I wrote this commentary together with Adam Chekroud. You can find a PDF version of this commentary here.
The largest and longest investigation of treatment-resistant depression (TRD) to date, the Sequenced Treatment Alternatives to Relieve Depression (STAR*D), was conducted in four stages. Each stage comprised a different medication, and patients moved to the next stage if they did not improve considerably. Only 25% of all depressed patients remitted in stage I, only 46% after all stages . TRD is a common and severe mental health issue, and research has shown that it is associated with worse outcomes and predicts lack of future treatment response: only 1 in 10 patients respond to standard treatments within 1 year .
I published a new paper in the Journal of Affective Disorders entitled “The 52 symptoms of major depression: Lack of content overlap among seven common depression scales” (PDF).
The paper examines content overlap of 7 common depression scales, and concludes that there scales feature 52 distinct depression symptoms that are listed in the main Figure below.
After reviewing the paper “The relative responsiveness of test instruments can be estimated using a meta-analytic approach: an illustration with treatments for depression” by Kounali et al. 2016, the editor invited me to write a response because I raised the point that a large amount of researchers seem to misunderstand the concept of scale responsiveness.
In my paper entitled “Are more responsive depression scales really superior depression scales?” published in the Journal of Clinical Epidemiology (PDF), I clarify problems with contemporary work on responsiveness pertaining to content validity, longitudinal measurement invariance, and unidimensionality.
We have a new commentary in Molecular Psychiatry entitled “Problems with latent class analysis to detect data-driven subtypes of depression” (PDF; together with the fantastic Hanna van Loo, Rob Wanders & Klaas Wardenaar).
This was a response to numerous papers over the last years that found separate latent classes in depression that may be statistical artifacts and driven by violations of local independence.
» van Loo, H. M., Wanders, R. B. K., Wardenaar, K. J., & Fried, E. I. (2016). Problems with latent class analysis to detect data-driven subtypes of depression. Molecular Psychiatry. (PDF)
We have a new network paper out in Psychological Medicine entitled “Network Analysis of Depression and Anxiety Symptom Relations in a Psychiatric Sample” (PDF).
I tend to think of the paper as a state-of-the-art replication of the great comorbidity paper by Cramer et al. 2010 who investigated the comorbidity of depression and generalized anxiety disorder.
In contrast to Cramer et al. who analyzed data of mostly healthy participants, this paper (first-authored by Courtney Beard and Alex Millner ) estimated the network structure of depression and anxiety symptoms in a large clinical population, using regularized partial correlation networks. We also investigated the change of network structure over the course of an 8-day period.
I am very happy to announce that Cherie Armour and me are organizing a special issue for the European Journal of Psychotraumatology, together with the editor-in-chief Miranda Olff.
In summary, we are looking for papers on:
- PTSD symptom networks, either on the level of groups or individuals, in cross-sectional or time-series data
- The stability of PTSD symptomatology and PTSD networks over time
- PTSD comorbidity research from a network perspective
- Symptom-based analyses that investigate whether PTSD symptoms are differentially related to various clinically relevant variables such as impairment, risk factors, biomarkers, etc.
- Systematic reviews focusing on PTSD symptomics
The deadline is December 20th; more infos are available here.
Imagine you are a group of scientists and want to find out whether a novel drug X works on a specific problem Y. You run the following study:
- You enroll a small sample of 14 participants who have the problem Y.
- You give these 14 participants your novel drug, but you do not enroll a second group who received placebo medication, which means that any effect you find may be due to the placebo effect.
- On top of that, all your 14 patients keep using a well-accepted medication for problem Y while taking the novel drug X, meaning that any changes you find may be due to their normal medication, not X.
- You measure improvements of problem Y 240 minutes and 3 months after the treatment.
- You find evidence of improvements for 7 of 14 people 240 minutes after treatment, and for 2 of 14 people 3 months later; it is unclear whether this is due to placebo, normal medication, or other reasons.
- You write a scientific paper with the strong conclusion: “repeated doses of X rapidly and robustly decreased Y.”
This is exactly what what Ionescu et al. (2016) did in a new study entitled “Rapid and Sustained Reductions in Current Suicidal Ideation Following Repeated Doses of Intravenous Ketamine: Secondary Analysis of an Open-Label Study”, published in the Journal of Clinical Psychiatry. Continue reading
(Series: critical commentaries on depression trials. Prior posts: 1, 2, 3, 4)
Antidepressants only marginally outperform placebos (Khan & Brown, 2015) – which has led to a number of novel strategies to try to improve treatment for patients suffering from depressive disorders. Adjunctive Nutraceuticals present one such strategy: providing patients with specific forms of dietary supplements in addition to antidepressants (Wikipedia: Nutraceuticals). This is in line with general dietary supplements: the industry has grown considerably in the last decades, and more than half of the US adult population consume dietary supplements regularly (Wikipedia: Dietary Supplements). Interestingly, meta-analyses have repeatedly failed to find any evidence for positive effects of dietary supplements such as Vitamin C on numerous health outcomes – ranging from the common cold to cancer – in general population samples (Hemilä & Chalker, 2013; Lee, Oh & Myung, 2015).
In a new paper entitled “Adjunctive Nutraceuticals for Depression: A Systematic Review and Meta-Analyses”, Sarris et al. investigated whether adjunctive nutraceuticals provide significant benefits to patients with Major Depression. They reviewed 40 studies in total: 9 studies on folic acid, folinic acid, methylfolate, or a combination of folic acid and vitamins B6 and B12; 8 on tryptophan (or 5-HTP) and omega-3; 4 on S-adenosylmethionine; 2 on zinc, inositol, vitamin C, and vitamin D; and 1 for creatine, B12, and an amino acid combination. The mean sample size per study was small (n=63), and only 31 of the 40 studies were randomized placebo-controlled trials.
A few months back, a study was published in JAMA Psychiatry claiming that whole-body hyperthermia is an effective treatment for depression (UPDATE: the paper was published in full now on August 6th 2016, some time after the online first print). For those who don’t know psychiatric journals very well, JAMA Psychiatry is currently ranked highest psychiatric journal in terms of impact-factor.
I never got around to submit the commentary I wrote, but was interviewed a few days ago about the study by Eric Boodman, so I will briefly explain the main issues with the trial.
Due to the comparably low efficacy of classic antidepressant [1–3], psychiatry has recently looked for different avenues to depression treatment. One example is Ketamin , although the findings so far are very weak. Another example is a novel drug aimed to selectively influence the hippocampal volume of patients  (I previously wrote about this study here) – although hippocampal abnormalities are negligible in patients with Major Depression .
Dr. Fava and colleagues have published an antidepressant trial on adjunctive Brexpiprazole, a novel atypical antipsychotic drug that was developed to treat schizophrenia, in the Journal of Clinical Psychiatry.
The trial consisted of 4 steps:
- The authors carefully selected 50 patients who had not shown improvements with their current antidepressant.
- These patients received 2 more weeks of treatment with their current antidepressant, to provide a baseline measure.
- For the next 6 weeks, patients additionally received Brexpiprazole, with a dosage from 1mg in week 1, 2mg in week 2, and 3mg in subsequent weeks.
- Finally, patients received 4 more weeks of antidepressant treatment without Brexpiprazole.
Unpublished commentary. PDF, DOI 10.13140/RG.2.1.5149.7362.
Eiko I. Fried, University of Leuven, Belgium
Lauren M. Bylsma, University of Pittsburgh, USA
Randolph M. Nesse, Arizona State University, USA
After submitting the commentary to Clinical Psych Science, the Editor wrote us that they generally do not publish commentaries, despite the website stating they do. Unfortunate … we’ll just publish it here.
I recently stumbled across the paper “A metastructural model of mental disorders and pathological personality traits”, authored by Aidan Wright and Leonard Simms in 2015. I enjoyed reading it: it’s a strong methodological paper, using state-of-the-art exploratory structural equation models (ESEM). It would have been a pleasure for me to review this paper: a very clear recommendation to publish.
However, the paper contains a few (what I personally consider to be) issues representative of the psychopathology structural equation modeling (SEM) literature in general. Therefore, I will use it as an example to write a more general critique. I’ll try to use non-technical language so that readers who know neither SEM nor psychopathology research well can follow.
It’s also very important to me to not misunderstand this as criticism of the authors. I very much like their work – in fact, we’re speaking in the same symposium at a conference later this year – it could really be any other paper as well.
Dr Payne, director of the Women’s Mood Disorders Center and an associate professor of psychiatry and behavioral sciences at Johns Hopkins University, has written a commentary on the website of the American Psychiatric Association entitled “Yes or No: Prescribing Antidepressants to Pregnant Patients”.
Her main argument is summarized in the abstract of the article:
We published a new study in Psychological Assessment a few days ago, and I would like to take the time to explain what these results imply. You can find the full text here.
Let me summarize the findings first.
We examined 2 crucial psychometric assumptions that are part of nearly all contemporary depression research. We find strong evidence that these assumptions do not seem to hold in general, which impacts on the validity of depression research as a whole.
What are these psychometric assumptions? In depression research, various symptoms are routinely assessed via rating scales and added to construct sum-scores. These scores are used as a proxy for depression severity in cross-sectional research, and differences in sum-scores over time are taken to reflect changes in an underlying depression construct. For example, a sum-score of symptoms that supposedly reflects “depression severity” is often correlated to stress or gender or biomarkers to find out what the relationship between these variables and depression is; this is only valid if a sum-score of symptoms is actually a reasonable proxy for depression severity. In longitudinal research, if a sum-score decreases from 20 points to 15 points in a population, we conclude that depression improved somewhat. This is only valid if the 20 points and the 15 points reflect the same thing (if the 20 points would reflect intelligence and the 15 points neuroticism, the difference of 5 points over time would be meaningless).
On December 15th, Molecular Psychiatry published our commentary “The volumes of subcortical regions in depressed and healthy individuals are strikingly similar: a reinterpretation of the results by Schmaal et al”. You can find the full text PDF in the above link if you have a subscription to the journal, otherwise see the project’s open science repository where you can find a full text (PDF) along with the R-code and data we used for the simulation analysis.
The commentary is a response to the insightful paper by Schmaal et al. (2015) published in Molecular Psychiatry a few months ago: a large team of collaborators meta-analyzed the volumes of a number of brain regions, and found the hippocampal volume to be smaller in 1728 depressed patients compared to 7199 healthy participants. We greatly commend the authors to the huge amount of very thorough work they did; analyzing a number of large datasets really moves the field forward by providing much more accurate estimates than the prior literature.
We wrote the commentary for a number of reasons: