The randomized, placebo-controlled, double-blind trial is usually the methodology to test the efficacy of a therapy that carries the least risk of bias. This fact is an obvious annoyance to some alt med enthusiasts, because such trials far too often fail to produce the results they were hoping for.

But there is no need to despair. Here I provide a few simple tips on how to mislead the public with seemingly rigorous trials.


The most brutal method for misleading people is simply to cheat. The Germans have a saying, ‘Papier ist geduldig’ (paper is patient), implying that anyone can put anything on paper. Fortunately we currently have plenty of alt med journals which publish any rubbish anyone might dream up. The process of ‘peer-review’ is one of several mechanisms supposed to minimise the risk of scientific fraud. Yet alt med journals are more clever than that! They tend to have a peer-review that rarely involves independent and critical scientists, more often than not you can even ask that you best friend is invited to do the peer-review, and the alt med journal will follow your wish. Consequently the door is wide open to cheating. Once your fraudulent paper has been published, it is almost impossible to tell that something is fundamentally wrong.

But cheating is not confined to original research. You can also apply the method to other types of research, of course. For instance, the authors of the infamous ‘Swiss report’ on homeopathy generated a false positive picture using published systematic reviews of mine by simply changing their conclusions from negative to positive. Simple!


Obviously, outright cheating is not always as simple as that. Even in alt med, you cannot easily claim to have conducted a clinical trial without a complex infrastructure which invariably involves other people. And they are likely to want to have some control over what is happening. This means that complete fabrication of an entire data set may not always be possible. What might still be feasible, however, is the ‘prettification’ of the results. By just ‘re-adjusting’ a few data points that failed to live up to your expectations, you might be able to turn a negative into a positive trial. Proper governance is aimed at preventing his type of ‘mini-fraud’ but fortunately you work in alt med where such mechanisms are rarely adequately implemented.


Another very handy method is the omission of aspects of your trial which regrettably turned out to be in disagreement with the desired overall result. In most studies, one has a myriad of endpoints. Once the statistics of your trial have been calculated, it is likely that some of them yield the wanted positive results, while others do not. By simply omitting any mention of the embarrassingly negative results, you can easily turn a largely negative study into a seemingly positive one. Normally, researchers have to rely on a pre-specified protocol which defines a primary outcome measure. Thankfully, in the absence of proper governance, it usually is possible to publish a report which obscures such detail and thus mislead the public (I even think there has been an example of such an omission on this very blog).


Yes – lies, dam lies, and statistics! A gifted statistician can easily find ways to ‘torture the data until they confess’. One only has to run statistical test after statistical test, and BINGO one will eventually yield something that can be marketed as the longed-for positive result. Normally, researchers must have a protocol that pre-specifies all the methodologies used in a trial, including the statistical analyses. But, in alt med, we certainly do not want things to function normally, do we?


All the above tricks are a bit fraudulent, of course. Unfortunately, fraud is not well-seen by everyone. Therefore, a more legitimate means of misleading the public would be highly desirable for those aspiring alt med researchers who do not want to tarnish their record to their disadvantage. No worries guys, help is on the way!

The fool-proof trial design is obviously the often-mentioned ‘A+B versus B’ design. In such a study, patients are randomized to receive an alt med treatment (A) together with usual care (B) or usual care (B) alone. This looks rigorous, can be sold as a ‘pragmatic’ trial addressing a real-fife problem, and has the enormous advantage of never failing to produce a positive result: A+B is always more than B alone, even if A is a pure placebo. Such trials are akin to going into a hamburger joint for measuring the calories of a Big Mac without chips and comparing them to the calories of a Big Mac with chips. We know the result before the research has started; in alt med, that’s how it should be!

I have been banging on about the ‘A+B versus B’ design often enough, but recently I came across a new study design used in alt med which is just as elegantly misleading. The trial in question has a promising title: Quality-of-life outcomes in patients with gynecologic cancer referred to integrative oncology treatment during chemotherapy. Here is the unabbreviated abstract:


Integrative oncology incorporates complementary medicine (CM) therapies in patients with cancer. We explored the impact of an integrative oncology therapeutic regimen on quality-of-life (QOL) outcomes in women with gynecological cancer undergoing chemotherapy.


A prospective preference study examined patients referred by oncology health care practitioners (HCPs) to an integrative physician (IP) consultation and CM treatments. QOL and chemotherapy-related toxicities were evaluated using the Edmonton Symptom Assessment Scale (ESAS) and Measure Yourself Concerns and Wellbeing (MYCAW) questionnaire, at baseline and at a 6-12-week follow-up assessment. Adherence to the integrative care (AIC) program was defined as ≥4 CM treatments, with ≤30 days between each session.


Of 128 patients referred by their HCP, 102 underwent IP consultation and subsequent CM treatments. The main concerns expressed by patients were fatigue (79.8 %), gastrointestinal symptoms (64.6 %), pain and neuropathy (54.5 %), and emotional distress (45.5 %). Patients in both AIC (n = 68) and non-AIC (n = 28) groups shared similar demographic, treatment, and cancer-related characteristics. ESAS fatigue scores improved by a mean of 1.97 points in the AIC group on a scale of 0-10 and worsened by a mean of 0.27 points in the non-AIC group (p = 0.033). In the AIC group, MYCAW scores improved significantly (p < 0.0001) for each of the leading concerns as well as for well-being, a finding which was not apparent in the non-AIC group.


An IP-guided CM treatment regimen provided to patients with gynecological cancer during chemotherapy may reduce cancer-related fatigue and improve other QOL outcomes.

A ‘prospective preference study’ – this is the design the world of alt med has been yearning for! Its principle is beautiful in its simplicity. One merely administers a treatment or treatment package to a group of patients; inevitably some patients take it, while others don’t. The reasons for not taking it could range from lack of perceived effectiveness to experience of side-effects. But never mind, the fact that some do not want your treatment provides you with two groups of patients: those who comply and those who do not comply. With a bit of skill, you can now make the non-compliers appear like a proper control group. Now you only need to compare the outcomes and BOB IS YOUR UNCLE!

Brilliant! Absolutely brilliant!

I cannot think of a more deceptive trial-design than this one; it will make any treatment look good, even one that is a mere placebo. Alright, it is not randomized, and it does not even have a proper control group. But it sure looks rigorous and meaningful, this ‘prospective preference study’!

4 Responses to Five ways to mislead people with seemingly rigorous trials

  • Great post! The ‘prospective preference’ study design is indeed one of the most insidiously deceptive I’ve ever heard of. Its equivalent might be to test a group of people repeatedly trying to throw a basketball through a hoop, then counting the ones who miss most of the time as a control group. A subjective questionnaire about ‘how well do you think you did’ will almost certainly produce a significant score difference between the two groups; even a quasi-objective measure such as a coach’s assessment of individuals’ throwing skills would produce a significant difference. Wonderful!

  • The establishment of journals seemingly created with the aim of publishing bad trials is like paying people to drop litter in the streets. Unfortunately in medicine there are economic advantages and careers to sustain in dropping litter.

    It’s an ironic application of the ‘polluter pays’ principle, transforming it from a punishment to a business opportunity.

  • Prof Ernst, your research colleague and associate Sean Holt in his research published in the NZ Medical Journal appears to have ticked all these boxes. It is a travesty that you do not do a critical analysis of his deceitful research of the chiropractic profession.

  • Self selection is biased selection.

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