1 Boring Old Man

When I started nosing around, I found all kinds of interesting and strange things. There was wide variability in efficacy among countries and doses.

The FDA reviewer recommended against approval, but was over-ridden by the FDA committee [see ought to know by now…, in the shadows…, wait…]. The Director of the FDA wrote a special report justifying that action. The «published» study was coordinated by the shakiest of clinical research centers [see hiding uptown…, hiding uptown [more]…].

I didn’t give Latuda® much thought after that. I had noticed in their signature before-and-after ads that Latuda®  apparently moves the part from one side of the head to the other like a mirror. So when one of those ads popped up, I always noticed. But otherwise, Latuda® was off my radar. And then it was February 2014, and I looked at the American Journal of Psychiatry and there were two articles about Latuda® Clinical Trials in the same issue and there  was an editorial about it. You guessed it, a Bipolar Disorder indication. Then, I wrote [from or both…]:

This month’s American Journal of Psychiatry has two articles reporting clinical trials on Lurasidone [Latuda®] as either monotherapy [Lurasidone Monotherapy in the Treatment of Bipolar I Depression: A Randomized, Double-Blind, Placebo-Controlled Study] or as an adjunct to mood stabilizers [Lurasidone as Adjunctive Therapy With Lithium or Valproate for the Treatment of Bipolar I Depression: A Randomized, Double-Blind, Placebo-Controlled Study] in Major Depressions associated with Bipolar Disorder. The trials were both sponsored by Sunovion Pharmaceuticals, were both registered on clinical trials.gov on the same day, and share a number of their many trial sites [NCT00868699 (71 sites) & NCT00868452 (55 sites)]. All except the last author for both articles are Sunovion employees «7 authors each»… Dr. Sachs directs a Mood Disorders clinic at Mass General. Dr. Calebrese directs one at Case Western Reserve. Both are consultants for Sunovion and multiple other pharmaceutical companies.

Tuesday, after a long morning working in the clinic, I was awakened from a recovery nap by a mid-afternoon phone call. It was the FDA calling. The lady on the phone told me she’d seen I’d made other requests, but they were for "older drugs" [I wasn’t liking how this was sounding so far]. She said it was different for recent approvals. I didn’t follow all of what she was saying, but she seemed to be trying to dissuade me from pursuing my request. She said it had to go through several processes one of which was "disclosures" and there might be a charge. Did I still want to stay in the queue? By this time I was awake enough to say, "Sure. How long is the usual wait?" She said "18 to 24 months. You know, we get over 3000 requests every year." long pause. I said "Okay" [having given up with taking out my frustrations with big systems on small bureaucrats long ago]. But when I was fully awake and thought about it, I think I learned something, and have to revise my theory. My now fading enthusiasm for the FDA had been based on experiences where what I was requesting was old hat, and just required copying a disk from somebody else’s previous request. The fantasy that I could get the information on a recent approval in a timely fashion was naive. This drug is going to be well down the line in it’s patent life before I ever see that report, just like before.

There’s a point to this narrative. I went looking for how Latuda® was doing on the market. What about sales? That took me to various business sites, and somewhere on that road I read that Latuda® prescriptions were up 29% since the Bipolar approval. I finally ended up on the Dainippon Sumitomo Pharma Co., Ltd. site [Sunovion is owned by this Japanese firm]. And going through the yearly financial reports, I came up with this table [ballpark figures]:

It looks like it’s headed for "Blockbuster" status next year [$1 B/YR]. So here’s a late-comer that dribbled out of the pipeline just as the spigot was being turned off. It’s competing with its atypical predecessors and the first generation antipsychotics, all of which are available as less expensive generics. And it’s at the bottom of the efficacy list in the only meta-analysis that I know of that includes it [see the mother of meta…]

:

And yet it got through the FDA with an 11th hour intervention by the FDA Director. It had three published Clinical Trial articles and an editorial that made it into the prestigious American Journal of Psychiatry. So it seems to be having an unusually charmed life, and is selling like hotcakes. As I said, these Sunovion people really seem to know how to sell some drugs [Sales Force Report: A Walk on the Sales Side].

That point I said I was aiming for is that while Latuda® is zooming up the ladder in sales and usage, I’m measuring my ability to find out about its safety and efficacy literally in years. So long as that’s the case, it’s going to be the pharmaceutical industry’s sales force that will exert the major influence on which drugs are prescribed – rather than the plain clinical facts. There’s no reason in the world for the FDA data seen by the reviewers to need anything except a postage stamp to get it to anyone else qualified to take a look. If there’s commercially confidential information on a drug, don’t put it in the material submitted. If there’s data that needs to be anonymized, do it before the submission. The FDA has more important things to do than calling some old man in the middle of a perfectly good nap to try to talk him out of wanting what he wants. Leave the keeping secrets business for the people in Langley Virginia where it makes sense…

Afterthought: When I looked back over this, I realized I left out something that belongs here. I think new has a high value. There are plenty of patients who have cycled through many of the available meds and are shopping for something new. And Latuda® came after an empty space – was the last something new on the block punctuating the dry pipeline meme. And I would recommend reading that article on drug rep technology [Sales Force Report: A Walk on the Sales Side]…

Of course 1boringoldman isn’t in the CME business and certainly can’t offer any kind of credits, but reading the articles in the series above [all freely available full text on-line] is definitely a valuable educational experience about any number of things.

Telomere length and cortisol reactivity in children of depressed mothers

by I H Gotlib, J LeMoult, N L Colich, L C Foland-Ross, J Hallmayer, J Joormann, J Lin and O M Wolkowitz

Molecular Psychatry. doi: 10.1038/mp.2014.119.

[full text on-line]

A growing body of research demonstrates that individuals diagnosed with major depressive disorder (MDD) are characterized by shortened telomere length, which has been posited to underlie the association between depression and increased instances of medical illness. The temporal nature of the relation between MDD and shortened telomere length, however, is not clear. Importantly, both MDD and telomere length have been associated independently with high levels of stress, implicating dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis and anomalous levels of cortisol secretion in this relation. Despite these associations, no study has assessed telomere length or its relation with HPA-axis activity in individuals at risk for depression, before the onset of disorder. In the present study, we assessed cortisol levels in response to a laboratory stressor and telomere length in 97 healthy young daughters of mothers either with recurrent episodes of depression (i.e., daughters at familial risk for depression) or with no history of psychopathology. We found that daughters of depressed mothers had shorter telomeres than did daughters of never-depressed mothers and, further, that shorter telomeres were associated with greater cortisol reactivity to stress. This study is the first to demonstrate that children at familial risk of developing MDD are characterized by accelerated biological aging, operationalized as shortened telomere length, before they had experienced an onset of depression; this may predispose them to develop not only MDD but also other age-related medical illnesses. It is critical, therefore, that we attempt to identify and distinguish genetic and environmental mechanisms that contribute to telomere shortening.

What can be done to reduce the risk of depression? In previous experiments, girls at risk for depression exhibited different patterns of brain activation during experimental mood regulation. In ongoing experiments, the Gotlib team is using neurofeedback to help these girls retrain their brain circuits and hopefully their stress responses. It will be a few years before we will know how much this intervention reduces risk for depression, but anything that prevents or slows the telomere shortening may be an early indication of success.

In this two-part blog post, I’ll document this process of amplification of the distortion of science from article to press release to subsequent coverage. In the first installment, I’ll provide a walkthrough commentary and critique of a flawed small study of telomere length among daughters of depressed women published in the prestigious Nature Publishing Group journal, Molecular Psychiatry. In the second, I will compare the article and press release to media coverage, specifically the personal blog of NIMH Director Thomas Insel. I warn the squeamish that I will whack some bad science and outrageous assumptions with demands for evidence and pelt the study, its press release, and Insel’s interpretation with contradictory evidence.  I’m devoting a two-part blog to this effort. Bad science with misogynist, mother bashing assumptions is being touted by the Director of NIMH as an example to be followed.

NIMH Director Thomas Insel should stick to his knitting. He has no business setting scientific directions for the field, as with the RDoC mandates. I suppose it is an occupational hazard of administrators to prefer a top-down management style. Problem is, science is a bottom-up business. His shallow hyping of the Gotlib-Wolkowitz report is a further reason to disqualify him.

Dr. Insel is on record with an amateurish view of how clinical science moves forward. See PubMed #22869033. When he says we should sidestep the issue of a gold standard he plainly doesn’t understand the iterative process of nosology or the related concept of convergent validity. He doesn’t seem to understand the need to advance nosology by incorporating biomarkers along with clinical symptoms in diagnostic definitions, as happened in general medicine. And he shows no understanding of the need for a Bayesian perspective on the interpretation of biomarkers as well as of definitional symptoms. When he proposes tracking biological markers across current diagnostic domains he ignores the central issue of pathophysiology. Consider where we would end up if, for instance, we lumped Cushing disease together with juvenile onset diabetes mellitus, Type II diabetes mellitus, severe psychological or physiological stress, metabolic syndrome, anorexia nervosa, and pregnancy on the basis of an abnormal glucose tolerance test, which these all can display.

The RDoC matrix that Dr. Insel insists should be adopted in new grant proposals to NIMH is a classic product of armchair scholastic theorizing. We are already seeing researchers bend like pretzels to make their proposals and journal submissions appear RDoC-friendly. The effect is contrived, to put it mildly. We can expect that the RDoC matrix eventually will go the way of the eccentrics and epicycles of 16th Century astronomy. The pressing issue is how much damage will be done before that happens? Director Insel and his lieutenants at NIMH need to get out of the way – their presumptuousness is breathtaking.

Now, we’ve arrived at a point in time where the published results from a number of these articles are being questioned, and the broad consensus is that the raw data from these articles needs to be reanalyzed by independent investigators. It’s not just an academic interest that drives this call for reanalysis. It’s because these drugs remain in wide use based on the efficacy and safety results published in those articles – often certified by the FDA in the drug approval process.

Sharing Older Clinical Trial Data Remains a Problem for Pharma

Pharmalot: WSJ

By Ed Silverman

Jan 29, 2015

With considerable fanfare, the Institute of Medicine released a long-awaited report last month praising the virtues of sharing clinical trial data. This is an important, but also contentious issue because without access to such information it can be virtually impossible for independent researchers to verify results that can lead to improved treatments, better health care and lower costs. Although the report is nothing more than a set of recommendations, the IOM effort is, nonetheless, seen as a needed step toward prodding industry and academia to release detailed data. Many drug makers, in particular, have been reluctant to grant much, if any, access over concerns about relinquishing trade secrets and compromising patient privacy, among other things. But some have taken steps to do so.

There is, however, one area where some experts say the report falls short and underscores ongoing difficulties in sharing trial information. While the IOM offers specific suggestions for releasing data from future trials, the institute did not provide a framework for obtaining retrospective data. This remains an unresolved issue for the pharmaceutical industry, especially in light of various safety scandals and ensuing litigation that revealed data for some products were never fully published or disclosed. “Clinical trials underpin the approval of medicines that are in use today, so if we don’t have access to that older data, how can we verify accuracy and conduct independent analyses?” says Peter Doshi, an assistant professor of pharmaceutical health services at the University of Maryland. “It’s a basic issue surrounding data transparency.”

One IOM committee member, Ida Sim, a professor at the University of California, San Francisco, says the report does suggest sharing retrospective data, but on a case-by-case basis and for studies that may influence clinical care. Why? Informed consent must be obtained from people who participated years earlier. Often, researchers have scattered. “We have to be pragmatic and realistic.”

Some say convenience and liability are also factors. “Obviously, it’s less burdensome for companies to only have to do so going forward, especially if there’s a product on the market that’s raising concerns,” says Diana Zuckerman, who heads the National Center for Health Research, a non-profit think tank. “I see it as a way to protect companies from disclosing embarrassing and expensive information.”

Murray Stewart, however, argues progress is being made. As chief medical officer at GlaxoSmithKline, he oversees an effort to disclose data going back 15 years and has convinced nine other drug makers to similarly provide access through a jointly run website. But most will not provide retrospective data. “We’ve shown it can be done,” he says. “But it’s a long journey” getting others on board. The inconsistent approaches underscore a key issue – finding a universal gatekeeper to vet researcher requests and corporate concerns. Harlan Krumholz, a Yale University cardiologist, is trying to offer a template with the Yale Open Data Access project, which last month signed an agreement with Johnson & Johnson to provide data for diagnostic and device products, but only as of 2014. A deal that J&J signed with YODA last year covers drugs and does not have a cut-off date for data requests. So far, J&J has not refused any request, but Krumholz says any J&J refusal would be disclosed on the site. “We are open science advocates,” he says, “and are not interested in giving anyone a pass.”

Meanwhile, some say the FDA has been a hindrance. While European regulators have a policy that allows researchers to make requests, the FDA does not. An FDA spokeswoman says the agency published a notice seeking comment about ways to make trial data available. But that was nearly two years ago. She says reactions are being evaluated and next steps are being considered. “I think FDA should make as much information available as possible, as soon as possible, particularly for approved drugs, including any safety information that might not be in the primary trials,” says Steve Goodman, a Stanford University School of Medicine professor. “What we need to avoid is the situation where the FDA is in possession of information that could materially impact or alter the assessment of the relative benefits and harms of a drug, and that is not easily available to other scientists or the public.” And “information they have on ‘failed’ drugs can often have an impact on marketed drugs or on other new drugs in the same class, so such information can be quite important as well, even though currently little or no information about such drugs can be divulged.”

ra·tio·nal·ize   rash-na-liz
verb

: to think about or describe something [such as bad behavior] in a way that explains it and makes it seem proper, more attractive, etc.

Depression is not a consistent syndrome: An investigation of unique symptom patterns in the STAR*D study

by Eiko I. Fried and Randolph M. Nesse

Journal of Affective Disorders. 2014 172C:96-102.

[full text on-line] 

At my last count there were 19 possible symptoms in DSM-IV for major depressive disorder, with only 5 needed for the diagnosis. You do the math…

po·ly·the·tic  polí’θetik
adjective

Relating to or sharing a number of characteristics which occur commonly in members of a group or class, but none of which is essential for membership of that group or class.

monothetic / polythetic classification

• A monothetic class is defined in terms of characteristics that are both necessary and sufficient in order to identify members of that class. This way of defining a class is also termed the Aristotelian definition of a class.
• A polythetic class is defined in terms of a broad set of criteria that are neither necessary nor sufficient. Each member of the category must possess a certain minimal number of defining characteristics, but none of the features has to be found in each member of the category. This way of defining classes is associated with Wittgenstein’s concept of "family resemblances."

Quantifying Heterogeneity Attributable to Polythetic Diagnostic Criteria: Theoretical Framework and Empirical Application

by Charles Olbert, Gary Gala, and Larry Tupler

Journal of Abnormal Psychology. 2014 123[2]:452-62.

Heterogeneity within psychiatric disorders is both theoretically and practically problematic: For many disorders, it is possible for 2 individuals to share very few or even no symptoms in common yet share the same diagnosis. Polythetic diagnostic criteria have long been recognized to contribute to this heterogeneity, yet no unified theoretical understanding of the coherence of symptom criteria sets currently exists. A general framework for analyzing the logical and mathematical structure, coherence, and diversity of Diagnostic and Statistical Manual diagnostic categories [DSM-5 and DSM-IV-TR] is proposed, drawing from combinatorial mathematics, set theory, and information theory. Theoretical application of this framework to 18 diagnostic categories indicates that in most categories, 2 individuals with the same diagnosis may share no symptoms in common, and that any 2 theoretically possible symptom combinations will share on average less than half their symptoms. Application of this framework to 2 large empirical datasets indicates that patients who meet symptom criteria for major depressive disorder and posttraumatic stress disorder tend to share approximately three-fifths of symptoms in common. For both disorders in each of the datasets, pairs of individuals who shared no common symptoms were observed. Any 2 individuals with either diagnosis were unlikely to exhibit identical symptomatology. The theoretical and empirical results stemming from this approach have substantive implications for etiological research into, and measurement of, psychiatric disorders.

Long ago, before the blood pressure cuff, doctors approximated blood pressure readings by feeling the "hardness of the pulse." Before the thermometer, fever was measured with the back of the hand. Anemia was diagnosed by looking at the color on the back of the eyelid, and diabetes by tasting the urine. You do what you can do until something better comes along. What’s important is to know the precision and limits of the instrument, and not make more of it than it has to offer. The symptomatic, descriptive system worked in a few places, but was heavily overvalued in others. That was apparent on day one, yet it got reified and deified early on for reasons other than clinical accuracy. Worse, it was used by many to imply unproven unity and even etiology.

• Malignant Hypertension: The patients would arrive in the ER delirious or in coma with outrageous blood pressure readings. They would have retinal hemorrhages and other vascular eye signs. Kidney function would be compromised. A "stroke" was eminent if it hadn’t happened already. It was a medical emergency, and at the time, the drug of choice was parenteral Reserpine, which did the job. The easiest transition was to oral Reserpine, but the problem was that some fraction of the patients developed a profound melancholic depression [one of the phenomena that lead to the "catecholamine hypothesis" of depression]. So we used the other available drugs of the time – all with plenty of side effects as part of the package.
• Hypertensive Cardiovascular Disease: These patients were more common. They showed up in the clinics and ER with congestive heart failure of varying intensity: swollen legs, enlarged livers, shortness of breath, sleeping sitting up – with enlarged hearts and Left Ventricular Hypertrophy on EKG [big muscular hearts]. The symptoms cleared with lowering the blood pressure, but they usually got digitalis and diuretics as well.
• Hypertension: Back then, Hypertension [asymptomatic] was defined as a Diastolic Blood Pressure consistently over 100 mmHg. And there was plenty enough of that around to treat where I was.

Can This Treatment Help Me? There’s a Statistic for That

New York Times

4 heart attacks are not prevented. When 2,000 People Take a Daily Aspirin for Two Years: 1 Heart Attack is Prevented. People at risk for a first heart attack are often recommended to take aspirin daily to prevent it. Only a very few will actually see this benefit and there’s no way to know in advance who.

By Austin Frakt and Aaron E. Carroll

JAN. 26, 2015

In his State of the Union address last week, President Obama encouraged the development of “precision medicine,” which would tailor treatments based on individuals’ genetics or physiology. This is an effort to improve medical care’s effectiveness, which might cause some to wonder: Don’t we already have effective drugs and treatments? In truth, medical care is often far less effective than most believe. Just because you took some medicine for an illness and became well again, it doesn’t necessarily mean that the treatment provided the cure.

This fundamental lesson is conveyed by a metric known as the number needed to treat, or N.N.T. Developed in the 1980s, the N.N.T. tells us how many people must be treated for one person to derive benefit. An N.N.T. of one would mean every person treated improves and every person not treated fails to, which is how we tend to think most therapies work. What may surprise you is that N.N.T.s are often much higher than one. Double- and even triple-digit N.N.T.s are common.

Consider aspirin for heart attack prevention. Based upon both modifiable risk factors like cholesterol level and smoking, and factors that are beyond one’s control, like family history and age, it is possible to calculate the chance that a person will have a first heart attack in the next 10 years. The American Heart Association recommends that people who have more than a 10 percent chance take a daily aspirin to avoid that heart attack.

How effective is aspirin for that aim? According to clinical trials, if about 2,000 people follow these guidelines over a two-year period, one additional first heart attack will be prevented. That doesn’t mean the 1,999 other people have heart attacks. The fact is, on average about 3.6 of them would have a first heart attack regardless of whether they took the aspirin. Even more important, 1,995.4 people would never have a heart attack whether or not they took aspirin. Only one person is actually affected by aspirin. If he takes it, the number of people who remain heart attack-free rises to 1996.4. If he doesn’t, the number remains 1995.4. But for 1,999 of the 2,000 people, aspirin doesn’t make any difference at all.

Of course, nobody knows if they’re the lucky one for whom aspirin is helpful. So, if aspirin is cheap and doesn’t cause much harm, it might be worth taking, even if the chances of benefit are small. But this already reflects a trade-off we rarely consider rationally. [And many treatments do cause harm. There is a complementary metric known as the number needed to harm, or N.N.H., which says that if that number of people are treated, one additional person will have a specific negative outcome. For some treatments, N.N.T. can be higher than the number needed to harm, indicating more people are harmed than successfully treated.]

Not all N.N.T.s are as high as aspirin’s for heart attacks, but many are higher than you might think. A website developed by David Newman, a director of clinical research at Icahn School of Medicine at Mount Sinai hospital, and Dr. Graham Walker, an assistant clinical professor at the University of California, San Francisco, has become a clearinghouse of N.N.T. data, amassed from clinical trials…

The article is about the simplest of all statistics. If there’s a Clinical Trial where 40% respond to placebo and 50% respond to the drug, then the NNT = 1 ÷ (0.50 – 0.40) = 10. That literally means "you have to treat 10 cases to get 1 responder." Actually, 5 would respond, but only 1 would be because of the treatment. The other 4 were going to respond anyway. What could be simpler? And what could be more telling? The NNT is one of a family of measures of the Strength of the Effect of a treatment [see an anatomy of a deceit 3…].

In the days when those ancients first disuncovered the rules of logic and logical argument, they must’ve thought they’d found the path to determine absolutes, something like the truth. Alas, it was short-lived, because the senator on the other side of the forum rose and eloquently used those self-same rules of logic and logical argument to reach a diametrically opposite conclusion. And so the march began that lead to laws, the legal profession, courts of law with their judges and juries, our legislatures, etc. – all of those institutions involved in parsing a solution out of human disagreement before warring people end up shooting at each other. And somewhere along this path, there was a new class in the study of logic called logical fallacies – situations where logical arguments are chronically distorted or misused.

So I would encourage Dr. Sim and her colleagues to reconsider playing into this industry manufactured argument and helping medicine get things back on track…

1bom: …we can look at the raw data from the original trials. And fortunately, the sleight of hand occurred primarily in the analytic and publication processes that came after the blinds were broken. So those legacy trials are the very ones that need to be reanalyzed and meta-analyzed by independent investigators playing with a full deck. Without an accurate and very public re-appraisal, the problem is going to be perpetuated for decades.
response: The IOM report fully endorses this type of reasoning for prioritizing the sharing of certain legacy trials. It just wasn’t up to our committee to call out which legacy trials in particular, or to propose a process for prioritization.

But in thinking about all of this, I realized that my argument flies mighty close to one of those logical fallacies I was mentioning – a special pleading. It’s one of those fallacies our poor judges have to listen to day after day. It goes something like this: "I know that the law says «something the law says», but it doesn’t apply to me because «something their lawyer made up to say»." And, if I’m honest, I’m really arguing that although I can see why subject confidentiality might concern the committee in releasing individual patient data, the legacy Clinical Trials in psychiatry are a special case that justifies whatever measures are necessary to protect subject confidentiality and make that data available to independent investigators. I’m arguing that the distortion of scientific data in the psychiatric drug literature was so blatant, so widespread, and so damaging that it needs to be made available to the medical community at large for systematic examination for any number of reasons – at any cost.

"From a philosophic standpoint, the fallacy of Special Pleading is violating a well accepted principle, namely the Principle of Relevant Difference. According to this principle, two people can be treated differently if and only if there is a relevant difference between them. This principle is a reasonable one. After all, it would not be particularly rational to treat two people differently when there is no relevant difference between them."

I sometimes refer to the years since 1980 as the age of antidepressants or the age of psychopharmacology, but I would be closer to the mark if I called it the age of clinical trials. Not only were the pharmaceutical companies turning them out, the NIMH [then under Director Steven Hyman] was regularly funding them. Here are some of the big ones from that time period:

In the last two posts [latter day STAR*D I…, latter day STAR*D II…], I was looking at several independent studies using data from the STAR*D trial to address issues not central to the study itself. I expect that there’s a lot more mileage in that approach, plus, in some cases, a much needed re-analysis of the original research question [see significant III… for an example]. But the number of NIMH sponsored trials pales in the face of the industry run and funded clinical trials – and the same points apply in terms of both re-purposing and re-analyzing that data.

In back on track… I was arguing that it’s the legacy RCTs that actually need to be included in the Data Transparency programs because the out-of-patent drugs are going to be in use for a very long time, and much of what we think we know about them is suspect. The argument about Commercially Confidential Information obviously falls by the wayside with out-of-patent drugs. And the more I think about it, so does the argument about patient confidentiality for reasons I’ve already mentioned. I would suggest that the Institute of Medicine, the NIH, the EMA, the FDA, etc. begin to have committees and meetings looking into how to effectively anonymize the data rather than succumbing to the industry’s co-opting medical confidentiality as an excuse for continued secrecy. There’s a wealth of important medical information locked away in file drawers that needs inspecting, harvesting.

I don’t think we [psychiatrists] knew a lot about RCTs in those medicalizing days. I sure didn’t. And by the 1990s, our journals were filled with RCTs. It seems in retrospect that they quickly became the currency of the land, fitting right in with the emphasis on biomedical treatment and psychiatry’s new preoccupation with evidence-based medicine. I doubt that it ever occurred to me or many of us that they were financed by PHARMA, conducted by contract Clinical Research Organizations, or that the authors on the byline didn’t do the study, the analyses, even the writing. I only thought about those things a decade or more after the fact. I expect most of us looked at the graphs of rating scale scores like they were precise chemical measurements rather than results from subjective questionnaires or raters opinions. We looked at p rather than NNT or Effect Sizes. In the process of medicalizing, we took on the trappings of medical science too quickly without getting in up to our elbows and evaluating the instruments that were directing us. My point is that we were naive, gullible, ill-prepared to critically review what we were reading – and it showed in our performance.

So, at least in psychiatry, there’s more at stake than just the individual RTCs from the last thirty years. We need to re·search these studies to learn how to critically evaluate their content and find their place in making good clinical decisions. In my mind, that’s another strong reason we need for Data Transparency to include access to the raw data from those studies we’ve been reading about in our journals all these years. At least in our specialty, having independent teams re-evaluating that information is an important piece of our path to learning what we needed to know in the first place about the ins and outs of medical science [but didn’t]. We drank the Kool-Ade…

Quality of life in major depressive disorder before/after multiple steps of treatment and one-year follow-up.

by IsHak WW, Mirocha J, James D, Tobia G, Vilhauer J, Fakhry H, Pi S, Hanson E, Nashawati R, Peselow ED, Cohen RM.

Acta Psychiatrica Scandanavia. 2015 131[1]:51-60.

[full text on-line]

OBJECTIVE: This study examines the impact of major depressive disorder [MDD] and its treatment on quality of life [QOL].

METHOD: From the Sequenced Treatment Alternatives to Relieve Depression [STAR*D] trial, we analyzed complete data of 2280 adult MDD out-patients at entry/exit of each level of antidepressant treatments and after 12 months of entry to follow-up. QOL was measured using the QOL Enjoyment and Satisfaction Questionnaire [Q-LES-Q]. The proportions of patients scoring ‘within-normal’ QOL [within 10% of Q-LES-Q community norms] and those with ‘severely impaired’ QOL [>2 SD below Q-LES-Q community norms] were analyzed.

RESULTS: Before treatment, no more than 3% of MDD patients experienced ‘within-normal’ QOL. Following treatment, statistically significant improvements were detected; however, the proportion of patients achieving ‘within-normal’ QOL did not exceed 30%, with >50% of patients experiencing ‘severely impaired’ QOL. Although remitted patients had greater improvements compared with non-remitters, 32-60% continued to experience reduced QOL. 12-month follow-up data revealed that the proportion of patients experiencing ‘within-normal’ QOL show a statistically significant decrease in non-remitters.

CONCLUSION: Symptom-focused treatments of MDD may leave a misleading impression that patients have recovered when, in fact, they may be experiencing ongoing QOL deficits. These findings point to the need for investigating specific interventions to ameliorate QOL in MDD.

^{[click image for full screen version]}

To conclude, the present analysis highlights the major pitfalls associated with MDD treatments that are purely symptom-focused. Such treatments can give the misleading impression that a patient has recovered, when in fact the patient continues to experience ongoing deficits in QOL. QOL did not improve further after the acute treatment phase even in remitters, and non-remitters showed a statistically significant decline at follow-up after one year. Consequently, clinicians and researchers need to move beyond the mere assessment of symptoms when treating and/or researching MDD, by incorporating QOL measurement, and by investigating specific and personalized interventions to ameliorate QOL.

These attempts to turn clinical psychiatry into algorithms for medications driven by symptoms, often gathered by questionnaires, have yielded nothing. Worse, they trivialize both the human experience of patients and the practitioners’ efforts to help them. Let’s hope that the chart up there has finally run out of iterations and we can retire the side…

Depression is not a consistent syndrome: An investigation of unique symptom patterns in the STAR*D study

by Eiko I. Fried and Randolph M. Nesse

Journal of Affective Disorders. 2014 172C:96-102.

Background: The DSM-5 encompasses a wide range of symptoms for Major Depressive Disorder [MDD]. Symptoms are commonly added up to sum-scores, and thresholds differentiate between healthy and depressed individuals. The underlying assumption is that all patients diagnosed with MDD have a similar condition, and that sum-scores accurately reflect the severity of this condition. To test this assumption, we examined the number of DSM-5 depression symptom patterns in the “Sequenced Treatment Alternatives to Relieve Depression” [STAR*D] study.

Methods: We investigated the number of unique symptom profiles reported by 3703 depressed outpatients at the beginning of the first treatment stage of STAR*D.

Results: Overall, we identified 1030 unique symptom profiles. Of these profiles, 864 profiles [83.9%] were endorsed by five or fewer subjects, and 501 profiles [48.6%] were endorsed by only one individual. The most common symptom profile exhibited a frequency of only 1.8%. Controlling for overall depression severity did not reduce the amount of observed heterogeneity.

Limitations: Symptoms were dichotomized to construct symptom profiles. Many subjects enrolled in STAR*D reported medical conditions for which prescribed medications may have affected symptom presentation.

Conclusions: The substantial symptom variation among individuals who all qualify for one diagnosis calls into question the status of MDD as a specific consistent syndrome and offers a potential explanation for the difficulty in documenting treatment efficacy. We suggest that the analysis of individual symptoms, their patterns, and their causal associations will provide insights that could not be discovered in studies relying on only sum-scores.

 

"Bottom Line: Major Depression doesn’t exist in Nature. A political process in psychiatry created it…"

Neuroskeptic‘s blog on Discover is one my favorites. His current post is about Diederik Stapel, the Dutch Social Psychologist, now famous as an admitted fraud who fabricated a lot of data. Diedrik actually became a regular on another favorite blog, Ivan Oranski’s Retraction Watch [Diederik has 54 Retractions to date]. If you don’t know the story, here are some links [in the New York Times, Neuroskeptic’s post, and on Retraction Watch]. Stapel also wrote a book:

"Two years ago, Dutch science fraudster Diederik Stapel published a book, Ontsporing [“Derailment”], describing how he became one of the world’s leading social psychologists, before falling from grace when it emerged that he’d fabricated the data in dozens of papers. Stapel wrote Ontsporing in Dutch, but now his story has been translated into English, under the title of Faking Science – thanks to the efforts of Nick Brown."

Who among us doesn’t have personal memories of early experimentation with embellishments or outright lies? or dealt with the experimentation of our children? or recall disillusioning encounters with friends or colleagues once exposed? And it’s the unusual case of someone in psychotherapy who doesn’t get around to shamefully confessed secrets of such things. Here are Stapel’s comments on his step over the line, quoted by Neuroskeptic [QRP: The study he references is available online, a survey of "Questionable Research Practices" among psychologists that reports a surprisingly high prevalence]:

After years of balancing on the outer limits [of scientific integrity], the grey became darker and darker until it was black, and I fell off the edge into the abyss. I’d been having trouble with my experiments for some time. Even with my various “grey” methods for “improving” the data [i.e. ‘QRPs‘], I wasn’t able to get the results the way I wanted them. I couldn’t resist the temptation to go a step further. I wanted it so badly. I wanted to belong, to be part of the action, to score.

I really, really wanted to be really, really good. I wanted to be published in the best journals and speak in the largest room at conferences. I wanted people to hang on my every word as I headed for coffee or lunch after delivering a lecture.

I felt very alone. I was alone in my tastefully furnished office at the University of Groningen. I’d taken extra care when closing the door, and made my desk extra tidy. Everything had to be neat and orderly. No mess.

I opened the file with the data that I had entered and changed an unexpected 2 into a 4; then, a little further along, I changed a 3 into a 5. It didn’t feel right. I looked around me nervously. The data danced in front of my eyes. When the results are just not quite what you’d so badly hoped for; when you know that that hope is based on a thorough analysis of the literature; when this is your third experiment on this subject and the first two worked great; when you know that there are other people doing similar research elsewhere who are getting good results; then, surely, you’re entitled to adjust the results just a little?…

No. I clicked on “Undo Typing.” And again. I felt very alone. I didn’t want this. I’d worked so hard. I’d done everything I could and it just hadn’t quite worked out the way I’d expected. It just wasn’t quite how everyone could see that it logically had to be. I looked at the door of my office. It was still closed. I looked out the window. It was dark outside. “Redo Typing.” And again. For a moment I had the feeling that someone was standing behind me. I turned round slowly, fearfully. There was nobody there. I looked at the array of data and made a few mouse clicks to tell the computer to run the statistical analyses. When I saw the results, the world had become logical again. I saw what I’d imagined. I felt relieved, but my heart was heavy. This was great, but at the same time it was very wrong.

Any modern discussion of Moral Development usually has that word development in it, because morality is not something that just comes with the package. We acquire it along the way. And the terms used to describe it [Superego, Conscience, Moral Compass, Jiminy Cricket] imply it’s an attachment, rather than an integral component. Stapel‘s description of someone standing behind me, is pretty common. Many describe this moral agency as the personified part of the mind [as we acquire it from other people and it’s experienced as a presence]. My point is that we all know people whose morality depends on whether someone is looking or not – it hasn’t become an internalized part of the mind, but remains dependent on an outside monitor like it is in parts of childhood. In a later vignette, also quoted by Neuroskeptic, after Diederik Stapel has been confronted, he’s still thinking he can continue to fool the outside agent, and is fabricating new, more elaborate stories.

Retraction Watch is an interesting study of the fragility of human morality – high functioning people who step into the "grey," and then it gets "darker and darker." And it’s almost always "loners" doing the fabrication – I presume looking over their shoulders. This story attracted my attention at a time when I’ve been thinking about the issue of Data Transparency in Clinical Trials and all the distorted Clinical Trial reports strewn about in our medical literature. In Ben Goldacre’s now famous 2012 TED Talk, he mentioned that industry-funded Clinical Trials are better conducted than many independent trials. That was anti-intuitive to me when I first heard it, but I have to admit that now, having looked a a lot of trials, I reluctantly agree. There are a number of trials where the design is biased [wrong dosing of comparators, for example], but I haven’t run across fabrication of data – the kind of thing that’s common in the examples on Retraction Watch. The trouble comes after the blind gets broken. Negative studies go unpublished. And often, small [and trivial] effects are amplified with a whole host of "grey" techniques [that carefully stay out of the "black"].

What I’m implying [because I apparently don’t know how to say it clearly] is that the widespread embellishment and deceit seen in the Clinical Trial publications is categorically different from the cases we see in the people who show up on Retraction Watch. Each instance in a Clinical Trial includes the involvement of multiple people: guest authors, statisticians, scientists, medical writers, marketeers, legal consultants, CEOs, etc. And there’s an intentionality in staying "in the grey rather than the black." It is a widespread practice, a culture, with similar methods showing up from seemingly independent groups who are otherwise in competition. And throughout this struggle over Data Transparency, the individual companies and their collective [PhRMA] are fighting to hold onto as much control of data access as they can muster. The only reason I can think of for their dogged persistence is to maintain the latitude for embellishment and deceit in the future – as if it is potentially an essential element.