Posttraumatic Diagnostic Scale for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition from T1 to T2 (BAI, p<0.001; Center for Epidemiological Studies Depression Scale, p<0.001). Conclusion: Depression, anxiety symptoms, and secondary victimization after SA were associated with PTSD symptom non-remission 1 year after SA.
Full-text available
The United States Department of Veterans Affairs (VA) uses a systematized approach for disseminating evidence-based, trauma-focused psychotherapies for post-traumatic stress disorder (PTSD). Within this approach, veterans with PTSD must often choose between Prolonged Exposure (PE) and Cognitive Processing Therapy (CPT), each delivered in their standard protocols. Many veterans have been greatly helped by this approach. Yet limiting trauma-focused therapy to these two options leaves the VA unable to fully address the needs of a variety of veterans. This limitation , among other factors, contributes to the suboptimal attrition rates within the VA. The present review proposes solutions to address treatment barriers that are both practical (such as time and travel constraints) and psychological (such as resistance to trauma exposure). By reducing barriers, attrition may lessen. Proposed countermeasures against practical barriers include intensive protocols, shortened sessions, telehealth, smartphone application delivery, or any combination of these methods. Countermeasures against psychological barriers include alternative evidence-based treatment programs (such as Acceptance and Commitment Therapy), intensive protocols for exposure-based treatments, and the integration of components from complementary treatments to facilitate PE and CPT (such as Motivational Interviewing or family therapy). By further tailoring treatment to veterans' diverse needs, these additions may reduce attrition in VA services for PTSD.
Despite its ubiquity and impact, secondary trauma as a diagnostic entity cannot be found in the current DSM. Cases are described of childhood secondary trauma involving the role of loss. These losses are primarily the loss of support from adult caretakers. The role of loss among children under 5 years of age and that of older children, is presented. Often childhood secondary trauma is due to the acquisition of the trauma of caretakers. The impact of taking of another’s life is detailed as are the roles of war, natural disaster, and the intergenerational transmission of trauma. The impact of media on secondary trauma is reviewed. Interventions for childhood secondary trauma are discussed.
Full-text available
A review of 2,647 studies of posttraumatic stress disorder (PTSD) yielded 476 potential candidates for a meta-analysis of predictors of PTSD or of its symptoms. From these, 68 studies met criteria for inclusion in a meta-analysis of 7 predictors: (a) prior trauma, (b) prior psychological adjustment, (c) family history of psychopathology, (d) perceived life threat during the trauma, (e) posttrauma social support, (f) peritraumatic emotional responses, and (g) peritraumatic dissociation. All yielded significant effect sizes, with family history, prior trauma, and prior adjustment the smallest (weighted r = .17) and peritraumatic dissociation the largest (weighted r = .35). The results suggest that peritraumatic psychological processes, not prior characteristics, are the strongest predictors of PTSD.
Full-text available
Hintergrund In einer Mitte der 1990er Jahre untersuchten Stichprobe ehemaliger politisch Inhaftierter der DDR wurden im 15-Jahres-Follow-up die Veränderungen der Diagnose- und Symptomprävalenzen der posttraumatischen Belastungsstörung (PTBS) sowie anderer psychischer Störungen untersucht. Zudem wurden die durch Kliniker erhobenen Diagnosenverläufe mit den retrospektiven subjektiven Einschätzungen der Studienteilnehmer verglichen. Methode Dreiundneunzig ehemals politisch inhaftierte Personen nahmen an der Folgestudie teil (85% Wiederteilnahme), ihr mittleres Alter betrug 64 Jahre. Diagnosen und Symptome wurden mittels strukturierter klinischer Interviews sowie Fragebögen erfasst. Die retrospektiven subjektiven Verlaufseinschätzungen der Teilnehmer wurden mittels eines PTBS-Symptomindex basierend auf 4 Symptomgruppen (Intrusionen, Vermeidung, Numbing, Hyperarousal) errechnet. Ergebnisse Eine aktuelle PTBS lag bei 33% vor (1997: 29%). Nur ca. in der Hälfte der Fälle lag diese schon 1994 vor, bei der anderen Hälfte handelt es sich um neu-inzidente bzw. remittierte Fälle. Nächsthäufige Diagnosen waren Major-Depression, Episode (26%), Panik (mit oder ohne Agoraphobie: 24%) sowie somatoforme Störungen (19%). Im PTBS-Symptomprofil nahmen Intrusionen, Flashbacks bzw. Entfremdungsgefühl im Zeitverlauf ab, Reizbarkeit und Schreckreaktionen hingegen zu. Die subjektive Selbsteinschätzung der PTBS-Symptomverläufe durch die Studienteilnehmer ergab im Vergleich mit den Diagnostikern häufiger ein resilientes („nie PTBS“) oder verzögertes und seltener ein remittiertes Verlaufsmuster. Schlussfolgerung Die Ergebnisse sprechen für eine traumabezogene Langzeitmorbidität, die allerdings instabiler ist als bisher angenommen.
This volume considers the problem of quantitatively summarizing results from a stream of studies, each testing a common hypothesis. In the simplest case, each study yields a single estimate of the impact of some intervention. Such an estimate will deviate from the true effect size as a function of random error because each study uses a finite sample size. What is distinctive about this chapter is that the true effect size itself is regarded as a random variable taking on different values in different studies, based on the belief that differences between the studies generate differences in the true effect sizes. This approach is useful in quantifying the heterogeneity of effects across studies, incorporating such variation into confidence intervals, testing the adequacy of models that explain this variation, and producing accurate estimates of effect size in individual studies. After discussing the conceptual rationale for the random effects model, this chapter provides a general strategy for answering a series of questions that commonly arise in research synthesis: 1. Does a stream of research produce heterogeneous results? That is, do the true effect sizes vary? 2. If so, how large is this variation? 3. How can we make valid inferences about the average effect size when the true effect sizes vary? 4. Why do study effects vary? Specifically do observable differences between studies in their target populations, measurement approaches, definitions of the treatment, or historical contexts systematically predict the effect sizes? 5. How effective are such models in accounting for effect size variation? Specifically, how much variation in the true effect sizes does each model explain? 6. Given that the effect sizes do indeed vary, what is the best estimate of the effect in each study? I illustrate how to address these questions by re-analyzing data from a series of experiments on teacher expectancy effects on pupil's cognitive skill. My aim is to illustrate, in a comparatively simple setting, to a broad audience with a minimal background in applied statistics, the conceptual framework that guides analyses using random effects models and the practical steps typically needed to implement that framework. Although the conceptual framework guiding the analysis is straightforward, a number of technical issues must be addressed satisfactorily to ensure the validity the inferences. To review these issues and recent progress in solving them requires a somewhat more technical presentation. Appendix 16A considers alternative approaches to estimation theory, and appendix 16B considers alternative approaches to uncertainty estimation, that is, the estimation of standard errors, confidence intervals, and hypothesis tests. These appendices together provide re-analyses of the illustrative data under alternative approaches, knowledge of which is essential to those who give technical advice to analysts.
Errors in Byline, Author Affiliations, and Acknowledgment. In the Original Article titled “Prevalence, Severity, and Comorbidity of 12-Month DSM-IV Disorders in the National Comorbidity Survey Replication,” published in the June issue of the ARCHIVES (2005;62:617-627), an author’s name was inadvertently omitted from the byline on page 617. The byline should have appeared as follows: “Ronald C. Kessler, PhD; Wai Tat Chiu, AM; Olga Demler, MA, MS; Kathleen R. Merikangas, PhD; Ellen E. Walters, MS.” Also on that page, the affiliations paragraph should have appeared as follows: Department of Health Care Policy, Harvard Medical School, Boston, Mass (Drs Kessler, Chiu, Demler, and Walters); Section on Developmental Genetic Epidemiology, National Institute of Mental Health, Bethesda, Md (Dr Merikangas). On page 626, the acknowledgment paragraph should have appeared as follows: We thank Jerry Garcia, BA, Sara Belopavlovich, BA, Eric Bourke, BA, and Todd Strauss, MAT, for assistance with manuscript preparation and the staff of the WMH Data Collection and Data Analysis Coordination Centres for assistance with instrumentation, fieldwork, and consultation on the data analysis. We appreciate the helpful comments of William Eaton, PhD, Michael Von Korff, ScD, and Hans-Ulrich Wittchen, PhD, on earlier manuscripts. Online versions of this article on the Archives of General Psychiatry Web site were corrected on June 10, 2005.
IntroductionIndividual studiesThe summary effectHeterogeneity of effect sizesSummary points
A formula for the inverse of the Freeman–Tukey double arcsine transformation is derived. This formula is useful when expressing means of double arcsines as retransformed proportions. When the mean is taken from original proportions involving different n's, it is suggested that the harmonic mean of the n's be used in the inversion formula.