- Things some clinical and health psychology students wish they had known before they committed themselves to evaluating a psychotherapy for their dissertation study.
- A well designed pilot study addressing feasibility and acceptability issues in conducting and evaluating psychotherapies is preferable to an underpowered study which won’t provide a valid estimate of the efficacy of the intervention.
- PhD students would often be better off as research parasites – making use of existing published data – rather than attempting to organize their own original psychotherapy study, if their goal is to contribute meaningfully to the literature and patient care.
- Reading this blog, you will encounter a link to free, downloadable software that allows you to make quick determinations of the number of patients needed for an adequately powered psychotherapy trial.
I so relish the extra boost of enthusiasm that many clinical and health psychology students bring to their PhD projects. They not only want to complete a thesis of which they can be proud, they want their results to be directly applicable to improving the lives of their patients.
Many students are particularly excited about a new psychotherapy about which extravagant claims are being made that it’s better than its rivals.
I have seen lots of fad and fashions come and go, third wave, new wave, and no wave therapies. When I was a PhD student, progressive relaxation was in. Then it died, mainly because it was so boring for therapists who had to mechanically provide it. Client centered therapy was fading with doubts that anyone else could achieve the results of Carl Rogers or that his three facilitative conditions of unconditional positive regard, genuineness, and congruence were actually distinguishable enough to study. Gestalt therapy was supercool because of the charisma of Fritz Perls, who distracted us with his showmanship from the utter lack of evidence for its efficacy.
I hate to see PhD students demoralized when their grand plans prove unrealistic. Inevitably, circumstances force them to compromise in ways that limit any usefulness to their project, and maybe even threaten their getting done within a reasonable time period. Overly ambitious plans are the formidable enemy of the completed dissertation.
The numbers are stacked against a PhD student conducting an adequately powered evaluation of a new psychotherapy.
This blog post argues against PhD students taking on the evaluation of a new therapy in comparison to an existing one, if they expect to complete their projects and make meaningful contribution to the literature and to patient care.
I’ll be drawing on some straightforward analysis done by Pim Cuijpers to identify what PhD students are up against when trying to demonstrate that any therapy is better than treatments that are already available.
Pim has literally done dozens of meta-analyses, mostly of treatments for depression and anxiety. He commands a particular credibility, given the quality of this work. The way Pim and his colleagues present a meta-analysis is so straightforward and transparent that you can readily examine the basis of what he says.
Disclosure: I collaborated with Pim and a group of other authors in conducting a meta-analysis as to whether psychotherapy was better than a pill placebo. We drew on all the trials allowing a head-to-head comparison, even though nobody ever really set out to pit the two conditions against each other as their first agenda.
Pim tells me that the brief and relatively obscure letter, New Psychotherapies for Mood and Anxiety Disorders: Necessary Innovation or Waste of Resources? on which I will draw is among his most unpopular pieces of work. Lots of people don’t like its inescapable message. But I think that if PhD students should pay attention, they might avoid a lot of pain and disappointment.
Note how many psychotherapies have been claimed to be effective for depression and anxiety. Anyone trying to make sense of this literature has to contend with claims being based on a lot of underpowered trials– too small in sample size to be expected reasonably to detect the effects that investigators claim – and that are otherwise compromised by methodological limitations.
Some investigators were simply naïve about clinical trial methodology and the difficulties doing research with clinical populations. They may have not understand statistical power.
But many psychotherapy studies end up in bad shape because the investigators were unrealistic about the feasibility of what they were undertaken and the low likelihood that they could recruit the patients in the numbers that they had planned in the time that they had allotted. After launching the trial, they had to change strategies for recruitment, maybe relax their selection criteria, or even change the treatment so it was less demanding of patients’ time. And they had to make difficult judgments about what features of the trial to drop when resources ran out.
Declaring a psychotherapy trial to be a “preliminary” or a “pilot study” after things go awry
The titles of more than a few articles reporting psychotherapy trials contain the apologetic qualifier after a colon: “a preliminary study” or “a pilot study”. But the studies weren’t intended at the outset to be preliminary or pilot studies. The investigators are making excuses post-hoc – after the fact – for not having been able to recruit sufficient numbers of patients and for having had to compromise their design from what they had originally planned. The best they can hope is that the paper will somehow be useful in promoting further research.
Too many studies from which effect sizes are entered into meta-analyses should have been left as pilot studies and not considered tests of the efficacy of treatments. The rampant problem in the psychotherapy literature is that almost no one treats small scale trials as mere pilot studies. In a recent blog post, I provided readers with some simple screening rules to identify meta-analyses of psychotherapy studies that they could dismiss from further consideration. One was whether there were sufficient numbers of adequately powered studies, Often there are not.
Readers take their inflated claims of results of small studies seriously, when these estimates should be seen as unrealistic and unlikely to be replicated, given a study’s sample size. The large effect sizes that are claimed are likely the product of p-hacking and the confirmation bias required to get published. With enough alternative outcome variables to choose from and enough flexibility in analyzing and interpreting data, almost any intervention can be made to look good.
The problem is is readily seen in the extravagant claims about acceptance and commitment therapy (ACT), which are so heavily dependent on small, under-resourced studies supervised by promoters of ACT that should not have been used to generate effect sizes.
Back to Pim Cuijpers’ brief letter. He argues, based on his numerous meta-analyses, that it is unlikely that a new treatment will be substantially more effective than an existing credible, active treatment. There are some exceptions like relaxation training versus cognitive behavior therapy for some anxiety disorders, but mostly only small differences of no more than d= .20 are found between two active, credible treatments. If you search the broader literature, you can find occasional exceptions like CBT versus psychoanalysis for bulimia, but most you find prove to be false positives, usually based on investigator bias in conducting and interpreting a small, underpowered study.
You can see this yourself using the freely downloadable G*power program and plug in d= 0.20 for calculating the number of patients needed for a study. To be safe, add more patients to allow for the expectable 25% dropout rate that has occurred across trials. The number you get would require a larger study than has ever been done in the past, including the well-financed NIMH Collaborative trial.
Even more patients would be needed for the ideal situation in which a third comparison group allowed the investigator to show the active comparison treatment had actually performed better than a nonspecific treatment that was delivered with the same effectiveness that the other had shown in earlier trials. Otherwise, a defender of the established therapy might argue that the older treatment had not been properly implemented.
So, unless warned off, the PhD student plans a study to show not only that now hypothesis can be rejected that the new treatment is no better than the existing one, but that in the same study the existing treatment had been shown to be better than wait list. Oh my, just try to find an adequately powered, properly analyzed example of a comparison of two active treatments plus a control comparison group in the existing published literature. The few examples of three group designs in which a new psychotherapy had come out better than an effectively implemented existing treatment are grossly underpowered.
These calculations so far have all been based on what would be needed to reject the null hypothesis of no difference between the active treatment and a more established one. But if the claim is that the new treatment is superior to the existing treatment, our PhD student now needs to conduct a superiority trial in which some criteria is pre-set (such as greater than a moderate difference, d= .30) and the null hypothesis is that the advantage of the new treatment is less. We are now way out into the fantasyland of breakthrough, but uncompleted dissertation studies.
Two take away messages
The first take away message is that we should be skeptical of claims of the new treatment is better than past ones except when the claim occurs in a well-designed study with some assurance that it is free of investigator bias. But the claim also has to arise in a trial that is larger than almost any psychotherapy study is ever been done. Yup, most comparative psychotherapy studies are underpowered and we cannot expect robust claims are robust that one treatment is superior to another.
But for PhD students been doing a dissertation project, the second take away message is that they should not attempt to show that one treatment is superior to another in the absence of resources they probably don’t have.
The psychotherapy literature does not need another study with too few patients to support its likely exaggerated claims.
An argument can be made that it is unfair and even unethical to enroll patients in a psychotherapy RCT with insufficient sample size. Some of the patients will be randomized to the control condition that is not what attracted them to the trial. All of the patients will be denied having been in a trial makes a meaningful contribution to the literature and to better care for patients like themselves.
What should the clinical or health psychology PhD student do, besides maybe curb their enthusiasm? One opportunity to make meaningful contributions to literature by is by conducting small studies testing hypotheses that can lead to improvement in the feasibility or acceptability of treatments to be tested in studies with more resources.
Think of what would’ve been accomplished if PhD students had determined in modest studies that it is tough to recruit and retain patients in an Internet therapy study without some communication to the patients that they are involved in a human relationship – without them having what Pim Cuijpers calls supportive accountability. Patients may stay involved with the Internet treatment when it proves frustrating only because they have the support and accountability to someone beyond their encounter with an impersonal computer. Somewhere out there, there is a human being who supports them and sticking it out with the Internet psychotherapy and will be disappointed if they don’t.
A lot of resources have been wasted in Internet therapy studies in which patients have not been convinced that what they’re doing is meaningful and if they have the support of a human being. They drop out or fail to do diligently any homework expected of them.
Similarly, mindfulness studies are routinely being conducted without anyone establishing that patients actually practice mindfulness in everyday life or what they would need to do so more consistently. The assumption is that patients assigned to the mindfulness diligently practice mindfulness daily. A PhD student could make a valuable contribution to the literature by examining the rates of patients actually practicing mindfulness when the been assigned to it in a psychotherapy study, along with barriers and facilitators of them doing so. A discovery that the patients are not consistently practicing mindfulness might explain weaker findings than anticipated. One could even suggest that any apparent effects of practicing mindfulness were actually nonspecific, getting all caught up in the enthusiasm of being offered a treatment that has been sought, but not actually practicing mindfulness.
An unintended example: How not to recruit cancer patients for a psychological intervention trial
Sometimes PhD students just can’t be dissuaded from undertaking an evaluation of a psychotherapy. I was a member of a PhD committee of a student who at least produced a valuable paper concerning how not to recruit cancer patients for a trial evaluating problem-solving therapy, even though the project fell far short of conducting an adequately powered study.
The PhD student was aware that claims of effectiveness of problem-solving therapy reported in in the prestigious Journal of Consulting and Clinical Psychology were exaggerated. The developer of problem-solving therapy for cancer patients (and current JCCP Editor) claimed a huge effect size – 3.8 if only the patient were involved in treatment and an even better 4.4 if the patient had an opportunity to involve a relative or friend as well. Effect sizes for this trial has subsequently had to be excluded from at least meta-analyses as an extreme outlier (1,2,3,4).
The student adopted the much more conservative assumption that a moderate effect size of .6 would be obtained in comparison with a waitlist control. You can use G*Power to see that 50 patients would be needed per group, 60 if allowance is made for dropouts.
Such a basically inert control group, of course, has a greater likelihood of seeming to demonstrate a treatment is effective than when the comparison is another active treatment. Of course, such a control group also has the problem of not allowing a determination if it was the active ingredient of the treatment that made the difference, or just the attention, positive expectations, and support that were not available in the waitlist control group.
But PhD students should have the same option as their advisors to contribute another comparison between an active treatment and a waitlist control to the literature, even if it does not advance our knowledge of psychotherapy. They can take the same low road to a successful career that so many others have traveled.
This particular student was determined to make a different contribution to the literature. Notoriously, studies of psychotherapy with cancer patients often fail to recruit samples that are distressed enough to register any effect. The typical breast cancer patient, for instance, who seeks to enroll in a psychotherapy or support group trial does not have clinically significant distress. The prevalence of positive effects claimed in the literature for interventions with cancer patients in published studies likely represents a confirmation bias.
The student wanted to address this issue by limiting patients whom she enrolled in the study to those with clinically significant distress. Enlisting colleagues, she set up screening of consecutive cancer patients in oncology units of local hospitals. Patients were first screened for self-reported distress, and, if they were distressed, whether they were interested in services. Those who met both criteria were then re-contacted to see if that be willing to participate in a psychological intervention study, without the intervention being identified. As I reported in the previous blog post:
- Combining results of the two screenings, 423 of 970 patients reported distress, of whom 215 patients indicated need for services.
- Only 36 (4% of 970) patients consented to trial participation.
- We calculated that 27 patients needed to be screened to recruit a single patient, with 17 hours of time required for each patient recruited.
- 41% (n= 87) of 215 distressed patients with a need for services indicated that they had no need for psychosocial services, mainly because they felt better or thought that their problems would disappear naturally.
- Finally, 36 patients were eligible and willing to be randomized, representing 17% of 215 distressed patients with a need for services.
- This represents 8% of all 423 distressed patients, and 4% of 970 screened patients.
So, the PhD student’s heroic effort did not yield the sample size that she anticipated. But she ended up making a valuable contribution to the literature that challenges some of the basic assumptions that were being made about how cancer patients in psychotherapy research- that all or most were distressed. She also ended up producing some valuable evidence that the minority of cancer patients who report psychological distress are not necessarily interested in psychological interventions.
Fortunately, she had been prepared to collect systematic data about these research questions, not just scramble within a collapsing effort at a clinical trial.
Becoming a research parasite as an alternative to PhD students attempting an under-resourced study of their own
Psychotherapy trials represent an enormous investment of resources, not only the public funding that is often provided for them, but in the time, inconvenience, and exposure to ineffective treatments experienced by patients who participate in the trials. Increasingly, funding agencies require that investigators who get money to do a psychotherapy study some point make their data available for others to use. The 14 prestigious medical journals whose editors make up the International Committee of Medical Journal Editors (ICMJE) each published in earlier in 2016 a declaration that:
there is an ethical obligation to responsibly share data generated by interventional clinical trials because participants have put themselves at risk.
These statements proposed that as a condition for publishing a clinical trial, investigators would be required to share with others appropriately de-identified data not later than six months after publication. Further, the statements proposed that investigators describe their plans for sharing data in the registration of trials.
Of course, a proposal is only exactly that, a proposal, and these requirements were intended to take effect only after the document is circulated and ratified. The incomplete and inconsistent adoption of previous proposals for registering of trials in advance and investigators making declarations of conflicts of interest do not encourage a lot of enthusiasm that we will see uniform implementation of this bold proposal anytime soon.
Some editors of medical journals are already expressing alarmover the prospect of data sharing becoming required. The editors of New England Journal of Medicine were lambasted in social media for their raising worries about “research parasites” exploiting the availability of data:
a new class of research person will emerge — people who had nothing to do with the design and execution of the study but use another group’s data for their own ends, possibly stealing from the research productivity planned by the data gatherers, or even use the data to try to disprove what the original investigators had posited. There is concern among some front-line researchers that the system will be taken over by what some researchers have characterized as “research parasites.”
Richard Lehman’s Journal Review at the BMJ ‘s blog delivered a brilliant sarcastic response to these concerns that concludes:
I think we need all the data parasites we can get, as well as symbionts and all sorts of other creatures which this ill-chosen metaphor can’t encompass. What this piece really shows, in my opinion, is how far the authors are from understanding and supporting the true opportunities of clinical data sharing.
However, lost in all the outrage that The New England Journal of Medicine editorial generated was a more conciliatory proposal at the end:
How would data sharing work best? We think it should happen symbiotically, not parasitically. Start with a novel idea, one that is not an obvious extension of the reported work. Second, identify potential collaborators whose collected data may be useful in assessing the hypothesis and propose a collaboration. Third, work together to test the new hypothesis. Fourth, report the new findings with relevant coauthorship to acknowledge both the group that proposed the new idea and the investigative group that accrued the data that allowed it to be tested. What is learned may be beautiful even when seen from close up.
The PLOS family of journals has gone on record as requiring that all data for papers published in their journals be publicly available without restriction.A February 24, 2014 PLOS’ New Data Policy: Public Access to Data declared:
In an effort to increase access to this data, we are now revising our data-sharing policy for all PLOS journals: authors must make all data publicly available, without restriction, immediately upon publication of the article. Beginning March 3rd, 2014, all authors who submit to a PLOS journal will be asked to provide a Data Availability Statement, describing where and how others can access each dataset that underlies the findings. This Data Availability Statement will be published on the first page of each article.
Many of us are aware of the difficulties in achieving this lofty goal. I am holding my breath and turning blue, waiting for some specific data.
The BMJ has expanded their previous requirements for data being available:
Loder E, Groves T. The BMJ requires data sharing on request for all trials. BMJ. 2015 May 7;350:h2373.
The movement to make data from clinical trials widely accessible has achieved enormous success, and it is now time for medical journals to play their part. From 1 July The BMJ will extend its requirements for data sharing to apply to all submitted clinical trials, not just those that test drugs or devices. The data transparency revolution is gathering pace.
I am no longer heading dissertation committees after one that I am currently supervising is completed. But if any PhD students asked my advice about a dissertation project concerning psychotherapy, I would strongly encourage them to enlist their advisor to identify and help them negotiate access to a data set appropriate to the research questions they want to investigate.
Most well-resourced psychotherapy trials have unpublished data concerning how they were implemented, with what bias and with which patient groups ending up underrepresented or inadequately exposed to the intensity of treatment presumed to be needed for benefit. A story awaits to be told. The data available from a published trial are usually much more adequate than then any graduate student could collect with the limited resources available for a dissertation project.
I look forward to the day when such data is put into a repository where anyone can access it.
In this blog post I have argued that PhD students should not take on responsibility for developing and testing a new psychotherapy for their dissertation project. I think that using data from existing published trials is a much better alternative. However, PhD students may currently find it difficult, but certainly not impossible to get appropriate data sets. I certainly am not recruiting them to be front-line infantry in advancing the cause of routine data sharing. But they can make an effort to obtain such data and they deserve all support they can get from their dissertation committees in obtaining data sets and in recognizing when realistically that data are not being made available, even when the data have been promised to be available as a condition for publishing. Advisors, please request the data from published trials for your PhD students and protect them from the heartache of trying to collect such data themselves.