Psychological Treatments for PTSD
Meta-Analytic Database of Randomized Controlled Trials /
© The Metapsy Collaboration
This online meta-analysis tool allows exploring a simplified version of the “Psychological Treatments for PTSD (Psychotherapy vs. Control)” dataset. The code used to simplify the data for this application can be downloaded here.
The data included in this meta-analysis tool has been compiled by the National Center for PTSD (U.S. Department of Veterans Affairs).
By default, the web application automatically makes a few decisions concerning the analysis settings. These analysis settings can be seen below under "Analysis Settings". By default, the application uses a Random-Effects Pooling Model for your meta-analysis. This is nearly always the correct choice when one is analyzing data in mental health research. In addition, the Paule-Mandel estimator is used by default to calculate the between-study heterogeneity of your meta-analysis. However, when clicking on the dropdown menu "Pooling Model", you can see that there are various estimators available when assuming a Random-Effects (RE) model. Sometimes, the choice of the between-study heterogeneity estimator can have an impact on the pooled results, and not every estimator is optimal under all circumstances. If you want to learn more about this, you can consult Harrer, Cuijpers, Furukawa & Ebert, 2020. It is also possible to calculate effects using a Fixed-Effect Model; but this model should only be used if you have good reasons for applying it.
Under "Analyzed Moderators", the moderating variables which are currently inspected in meta-regression/subgroup analyses in the "Moderator Analysis" tab are shown. By default, the publication year is used for a meta-regression on your selected data, and the country/region of a study is used as a subgroup analysis. You can easily add more moderator analyses by clicking on the white box. A dropdown will then appear, showing you more variables which are available. You can then add those variables by clicking on them. It is also possible to remove variables. To do that, again click on the white box and use the backspace ← key.
By default, the application also uses Knapp-Hartung adjustments to calculate the confidence interval for your meta-analysis result. It is possible to disable this method by unchecking the "Use Knapp-Hartung Adjustments" checkbox. You can learn more about this method in Harrer, Cuijpers, Furukawa & Ebert, 2020.
Finally, after you have completed the reconfiguration of your meta-analysis settings, simply click the "Re-run Meta-Analysis" button. This will reculate the results of your meta-analysis, using the new settings you have provided. Please note that the reanalysis may take some time before it is finished.
Removed as outliers:
As a sensitivity analysis, it can be helpful to identify potentially outlying studies and how they might affect the pooled result. This is particularly helpful when the heterogeneity of a meta-analysis is high. In this tab, the pooled effect without outlying studies is presented. Especially if the pooled effect without outliers has a lower heterogeneity than the original one, it can be helpful to also take this estimate into account for further decision making. If no outliers are detected, the results in this tab will be identical to the ones in the main analysis.
About the Psychological Treatments for PTSD Web App
This website was developed by researchers at the Vrije Universiteit Amsterdam, The Netherlands, the Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany, and the Technical University Munich, Germany. The data are based on a meta-analytic database that was developed by the National Center for PTSD.
Detailed documentation of the “Psychological Treatment for PTSD (Psychotherapy vs. Control)” database can be found here.
More details about the development of this database can be read in a published paper.
More information about the METAPSY project in general can be found in the following documents:
- The full protocol of the METAPSY project ↗
- The search strings that were used for the searches in bibliographic databases ↗
- Description of the variables that were extracted from the included randomized trials ↗
- Definitions of types of psychotherapies that are included in the Metapsy database ↗
- A general manual for doing meta-analyses (free e-book) ↗
- The Metapsy database flowchart ↗
- References of the 661 studies in the Metapsy database ↗
- The depression database 1 January, 2019 (categorisation of all included studies) ↗
- The data on the included studies comparing psychotherapy with control conditions, including the effect sizes ↗
- Published meta-analyses using the Metapsy database ↗
- A paper summarizing main results of the Metapsy database ↗
- Published 'individual participant data' meta-analyses, based on the Metapsy database ↗
About the Metapsy Web Application
This web application serves as an interface to the Metapsy meta-analytic database. The application uses a Shiny server instance to access the database, enable data downloads, and handle computations using state-of-the-art meta-analytic techniques.
metagen function is used internally to perform the meta-analytic pooling. By default, the Paule-Mandel estimator (DerSimonian & Kacker, 2007) is used to estimate the between-study heterogeneity/variance τ2 in a random-effects model; Restricted Maximum-Likelihood (REML) is used for meta-regressions. To calculate the Number Needed to Treat (NNT), the method by Furukawa and Leucht (Furukawa & Leucht, 2011) is used, assuming Control Event Rate (CER) of 0.19, which is derived from Cuijpers et al., 2014. For subgroup analyses, the
subgroup.analysis.mixed.effects function is used, implementing a mixed-effect model for which results within subgroups are pooled using a random-effects model (inheriting the τ2-estimator specified for the overall analysis), and results between subgroups are compared assuming a fixed-effects model.
To generate forest plots, the
forest function in
meta is used.
Outlier selection is conducted using the
find.outliers function in
dmetar. The function implements a simple outlier removal algorithm where all results for which the 95% confidence interval is outside the 95% confidence interval of the pooled effect are removed as outliers. Due to high computational costs, sensitivity analyses based on the “Leave-One-Out” paradigm are not conducted for large meta-analyses (k>50). For smaller meta-analyses, a Baujat plot (Baujat et al., 2002) is created using the
baujat function in
Publication bias analyses are conducted using the
eggers.test function in
dmetar (which is a wrapper for the
metabias function in
meta). A P-curve is created using the
pcurve function in
dmetar. Please note that several prerequisites should be considered before P-curves can be interpreted (Simonsohn et al., 2014).
For the risk of bias overview, the
rob.summary function in
dmetar is used.
The metapsyData Package
metapsyData package allows you to access the meta-analytic psychotherapy databases in R. To install the package, use the following code:
if (!require("devtools")) install.packages("devtools") devtools::install_github("metapsy-project/metapsyData")
metapsyData package allows to access the Metapsy meta-analytic psychotherapy databases direct in your
R environment. Once installed, simply run the
getData function to save the data locally. The documentation of the package is also hosted by rdrr.io.
Here is an example:
#> study condition_arm1 condition_arm2 #> 1 Acarturk, 2016 EMDR Waitlist #> 2 Ahmadi, 2015 EMDR Waitlist #> 3 Aldahadha, 2012 EMDR Waitlist #> 4 Alghamdi, 2015 NET Waitlist #> 5 Alsheikh Ali, 2020 Other non-trauma-focused Other #> 6 Arntz, 2007 Exposure Therapy Waitlist #> [...]
The raw data files can be accessed in the associated GitHub repository under
You can run
citation("metapsyData") to retrieve the latest citation.
Harrer, M., Ebert, D. D., Karyotaki, E., Cuijpers, P. (2022). metapsyData: Access the Meta-Analytic Psychotherapy Database. R package version 0.1.0. DOI: 10.5281/zenodo.5171880.