Sugary Drinks as the Culprit in Childhood Obesity? a RCT among Primary School Children

24 09 2012

ResearchBlogging.org Childhood obesity is a growing health problem. Since 1980, the proportion of overweighted children has almost tripled in the USA:  nowadays approximately 17% of children and adolescents are obese.  (Source: cdc.gov [6])

Common sense tells me that obesity is the result of too high calory intake without sufficient physical activity.” - which is just what the CDC states. I’m not surprised that the CDC also mentions the greater availability of high-energy-dense foods and sugary drinks at home and at school as main reasons for the increased intake of calories among children.

In my teens I already realized that sugar in sodas were just “empty calories” and I replaced tonic and cola by low calory  Rivella (and omitted sugar from tea). When my children were young I urged the day care to restrain from routinely giving lemonade (often in vain).

I was therefore a bit surprised to notice all the fuss in the Dutch newspapers [NRC] [7] about a new Dutch study [1] showing that sugary drinks contributed to obesity. My first reaction was “Duhhh?!…. so what?”.

Also, it bothered me that the researchers had performed a RCT (randomized controlled trial) in kids giving one half of them sugar-sweetened drinks and the other half sugar-free drinks. “Is it ethical to perform such a scientific “experiment” in healthy kids?”, I wondered, “giving more than 300 kids 14 kilo sugar over 18 months, without them knowing it?”

But reading the newspaper and the actual paper[1], I found that the study was very well thought out. Also ethically.

It is true that the association between sodas and weight gain has been shown before. But these studies were either observational studies, where one cannot look at the effect of sodas in isolation (kids who drink a lot of sodas often eat more junk food and watch more television: so these other life style aspects may be the real culprit) or inconclusive RCT’s (i.e. because of low sample size). Weak studies and inconclusive evidence will not convince policy makers, organizations and beverage companies (nor schools) to take action.

As explained previously in The Best Study Design… For Dummies [8] the best way to test whether an intervention has a health effect is to do a  double blind RCT, where the intervention (in this case: sugary drinks) is compared to a control (drinks with artificial sweetener instead of sugar) and where the study participants, and direct researchers do not now who receives the  actual intervention and who the phony one.

The study of Katan and his group[1] was a large, double blinded RCT with a long follow-up (18 months). The researchers recruited 641 normal-weight schoolchildren from 8 primary schools.

Importantly, only children were included in the study that normally drank sugared drinks at school (see announcement in Dutch). Thus participation in the trial only meant that half of the children received less sugar during the study-period. The researchers would have preferred drinking water as a control, but to ensure that the sugar-free and sugar-containing drinks tasted and looked essentially the same they used an artificial sweetener as a control.

The children drank 8 ounces (250 ml) of a 104-calorie sugar-sweetened or no-calorie sugar-free fruit-flavoured drink every day during 18 months.  Compliance was good as children who drank the artificially sweetened beverages had the expected level of urinary sucralose (sweetener).

At the end of the study the kids in the sugar-free group gained a kilo less weight than their peers. They also had a significant lower BMI-increase and gained less body fat.

Thus, according to Katan in the Dutch newspaper NRC[7], “it is time to get rid of the beverage vending machines”. (see NRC [6]).

But does this research really support that conclusion and does it, as some headlines state [9]: “powerfully strengthen the case against soda and other sugary drinks as culprits in the obesity epidemic?”

Rereading the paper I wondered as to the reasons why this study was performed.

If the trial was meant to find out whether putting children on artificially sweetened beverages (instead of sugary drinks) would lead to less fat gain, then why didn’t the researchers do an  intention to treat (ITT) analysis? In an ITT analysis trial participants are compared–in terms of their final results–within the groups to which they were initially randomized. This permits the pragmatic evaluation of the benefit of a treatment policy.
Suppose there were more dropouts in the intervention group, that might indicate that people had a reason not to adhere to the treatment. Indeed there were many dropouts overall: 26% of the children had stopped consuming the drinks, 29% from the sugar-free group, and 22% from the sugar group.
Interestingly, the majority of the children who stopped drinking the cans because they no longer liked the drink (68/94 versus 45/70 dropouts in the sugar-free versus the sugar group).
Ànd children who correctly assumed that the sweetened drinks were “artificially sweetened” was 21% higher than expected by chance (correct identification was 3% lower in the sugar group).
Did some children stop using the non-sugary drinks because they found the taste less nice than usual or artificial? Perhaps.

This  might indicate that replacing sugar-drinks by artificially sweetened drinks might not be as effective in “practice”.

Indeed most of the effect on the main outcome, the differences in BMI-Z score (the number of standard deviations by which a child differs from the mean in the Netherland for his or her age or sex) was “strongest” after 6 months and faded after 12 months.

Mind you, the researchers did neatly correct for the missing data by multiple imputation. As long as the children participated in the study, their changes in body weight and fat paralleled those of children who finished the study. However, the positive effect of the earlier use of non-sugary drinks faded in children who went back to drinking sugary drinks. This is not unexpected, but it underlines the point I raised above: the effect may be less drastic in the “real world”.

Another (smaller) RCT, published in the same issue of the NEJM [2](editorial in[4]), aimed to test the effect of an intervention to cut the intake of sugary drinks in obese adolescents. The intervention (home deliveries of bottled water and diet drinks for one year) led to a significant reduction in mean BMI (body mass index), but not in percentage body fat, especially in Hispanic adolescents. However at one year follow up (thus one year after the intervention had stopped) the differences between the groups evaporated again.

But perhaps the trial was “just” meant as a biological-fysiological experiment, as Hans van Maanen suggested in his critical response in de Volkskrant[10].

Indeed, the data actually show that sugar in drinks can lead to a greater increase in obesity-related parameters (and vice versa). [avoiding the endless fructose-glucose debate [11].

In the media, Katan stresses the mechanistic aspects too. He claims that children who drank the sweetened drinks, didn’t compensate for the lower intake of sugars by eating more. In the NY-times he is cited as follows[12]: “When you change the intake of liquid calories, you don’t get the effect that you get when you skip breakfast and then compensate with a larger lunch…”

This seems a logic explanation, but I can’t find any substatation in the article.

Still “food intake of the children at lunch time, shortly after the morning break when the children have consumed the study drinks”, was a secondary outcome in the original protocol!! (see the nice comparison of the two most disparate descriptions of the trial design at clinicaltrials.gov [5], partly shown in the figure below).

“Energy intake during lunchtime” was later replaced by a “sensory evaluation” (with questions like: “How satiated do you feel?”). The results, however were not reported in their current paper. That is also true for a questionnaire about dental health.

Looking at the two protocol versions I saw other striking differences. At 2009_05_28, the primary outcomes of the study are the children’s body weight (BMI z-score),waist circumference (replaced by waist to height), skin folds and bioelectrical impedance.
The latter three become secondary outcomes in the final draft. Why?

Click to enlarge (source Clinicaltrials.gov [5])

It is funny that although the main outcome is the BMI z score, the authors mainly discuss the effects on body weight and body fat in the media (but perhaps this is better understood by the audience).

Furthermore, the effect on weight is less then expected: 1 kilo instead of 2,3 kilo. And only a part is accounted for by loss in body fat: -0,55 kilo fat as measured by electrical impedance and -0,35 kilo as measured by changes in skinfold thickness. The standard deviations are enormous.

Look for instance at the primary end point (BMI z score) at 0 and 18 months in both groups. The change in this period is what counts. The difference in change between both groups from baseline is -0,13, with a P value of 0.001.

(data are based on the full cohort, with imputed data, taken from Table 2)

Sugar-free group : 0.06±1.00  [0 Mo]  –> 0.08±0.99 [18 Mo] : change = 0.02±0.41  

Sugar-group: 0.01±1.04  [0 Mo]  –> 0.15±1.06 [18 Mo] : change = 0.15±0.42 

Difference in change from baseline: −0.13 (−0.21 to −0.05) P = 0.001

Looking at these data I’m impressed by the standard deviations (replaced by standard errors in the somewhat nicer looking fig 3). What does a value of 0.01 ±1.04 represent? There is a looooot of variation (even though BMI z is corrected for age and sex). Although no statistical differences were found for baseline values between the groups the “eyeball test” tells me the sugar- group has a slight “advantage”. They seem to start with slightly lower baseline values (overall, except for body weight).

Anyway, the changes are significant….. But significance isn’t identical to relevant.

At a second look the data look less impressive than the media reports.

Another important point, raised by van Maanen[10], is that the children’s weight increases more in this study than in the normal Dutch population. 6-7 kilo instead of 3 kilo.

In conclusion, the study by the group of Katan et al is a large, unique, randomized trial, that looked at the effects of replacement of sugar by artificial sweeteners in drinks consumed by healthy school children. An effect was noticed on several “obesity-related parameters”, but the effects were not large and possibly don’t last after discontinuation of the trial.

It is important that a single factor, the sugar component in beverages is tested in isolation. This shows that sugar itself “does matter”. However, the trial does not show that sugary drinks are the main obesity  factor in childhood (as suggested in some media reports).

It is clear that the investigators feel very engaged, they really want to tackle the childhood obesity problem. But they should separate the scientific findings from common sense.

The cans fabricated for this trial were registered under the trade name Blikkie (Dutch for “Little Can”). This was to make sure that the drinks would never be sold by smart business guys using the slogan: “cans which have scientifically been proven to help to keep your child lean and healthy”.[NRC]

Still soft drink stakeholders may well argue that low calory drinks are just fine and that curbing sodas is not the magic bullet.

But it is a good start, I think.

Photo credits Cola & Obesity:  Melliegrunt Flikr [CC]

  1. de Ruyter JC, Olthof MR, Seidell JC, & Katan MB (2012). A Trial of Sugar-free or Sugar-Sweetened Beverages and Body Weight in Children. The New England journal of medicine PMID: 22998340
  2. Ebbeling CB, Feldman HA, Chomitz VR, Antonelli TA, Gortmaker SL, Osganian SK, & Ludwig DS (2012). A Randomized Trial of Sugar-Sweetened Beverages and Adolescent Body Weight. The New England journal of medicine PMID: 22998339
  3. Qi Q, Chu AY, Kang JH, Jensen MK, Curhan GC, Pasquale LR, Ridker PM, Hunter DJ, Willett WC, Rimm EB, Chasman DI, Hu FB, & Qi L (2012). Sugar-Sweetened Beverages and Genetic Risk of Obesity. The New England journal of medicine PMID: 22998338
  4. Caprio S (2012). Calories from Soft Drinks – Do They Matter? The New England journal of medicine PMID: 22998341
  5. Changes to the protocol http://clinicaltrials.gov/archive/NCT00893529/2011_02_24/changes
  6. Overweight and Obesity: Childhood obesity facts  and A growing problem (www.cdc.gov)
  7. NRC Wim Köhler Eén kilo lichter.NRC | Zaterdag 22-09-2012 (http://archief.nrc.nl/)
  8.  The Best Study Design… For Dummies (http://laikaspoetnik.wordpress.com)
  9. Studies point to sugary drinks as culprits in childhood obesity – CTV News (ctvnews.ca)
  10. Hans van Maanen. Suiker uit fris, De Volkskrant, 29 september 2012 (freely accessible at http://www.vanmaanen.org/)
  11. Sugar-Sweetened Beverages, Diet Coke & Health. Part I. (http://laikaspoetnik.wordpress.com)
  12. Roni Caryn Rabina. Avoiding Sugared Drinks Limits Weight Gain in Two Studies. New York Times, September 21, 2012




The Scatter of Medical Research and What to do About it.

18 05 2012

ResearchBlogging.orgPaul Glasziou, GP and professor in Evidence Based Medicine, co-authored a new article in the BMJ [1]. Similar to another paper [2] I discussed before [3] this paper deals with the difficulty for clinicians of staying up-to-date with the literature. But where the previous paper [2,3] highlighted the mere increase in number of research articles over time, the current paper looks at the scatter of randomized clinical trials (RCTs) and systematic reviews (SR’s) accross different journals cited in one year (2009) in PubMed.

Hofmann et al analyzed 7 specialties and 9 sub-specialties, that are considered the leading contributions to the burden of disease in high income countries.

They followed a relative straightforward method for identifying the publications. Each search string consisted of a MeSH term (controlled  term) to identify the selected disease or disorders, a publication type [pt] to identify the type of study, and the year of publication. For example, the search strategy for randomized trials in cardiology was: “heart diseases”[MeSH] AND randomized controlled trial[pt] AND 2009[dp]. (when searching “heart diseases” as a MeSH, narrower terms are also searched.) Meta-analysis[pt] was used to identify systematic reviews.

Using this approach Hofmann et al found 14 343 RCTs and 3214 SR’s published in 2009 in the field of the selected (sub)specialties. There was a clear scatter across journals, but this scatter varied considerably among specialties:

“Otolaryngology had the least scatter (363 trials across 167 journals) and neurology the most (2770 trials across 896 journals). In only three subspecialties (lung cancer, chronic obstructive pulmonary disease, hearing loss) were 10 or fewer journals needed to locate 50% of trials. The scatter was less for systematic reviews: hearing loss had the least scatter (10 reviews across nine journals) and cancer the most (670 reviews across 279 journals). For some specialties and subspecialties the papers were concentrated in specialty journals; whereas for others, few of the top 10 journals were a specialty journal for that area.
Generally, little overlap occurred between the top 10 journals publishing trials and those publishing systematic reviews. The number of journals required to find all trials or reviews was highly correlated (r=0.97) with the number of papers for each specialty/ subspecialty.”

Previous work already suggested that this scatter of research has a long tail. Half of the publications is in a minority of papers, whereas the remaining articles are scattered among many journals (see Fig below).

Click to enlarge en see legends at BMJ 2012;344:e3223 [CC]

The good news is that SRs are less scattered and that general journals appear more often in the top 10 journals publishing SRs. Indeed for 6 of the 7 specialties and 4 of the 9 subspecialties, the Cochrane Database of Systematic Reviews had published the highest number of systematic reviews, publishing between 6% and 18% of all the systematic reviews published in each area in 2009. The bad news is that even keeping up to date with SRs seems a huge, if not impossible, challenge.

In other words, it is not sufficient for clinicians to rely on personal subscriptions to a few journals in their specialty (which is common practice). Hoffmann et al suggest several solutions to help clinicians cope with the increasing volume and scatter of research publications.

  • a central library of systematic reviews (but apparently the Cochrane Library fails to fulfill such a role according to the authors, because many reviews are out of date and are perceived as less clinically relevant)
  • registry of planned and completed systematic reviews, such as prospero. (this makes it easier to locate SRs and reduces bias)
  • Synthesis of Evidence and synopses, like the ACP-Jounal Club which summarizes the best evidence in internal medicine
  • Specialised databases that collate and critically appraise randomized trials and systematic reviews, like www.pedro.org.au for physical therapy. In my personal experience, however, this database is often out of date and not comprehensive
  • Journal scanning services like EvidenceUpdates from mcmaster.ca), which scans over 120 journals, filters articles on the basis of quality, has practising clinicians rate them for relevance and newsworthiness, and makes them available as email alerts and in a searchable database. I use this service too, but besides that not all specialties are covered, the rating of evidence may not always be objective (see previous post [4])
  • The use of social media tools to alert clinicians to important new research.

Most of these solutions are (long) existing solutions that do not or only partly help to solve the information overload.

I was surprised that the authors didn’t propose the use of personalized alerts. PubMed’s My NCBI feature allows to create automatic email alerts on a topic and to subscribe to electronic tables of contents (which could include ACP journal Club). Suppose that a physician browses 10 journals roughly covering 25% of the trials. He/she does not need to read all the other journals from cover to cover to avoid missing one potentially relevant trial. Instead it is far more efficient to perform a topic search to filter relevant studies from journals that seldom publish trials on the topic of interest. One could even use the search of Hoffmann et al to achieve this.* Although in reality, most clinical researchers will have narrower fields of interest than all studies about endocrinology and neurology.

At our library we are working at creating deduplicated, easy to read, alerts that collate table of contents of certain journals with topic (and author) searches in PubMed, EMBASE and other databases. There are existing tools that do the same.

Another way to reduce the individual work (reading) load is to organize journals clubs or even better organize regular CATs (critical appraised topics). In the Netherlands, CATS are a compulsory item for residents. A few doctors do the work for many. Usually they choose topics that are clinically relevant (or for which the evidence is unclear).

The authors shortly mention that their search strategy might have missed  missed some eligible papers and included some that are not truly RCTs or SRs, because they relied on PubMed’s publication type to retrieve RCTs and SRs. For systematic reviews this may be a greater problem than recognized, for the authors have used meta-analyses[pt] to identify systematic reviews. Unfortunately PubMed has no publication type for systematic reviews, but it may be clear that there are many more systematic reviews that meta-analyses. Possibly systematical reviews might even have a different scatter pattern than meta-analyses (i.e. the latter might be preferentially included in core journals).

Furthermore not all meta-analyses and systematic reviews are reviews of RCTs (thus it is not completely fair to compare MAs with RCTs only). On the other hand it is a (not discussed) omission of this study, that only interventions are considered. Nowadays physicians have many other questions than those related to therapy, like questions about prognosis, harm and diagnosis.

I did a little imperfect search just to see whether use of other search terms than meta-analyses[pt] would have any influence on the outcome. I search for (1) meta-analyses [pt] and (2) systematic review [tiab] (title and abstract) of papers about endocrine diseases. Then I subtracted 1 from 2 (to analyse the systematic reviews not indexed as meta-analysis[pt])

Thus:

(ENDOCRINE DISEASES[MESH] AND SYSTEMATIC REVIEW[TIAB] AND 2009[DP]) NOT META-ANALYSIS[PT]

I analyzed the top 10/11 journals publishing these study types.

This little experiment suggests that:

  1. the precise scatter might differ per search: apparently the systematic review[tiab] search yielded different top 10/11 journals (for this sample) than the meta-analysis[pt] search. (partially because Cochrane systematic reviews apparently don’t mention systematic reviews in title and abstract?).
  2. the authors underestimate the numbers of Systematic Reviews: simply searching for systematic review[tiab] already found appr. 50% additional systematic reviews compared to meta-analysis[pt] alone
  3. As expected (by me at last), many of the SR’s en MA’s were NOT dealing with interventions, i.e. see the first 5 hits (out of 108 and 236 respectively).
  4. Together these findings indicate that the true information overload is far greater than shown by Hoffmann et al (not all systematic reviews are found, of all available search designs only RCTs are searched).
  5. On the other hand this indirectly shows that SRs are a better way to keep up-to-date than suggested: SRs  also summarize non-interventional research (the ratio SRs of RCTs: individual RCTs is much lower than suggested)
  6. It also means that the role of the Cochrane Systematic reviews to aggregate RCTs is underestimated by the published graphs (the MA[pt] section is diluted with non-RCT- systematic reviews, thus the proportion of the Cochrane SRs in the interventional MAs becomes larger)

Well anyway, these imperfections do not contradict the main point of this paper: that trials are scattered across hundreds of general and specialty journals and that “systematic reviews” (or meta-analyses really) do reduce the extent of scatter, but are still widely scattered and mostly in different journals to those of randomized trials.

Indeed, personal subscriptions to journals seem insufficient for keeping up to date.
Besides supplementing subscription by  methods such as journal scanning services, I would recommend the use of personalized alerts from PubMed and several prefiltered sources including an EBM search machine like TRIP (www.tripdatabase.com/).

*but I would broaden it to find all aggregate evidence, including ACP, Clinical Evidence, syntheses and synopses, not only meta-analyses.

**I do appreciate that one of the co-authors is a medical librarian: Sarah Thorning.

References

  1. Hoffmann, Tammy, Erueti, Chrissy, Thorning, Sarah, & Glasziou, Paul (2012). The scatter of research: cross sectional comparison of randomised trials and systematic reviews across specialties BMJ, 344 : 10.1136/bmj.e3223
  2. Bastian, H., Glasziou, P., & Chalmers, I. (2010). Seventy-Five Trials and Eleven Systematic Reviews a Day: How Will We Ever Keep Up? PLoS Medicine, 7 (9) DOI: 10.1371/journal.pmed.1000326
  3. How will we ever keep up with 75 trials and 11 systematic reviews a day (laikaspoetnik.wordpress.com)
  4. Experience versus Evidence [1]. Opioid Therapy for Rheumatoid Arthritis Pain. (laikaspoetnik.wordpress.com)




RIP Statistician Paul Meier. Proponent not Father of the RCT.

14 08 2011

This headline in Boing Boing caught my eye today:  RIP Paul Meier, father of the randomized trial

Not surprisingly, I knew that Paul Meier (with Kaplan) introduced the Kaplan-Meier estimator (1958), a very important tool for measuring how many patients survive a medical treatment. But I didn’t know he was “father of the randomized trial”….

But is he really?:Father of the randomized trial and “probably best known for the introduction of randomized trials into the evaluation of medical treatments”, as Boing Boing states?

Boing Boing’s very short article is based on the New York Times article: Paul Meier, Statistician Who Revolutionized Medical Trials, Dies at 87. According to the NY Times “Dr. Meier was one of the first and most vocal proponents of what is called “randomization.” 

Randomization, the NY-Times explains, is:

Under the protocol, researchers randomly assign one group of patients to receive an experimental treatment and another to receive the standard treatment. In that way, the researchers try to avoid unintentionally skewing the results by choosing, for example, the healthier or younger patients to receive the new treatment.

(for a more detailed explanation see my previous posts The best study designs…. for dummies and #NotSoFunny #16 – Ridiculing RCTs & EBM)

Meier was a very successful proponent, that is for sure. According to Sir Richard Peto, (Dr. Meier) “perhaps more than any other U.S. statistician, was the one who influenced U.S. drug regulatory agencies, and hence clinical researchers throughout the U.S. and other countries, to insist on the central importance of randomized evidence.”

But an advocate need not be a father, for advocates are seldom the inventors/creators. A proponent is more of a nurse, a mentor or a … foster-parent.

Is Meier the true father/inventor of the RCT? And if not, who is?

Googling “Father of the randomized trial” won’t help, because all 1.610  hits point to Dr. Meier…. thanks to Boing Boing careless copying.

What I read so far doesn’t point at one single creator. And the RCT wasn’t just suddenly there. It started with comparison of treatments under controlled conditions. Back in 1753, the British naval surgeon James Lind published his famous account of 12 scurvy patients, “their cases as similar as I could get them” noting that “the most sudden and visible good effects were perceived from the uses of the oranges and lemons and that citrus fruit cured scurvy [3]. The French physician Pierre Louis and Harvard anatomist Oliver Wendell Holmes (19th century) were also fierce proponents of supporting conclusions about the effectiveness of treatments with statistics, not subjective impressions.[4]

But what was the first real RCT?

Perhaps the first real RCT was The Nuremberg salt test (1835) [6]. This was possibly not only the first RCT, but also the first scientific demonstration of the lack of effect of a homeopathic dilution. More than 50 visitors of a local tavern participated in the experiment. Half of them received a vial  filled with distilled snow water, the other half a vial with ordinary salt in a homeopathic C30-dilution of distilled snow water. None of the participants knew whether he got the “actual medicine or not” (blinding). The numbered vials were coded and the code was broken after the experiment (allocation concealment).

The first publications of RCT’s were in the field of psychology and agriculture. As a matter of fact one other famous statistician, Ronald A. Fisher  (of the Fisher’s exact test) seems to play a more important role in the genesis and popularization of RCT’s than Meier, albeit in agricultural research [5,7]. The book “The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century” describes how Fisher devised a randomized trial at the spot to test the contention of a lady that she could taste the difference between tea into which milk had been poured and tea that had been poured into milk (almost according to homeopathic principles) [7]

According to Wikipedia [5] the published (medical) RCT appeared in the 1948 paper entitled “Streptomycin treatment of pulmonary tuberculosis”. One of the authors, Austin Bradford Hill, is (also) credited as having conceived the modern RCT.

Thus the road to the modern RCT is long, starting with the notions that experiments should be done under controlled conditions and that it doesn’t make sense to base treatment on intuition. Later, experiments were designed in which treatments were compared to placebo (or other treatments) in a randomized and blinded fashion, with concealment of allocation.

Paul Meier was not the inventor of the RCT, but a successful vocal proponent of the RCT. That in itself is commendable enough.

And although the Boing Boing article was incorrect, and many people googling for “father of the RCT” will find the wrong answer from now on, it did raise my interest in the history of the RCT and the role of statisticians in the development of science and clinical trials.
I plan to read a few of the articles and books mentioned below. Like the relatively lighthearted “The Lady Tasting Tea” [7]. You can envision a book review once I have finished reading it.

Note added 15-05 13.45 pm:

Today a more accurate article appeared in the Boston Globe (“Paul Meier; revolutionized medical studies using math”), which does justice to the important role of Dr Meier in the espousal of randomization as an essential element in clinical trials. For that is what he did.

Quote:

Dr. Meier published a scathing paper in the journal Science, “Safety Testing of Poliomyelitis Vaccine,’’ in which he described deficiencies in the production of vaccines by several companies. His paper was seen as a forthright indictment of federal authorities, pharmaceutical manufacturers, and the National Foundation for Infantile Paralysis, which funded the research for a polio vaccine.

  1. RIP Paul Meier, father of the randomized trial (boingboing.net)
  2. Paul Meier, Statistician Who Revolutionized Medical Trials, Dies at 87 (nytimes.com)
  3. M L Meldrum A brief history of the randomized controlled trial. From oranges and lemons to the gold standard. Hematology/ Oncology Clinics of North America (2000) Volume: 14, Issue: 4, Pages: 745-760, vii PubMed: 10949771  or see http://www.mendeley.com
  4. Fye WB. The power of clinical trials and guidelines,and the challenge of conflicts of interest. J Am Coll Cardiol. 2003 Apr 16;41(8):1237-42. PubMed PMID: 12706915. Full text
  5. http://en.wikipedia.org/wiki/Randomized_controlled_trial
  6. Stolberg M (2006). Inventing the randomized double-blind trial: The Nuremberg salt test of 1835. JLL Bulletin: Commentaries on the history of treatment evaluation (www.jameslindlibrary.org).
  7. The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century Peter Cummings, MD, MPH, Jama 2001;286(10):1238-1239. doi:10.1001/jama.286.10.1238  Book Review.
    Book by David Salsburg, 340 pp, with illus, $23.95, ISBN 0-7167-41006-7, New York, NY, WH Freeman, 2001.
  8. Kaptchuk TJ. Intentional ignorance: a history of blind assessment and placebo controls in medicine. Bull Hist Med. 1998 Fall;72(3):389-433. PubMed PMID: 9780448. abstract
  9. The best study design for dummies/ (http://laikaspoetnik.wordpress.com: 2008/08/25/)
  10. #Notsofunny: Ridiculing RCT’s and EBM (http://laikaspoetnik.wordpress.com: 2010/02/01/)
  11. RIP Paul Meier : Research Randomization Advocate (mystrongmedicine.com)
  12. If randomized clinical trials don’t show that your woo works, try anthropology! (scienceblogs.com)
  13. The revenge of “microfascism”: PoMo strikes medicine again (scienceblogs.com)




#NotSoFunny #16 – Ridiculing RCTs & EBM

1 02 2010

I remember it well. As a young researcher I presented my findings in one of my first talks, at the end of which the chair killed my work with a remark, that made the whole room of scientists laugh, but was really beside the point. My supervisor, a truly original and very wise scientist, suppressed his anger. Afterwards, he said: “it is very easy ridiculing something that isn’t a mainstream thought. It’s the argument that counts. We will prove that we are right.” …And we did.

This was not my only encounter with scientists who try to win the debate by making fun of a theory, a finding or …people. But it is not only the witty scientist who is to *blame*, it is also the uncritical audience that just swallows it.

I have similar feelings with some journal articles or blog posts that try to ridicule EBM – or any other theory or approach. Funny, perhaps, but often misunderstood and misused by “the audience”.

Take for instance the well known spoof article in the BMJ:

“Parachute use to prevent death and major trauma related to gravitational challenge: systematic review of randomised controlled trials”

It is one of those Christmas spoof articles in the BMJ, meant to inject some medical humor into the normally serious scientific literature. The spoof parachute article pretends to be a Systematic Review of RCT’s  investigating if parachutes can prevent death and major trauma. Of course, no such trial has been done or will be done: dropping people at random with and without a parachute to proof that you better jump out of a plane with a parachute.

I found the article only mildly amusing. It is so unrealistic, that it becomes absurd. Not that I don’t enjoy absurdities at times, but  absurdities should not assume a live of their own.  In this way it doesn’t evoke a true discussion, but only worsens the prejudice some people already have.

People keep referring to this 2003 article. Last Friday, Dr. Val (with whom I mostly agree) devoted a Friday Funny post to it at Get Better Health: “The Friday Funny: Why Evidence-Based Medicine Is Not The Whole Story”.* In 2008 the paper was also discussed by Not Totally Rad [3]. That EBM is not the whole story seems pretty obvious to me. It was never meant to be…

But lets get specific. Which assumptions about RCT’s and SR’s are wrong, twisted or put out of context? Please read the excellent comments below the article. These often put the finger on the spot.

1. EBM is cookbook medicine.
Many define EBM as “make clinical decisions based on a synthesis of the best available evidence about a treatment.” (i.e. [3]). However, EBM is not cookbook medicine.

The accepted definition of EBM  is “the conscientious, explicit and judicious use of current best evidence in making decisions about the care of individual patients” [4]. Sacket already emphasized back in 1996:

Good doctors use both individual clinical expertise and the best available external evidence, and neither alone is enough. Without clinical expertise, practice risks becoming tyrannised by evidence, for even excellent external evidence may be inapplicable to or inappropriate for an individual patient. Without current best evidence, practice risks becoming rapidly out of date, to the detriment of patients.


2. RCT’s are required for evidence.

Although a well performed RCT provides the “best” evidence, RCT’s are often not appropriate or indicated. That is especially true for domains other than therapy. In case of prognostic questions the most appropriate study design is usually an inception cohort. A RCT for instance can’t tell whether female age is a prognostic factor for clinical pregnancy rates following IVF: there is no way to randomize for “age”, or for “BMI”. ;)

The same is true for etiologic or harm questions. In theory, the “best” answer is obtained by RCT. However RCT’s are often unethical or unnecessary. RCT’s are out of the question to address whether substance X causes cancer. Observational studies will do. Sometimes cases provide sufficient evidence. If a woman gets hepatic veno-occlusive disease after drinking loads of a herbal tea the finding of  similar cases in the literature may be sufficient to conclude that the herbal tea probably caused the disease.

Diagnostic accuracy studies also require another study design (cross-sectional study, or cohort).

But even in the case of  interventions, we can settle for less than a RCT. Evidence is not present or not, but exists on a hierarchy. RCT’s (if well performed) are the most robust, but if not available we have to rely on “lower” evidence.

BMJ Clinical Evidence even made a list of clinical questions unlikely to be answered by RCT’s. In this case Clinical Evidence searches and includes the best appropriate form of evidence.

  1. where there are good reasons to think the intervention is not likely to be beneficial or is likely to be harmful;
  2. where the outcome is very rare (e.g. a 1/10000 fatal adverse reaction);
  3. where the condition is very rare;
  4. where very long follow up is required (e.g. does drinking milk in adolescence prevent fractures in old age?);
  5. where the evidence of benefit from observational studies is overwhelming (e.g. oxygen for acute asthma attacks);
  6. when applying the evidence to real clinical situations (external validity);
  7. where current practice is very resistant to change and/or patients would not be willing to take the control or active treatment;
  8. where the unit of randomisation would have to be too large (e.g. a nationwide public health campaign); and
  9. where the condition is acute and requires immediate treatment.
    Of these, only the first case is categorical. For the rest the cut off point when an RCT is not appropriate is not precisely defined.

Informed health decisions should be based on good science rather than EBM (alone).

Dr Val [2]: “EBM has been an over-reliance on “methodolatry” – resulting in conclusions made without consideration of prior probability, laws of physics, or plain common sense. (….) Which is why Steve Novella and the Science Based Medicine team have proposed that our quest for reliable information (upon which to make informed health decisions) should be based on good science rather than EBM alone.

Methodolatry is the profane worship of the randomized clinical trial as the only valid method of investigation. This is disproved in the previous sections.

The name “Science Based Medicine” suggests that it is opposed to “Evidence Based Medicine”. At their blog David Gorski explains: “We at SBM believe that medicine based on science is the best medicine and tirelessly promote science-based medicine through discussion of the role of science and medicine.”

While this may apply to a certain extent to quack or homeopathy (the focus of SBM) there are many examples of the opposite: that science or common sense led to interventions that were ineffective or even damaging, including:

As a matter of fact many side-effects are not foreseen and few in vitro or animal experiments have led to successful new treatments.

At the end it is most relevant to the patient that “it works” (and the benefits outweigh the harms).

Furthermore EBM is not -or should not be- without consideration of prior probability, laws of physics, or plain common sense. To me SBM and EBM are not mutually exclusive.

Why the example is bullshit unfair and unrealistic

I’ll leave it to the following comments (and yes the choice is biased) [1]

Nibu A George,Scientist :

First of all generalizing such reports of some selected cases and making it a universal truth is unhealthy and challenging the entire scientific community. Secondly, the comparing the parachute scenario with a pure medical situation is unacceptable since the parachute jump is rather a physical situation and it become a medical situation only if the jump caused any physical harm to the person involved.

Richard A. Davidson, MD,MPH:

This weak attempt at humor unfortunately reinforces one of the major negative stereotypes about EBM….that RCT’s are required for evidence, and that observational studies are worthless. If only 10% of the therapies that are paraded in front of us by journals were as effective as parachutes, we would have much less need for EBM. The efficacy of most of our current therapies are only mildly successful. In fact, many therapies can provide only a 25% or less therapeutic improvement. If parachutes were that effective, nobody would use them.
While it’s easy enough to just chalk this one up to the cliche of the cantankerous British clinician, it shows a tremendous lack of insight about what EBM is and does. Even worse, it’s just not funny.

Aviel Roy-Shapira, Senior Staff Surgeon

Smith and Pell succeeded in amusing me, but I think their spoof reflects a common misconception about evidence based medicine. All too many practitioners equate EBM with randomized controlled trials, and metaanalyses.
EBM is about what is accepted as evidence, not about how the evidence is obtained. For example, an RCT which shows that a given drug lowers blood pressure in patients with mild hypertension, however well designed and executed, is not acceptable as a basis for treatment decisions. One has to show that the drug actually lowers the incidence of strokes and heart attacks.
RCT’s are needed only when the outcome is not obvious. If most people who fall from airplanes without a parachute die, this is good enough. There is plenty of evidence for that.

EBM is about using outcome data for making therapeutic decisions. That data can come from RCTs but also from observation

Lee A. Green, Associate Professor

EBM is not RCTs. That’s probably worth repeating several times, because so often both EBM’s detractors and some of its advocates just don’t get it. Evidence is not binary, present or not, but exists on a heirarchy (Guyatt & Rennie, 2001). (….)
The methods and rigor of EBM are nothing more or less than ways of correcting for our
imperfect perceptions of our experiences. We prefer, cognitively, to perceive causal connections. We even perceive such connections where they do not exist, and we do so reliably and reproducibly under well-known sets of circumstances. RCTs aren’t holy writ, they’re simply a tool for filtering out our natural human biases in judgment and causal attribution. Whether it’s necessary to use that tool depends upon the likelihood of such bias occurring.

Scott D Ramsey, Associate Professor

Parachutes may be a no-brainer, but this article is brainless.

Unfortunately, there are few if any parallels to parachutes in health care. The danger with this type of article is that it can lead to labeling certain medical technologies as “parachutes” when in fact they are not. I’ve already seen this exact analogy used for a recent medical technology (lung volume reduction surgery for severe emphysema). In uncontrolled studies, it quite literally looked like everyone who didn’t die got better. When a high quality randomized controlled trial was done, the treatment turned out to have significant morbidity and mortality and a much more modest benefit than was originally hypothesized.

Timothy R. Church, Professor

On one level, this is a funny article. I chuckled when I first read it. On reflection, however, I thought “Well, maybe not,” because a lot of people have died based on physicians’ arrogance about their ability to judge the efficacy of a treatment based on theory and uncontrolled observation.

Several high profile medical procedures that were “obviously” effective have been shown by randomized trials to be (oops) killing people when compared to placebo. For starters to a long list of such failed therapies, look at antiarrhythmics for post-MI arrhythmias, prophylaxis for T. gondii in HIV infection, and endarterectomy for carotid stenosis; all were proven to be harmful rather than helpful in randomized trials, and in the face of widespread opposition to even testing them against no treatment. In theory they “had to work.” But didn’t.

But what the heck, let’s play along. Suppose we had never seen a parachute before. Someone proposes one and we agree it’s a good idea, but how to test it out? Human trials sound good. But what’s the question? It is not, as the author would have you believe, whether to jump out of the plane without a parachute or with one, but rather stay in the plane or jump with a parachute. No one was voluntarily jumping out of planes prior to the invention of the parachute, so it wasn’t to prevent a health threat, but rather to facilitate a rapid exit from a nonviable plane.

Another weakness in this straw-man argument is that the physics of the parachute are clear and experimentally verifiable without involving humans, but I don’t think the authors would ever suggest that human physiology and pathology in the face of medication, radiation, or surgical intervention is ever quite as clear and predictable, or that non-human experience (whether observational or experimental) would ever suffice.

The author offers as an alternative to evidence-based methods the “common sense” method, which is really the “trust me, I’m a doctor” method. That’s not worked out so well in many high profile cases (see above, plus note the recent finding that expensive, profitable angioplasty and coronary artery by-pass grafts are no better than simple medical treatment of arteriosclerosis). And these are just the ones for which careful scientists have been able to do randomized trials. Most of our accepted therapies never have been subjected to such scrutiny, but it is breathtaking how frequently such scrutiny reveals problems.

Thanks, but I’ll stick with scientifically proven remedies.

parachute experiments without humans

* on the same day as I posted Friday Foolery #15: The Man who pioneered the RCT. What a coincidence.

** Don’t forget to read the comments to the article. They are often excellent.

Photo Credits

ReferencesResearchBlogging.org

  1. Smith, G. (2003). Parachute use to prevent death and major trauma related to gravitational challenge: systematic review of randomised controlled trials BMJ, 327 (7429), 1459-1461 DOI: 10.1136/bmj.327.7429.1459
  2. The Friday Funny: Why Evidence-Based Medicine Is Not The Whole Story”. (getbetterhealth.com) [2010.01.29]
  3. Call for randomized clinical trials of Parachutes (nottotallyrad.blogspot.com) [08-2008]
  4. Sackett DL, Rosenberg WM, Gray JA, Haynes RB, & Richardson WS (1996). Evidence based medicine: what it is and what it isn’t. BMJ (Clinical research ed.), 312 (7023), 71-2 PMID: 8555924
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are very well edged off




Friday Foolery #15: The Man who pioneered the RCT

29 01 2010

This BMJ video certainly belongs on a blog like this, focussing on EBM. This video shows “John Crofton who pioneered the randomised controlled trial in a 1948 BMJ paper which looked at the antibiotic streptomycin to treat TB. Now in his 90s, Dr Crofton talks to Colin Blakemore about the importance of randomisation and blinding, and how it has helped to make medicine more evidence based.”

First seen on the Clinical Cases and Images: CasesBlog of Ves Dimov.

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NOT ONE RCT on Swine Flu or H1N1?! – Outrageous!

16 12 2009

Last week doctorblogs (Annabel Bentley) tweeted: “Outrageous- there isn’t ONE randomised trial on swine flu or #H1N1

Annabel referred to an article at Trust the Evidence, the excellent blog of the Centre for Evidence-Based Medicine (CEBM) in Oxford, UK.

In the article “Is swine flu the most over-published and over-hyped disease ever?Carl Heneghan first showed the results of a quick PubMed search using the terms ‘swine flu’ and ‘H1N1’: this yielded 4,475 articles on the subject, with approximately one third (1,437 articles) published in the last 7 months (search: November 27th). Of these 107, largely news articles, were published in the BMJ, followed by the Lancet and NEJM at 35 each.

Top News stories on H1N1 generated appr. 2000 to 4000 news articles each (in Google). Items included outbreak of a new form of ‘swine flu’ which prompted the United States and the World Health Organization to declare a public health emergency (April), Southern Hemisphere being mostly spared in the swine flu epidemic (May), Tamiflu, i.e. the effects of Tamiflu in children in the BMJ (co-authored by Carl) in August and the availability of the vaccine H1N1 vaccine clinics to offer seasonal flu shots in November.

According to Heneghan this must be the most over-hyped disease ever, and he wonders: “are there any other infections out there?”

Finally he ends with: Do you know what the killer fact is in all of this? There isn’t one randomized trial out there on swine flu or H1N1 – outrageous.”

My first thoughts were: “is H1N1 really so over-published compared to other (infectious) diseases?”, “Is it really surprising that there are no RCTs yet? The H1N1-pandemics just started a few months ago!” and even “are RCT’s really the study designs we urgently need right now?”

Now the severity of the H1N1 flu seems less than feared, it is easy to be wise. Isn’t is logic that there are a lot of “exploratory studies” first: characterization of the virus, establishing the spread of H1N1 around the world, establishing mortality and morbidity, and patterns of vulnerability among the population? It is also understandable that a lot of news articles are published, in the BMJ or in online newspapers. We want to be informed. In the Netherlands we now have a small outbreak of Q-fever, partly because the official approach was slow and underestimated the public health implications of Q-fever. So the public was really underinformed. That is worse than being “overexposed”.

News often spreads like wildfire, that is no news. When I google “US Preventive Services Task Force” (who issued the controversial US breast cancer screening guidelines last month) 2,364 hits still pop up in Google News (over the last month). All papers and other news sources echo the news. 2,000 hits are easily reached.

4,475 PubMed articles on ‘swine flu’ and ‘H1N1’ isn’t really that much. When I quickly search PubMed for the rather “new” disease Q-fever I get 3,752 hits, a search for HPV (Alphapapillomavirus OR papilloma infections OR HPV OR human papilloma virus) gives 19,543 hits (1,330 over the last 9 months), and a quick search for (aids) AND “last 9 months”[edat] yields 4,073 hits!

The number of hits alone doesn’t mean much, certainly not if news, editorials and comments are included. But lets go to the second comment, that there is “not ONE RCT on H1N1.”

Again, is it reasonable to expect ONE RCT published and included in PubMed over a 9 month period? Any serious study takes time from concept to initiation, patient-enrollment, sufficient follow-up, collection of data, writing and submitting the article, peer review, publication, inclusion in PubMed and assignment of MeSH-terms (including the publication type “Randomized Controlled Trial”).

Furthermore RCTs are not always the most feasible or appropriate study designs for answering certain questions. For instance for questions related to harm, etiology, epidemiology, spreading of virus, characteristics, diagnosis and prognosis. RCTs may be most suitable to evaluate the efficacy of treatment or prevention interventions. Thus in case of H1N1 the efficacy of vaccines and of neuraminidase inhibitors to prevent or treat H1N1 flu. However, it may not always be ethical to do so (see below).

I’ve repeated the search, and using prefab “My NCBI filters” for RCTs discussed before I get the following results:

Using the Randomized Controlled Trials limits in PubMed I do get 7 hits, and using broader filters, like the Therapy/Narrow Filter under  Clinical Queries I even find 2 more RCTs that have not yet been indexed by PubMed. With the Cochrane Highly sensitive Filter even more hits are obtained, most of which are “noise”, inherent to the use of a broad filter.

The found RCTs are safety/immunogenicity/stability studies of subunit or split vaccines to H1N1, H3N2, and B influenza strains. This means they are not restricted to H1N1, but this is true for the entire set of H1N1 publications. 40 of the 1443 hits are even animal studies. Thus the total number of articles dealing with H1N1 only -and in humans- is far less than 1443.
By the way, one of the 15 H1N1-hits in PubMed obtained with the SR-filter (see Fig) is a meta-analysis of RCTs in the BMJ, co-authored by Heneghan. It is not about H1N1, but contains the sentence: “Their (neuraminidase inhibitors) effects on the incidence of serious complications, and on the current A/H1N1 influenza strain remain to be determined.”

More important, if studies have been undertaken in this field they are probably not yet published. Thus, the place to look is a clinical trials register, like Clinical trials.gov (http://clinicaltrials.gov/), The International Clinical Registry Platform Search Portal at the WHO (www.who.int/trialsearch) , national or pharmaceutical industry trials registers.

A search for H1N1 OR swine flu in Clinical trials.gov, that offers the best searching functions, yields 132 studies, of which 116 were first recieved this year.

Again, most trials concern the safety and efficacy of H1N1 vaccines and include the testing of vaccines on subgroups, like pregnant women, children with asthma and people with AIDS. 30 trials are phase III.
Narrowing the search to H1N1
OR swine flu | neuraminidase inhibitors OR oseltamivir OR zanamivir (treatment filled in in the filed “Interventions”) yields 8 studies. One of the studies is a phase III trial.

This yield doesn’t seem bad per se. However, numbers of trials don’t mean a lot and a more pertinent issue is, whether the most important and urgent questions are investigated.

Three issues are important with respect to interventions:

  1. Are H1N1 vaccines safe and immunogenic? in subpopulations?
  2. Do H1N1 vaccines lower morbidity and mortality due to the H1N1 flu?
  3. Are neuraminidase inhibitors effective in preventing or treating H1N1 flu?
Question [1] will be answered by current trials.
Older Cochrane Reviews on the seasonal influenza flu (and updates) cast doubt on the efficacy of [2] vaccines (see the [poor*] Atlantic news article) ànd [2] neuraminidase inhibitors in children (Cochrane 2007 and BMJ 2009) ànd adults  (Cochrane 2006, update 2008 and BMJ 2009) against symptoms or complications of the seasonal flu. The possibility has even been raised that seasonal flu shots are linked to swine flu risk.
However, the current H1N1 isn’t a seasonal flu. It is a sudden, new pandemic that requires different actions. Overall H1N1 isn’t as deadly as the regular influenza strains, but it hits certain people harder: very young kids, people with asthma and pregnant women. About the latter group, Amy Tuteur (obstetrician-gynecologist blogging at The Skeptical OB) wrote a guest post at Kevin MD:
(…) the H1N1 influenza has had an unexpectedly devastating impact among pregnant women. According to the CDC, there have been approximately 700 reported cases of H1N1 in pregnant women since April.** Of these, 100 women have required admission to an intensive care unit and 28 have died. In other words, 1 out of every 25 pregnant women who contracted H1N1 died of it. By any standard, that is an appalling death rate. (……)
To put it in perspective, the chance of a pregnant woman dying from H1N1 is greater than the chance of a heart patient dying during triple bypass surgery. That is not a trivial risk.
The H1N1 flu has taken an extraordinary toll among pregnant women. A new vaccine is now available. Because of the nature of the emergency, there has not been time to do any long term studies of the vaccine. Yet pregnant women will need to make a decision as soon as possible on whether to be vaccinated. (Emphasis mine)
…. Given the dramatic threat and the fact that we know of no unusual complications of vaccination, the decision seems clear. Every pregnant woman should get vaccinated as soon as possible.
Thus the anticipated risks must be balanced against the anticipated benefits, Amy urges pregnant women to get vaccinated, even though no one can be sure about side effects ànd about the true efficacy of the vaccine.
For scientific purposes it would be best to perform a double randomized trial with half of a series of pregnant women receiving the vaccine, and the other half a placebo. This would provide the most rigid evidence for the true efficacy and safety of the vaccine.
However it would not be ethical to do so. As “Orac” of Orac Knows explains so well  in his post “Vaccination for H1N1 “swine” flu: Do The Atlantic, Shannon Brownlee, and Jeanne Lenzer matter?” RCTs are only acceptable from an ethical standpoint if we truly do not know whether one treatment is superior to another or a treatment is better than a placebo. There is sufficient reason to believe that vaccination for H1N1 will be more efficacious than “doing nothing”. Leaving a control group unvaccinated will certainly mean that a substantial percentage of pregnant women is going to die. To study the efficacy of the H1N1 among pregnant women observational studies (like cohort studies) are also suitable and more appropriate.
Among the studies found in ClinicalTrials.gov there are a few H1N1 Vaccine Clinical Studies in Pregnant Women, including RCTs. But these RCT’s never compare vaccinated women with a non-vaccinated women. All pregnant women are vaccinated, but the conditions vary.
In one Danish study the arms (study groups) are as follows:
Thus two doses of H1N1 with adjuvant are compared with a higher dose H1N1 without adjuvant. As a control non-pregnant women are vaccinated with the adjuvant H1N1.*** The RCT is performed within a prospective, birth-cohort study recruiting 800 pregnant mothers between Q1- 2009 and Q4-2010. As a natural control women pregnant in the H1N1 season (Q4) will be compared with women outside the season. Please note that the completion date of this study will be 2012, thus we will have to wait a number of years before the study describing the results will be found in PubMed….
To give an impression of the idea behind the study, here is the summary of that trial in the register (not because it is particularly outstanding, but to highlight the underlying thoughts):
“Pregnant women are at particular risk during the imminent H1N1v influenza pandemic. The new H1N1v virus requires urgent political and medical decisions on vaccination strategies in order to minimize severe disease and death from this pandemic. However, there is a lack of evidence to build such decisions upon. A vaccine will be provided in the fourth quarter of 2009, but there is little knowledge on the immunogenicity. Particularly its clinical effectiveness and duration of immunity in pregnant women and their newborn infants is unknown. Therefore, it will be important to study the optimal vaccination regimens with respect to dosing and use of adjuvant to decide future health policies on vaccination of pregnant women. We have a unique possibility to study these aspects of H1N1v infection in pregnant women in our ongoing unselected, prospective, birth-cohort study recruiting 800 pregnant mothers between Q1- 2009 and Q4-2010. Pregnant women from East-Denmark are being enrolled during the 2nd trimester and their infant will undergo a close clinical follow-up. The H1N1v pandemic is expected to reach Denmark Q4-2009. The timing of this enrollment and the imminent pandemic allows for an “experiment of nature” whereby the first half of the mothers completes pregnancy before the H1N1v pandemic. The other half of this cohort will be pregnant while H1N1v is prevalent in the community and will require H1N1v vaccination.The aim of this randomized, controlled, trial is to compare and evaluate the dose-related immune protection conferred by vaccine and adjuvant (Novartis vaccine Focetria) in pregnant women and non-pregnant women. In addition the protocol will assess the passive immunity conferred to the newborn from these vaccine regimes. The study will provide evidence-based guidance for health policies on vaccination for the population of pregnant women during future H1N1v pandemics.”
Although with regard to H1N1-vaccination, appropriate studies are being done, it is feasible that certain measures might not be appropriate on basis of what we know. For instance, pretreating people in the non-risk groups (healthy young adults) with neuraminidase-inhibitors, because they are “indispensable employees”. Perhaps Heneghan, who as you remember is a co-author of the BMJ paper on neuraminidase -inhibitors in children with the seasonal flu, was thinking of this when writing his post.
If Heneghan would have directed his arrows at certain interventions in certain circumstances in certain people he might have had a good point, but now his arrows don’t hit any target. Revere from Effect Measure and Orac from Orac Knows might well have diagnosed him as someone who suffers from “methodolatry,” which is, as Revere puts it, the “profane worship of the randomized clinical trial as the only valid method of investigation.”
Notes
* But see the excellent post of Orac who trashes the Atlantic paper in Flu vaccination: Do The Atlantic, Shannon Brownlee, and Jeanne Lenzer matter? (scienceblogs.com). He also critiques the attitude of the Cochrane author Jefferson, who has a different voice in the media compared to the Cochrane Reviews he co-authors. Here he is far more neutral.
** There is no direct link to the data in the post. I’m not sure whether all pregnant women in the US are routinely tested for H1N1. (if not the percentage of H1N1 deaths among H1N1 infected pregnant women might be overestimated)
***In the US, vaccins given to pregnant women are without adjuvant.

45,982

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#CECEM Bridging the Gap between Evidence Based Practice and Practice Based Evidence

15 06 2009

cochrane-symbol A very interesting presentation at the CECEM was given by the organizer of this continental Cochrane meeting, Rob de Bie. De Bie is Professor of Physiotherapy Research and director of Education of the Faculty of Health within the dept. of Epidemiology of the Maastricht University. He is both a certified physiotherapist and an epidemiologist. Luckily he kept the epidemiologic theory to a minimum. In fact he is a very engaging speaker who keeps your attention to the end.

Guidelines

While guidelines were already present in the Middle Ages in the form of formalized treatment of daily practice, more recently clinical guidelines have emerged. These are systematically developed statements which assists clinicians and patients in making decisions about appropriate treatement for specific conditions.

Currently, there are 3 kinds of guidelines, each with its own shortcomings.

  • Consensus based. Consensus may be largely influenced by group dynamics
    Consensus = non-sensus and Consensus guidelines are guidelies.
  • Expert based. Might be even worse than consensus. It can have all kind of biases, like expert and opinion bias or external financing.
  • Evidence based. Guideline recommendations are based on best available evidence, deals with specific interventions for specific populations and are based on a systematic approach.

The quality of Evidence Based Guidelines depends on whether the evidence is good enough, transparent, credible, available, applied and not ‘muddled’ by health care insurers.
It is good to realize that some trials are never done, for instance because of ethical considerations. It is also true that only part of what you read (in the conclusions) has actually be done and some trials are republished several times, each time with a better outcome…

Systematic reviews and qualitatively good trials that don’t give answers.

Next Rob showed us the results of a study ( Jadad and McQuay in J. Clin. Epidemiol. ,1996) with efficacy as stated in the review plotted on the X-axis and the Quality score on the Y-axis. Surprisingly meta-analysis of high quality were less likely to produce positive results. Similar results were also obtained by Suttorp et al in 2006. (see Figure below)

12066264  rob de bie CECEM

Photo made by Chris Mavergames

There may be several reasons why good trials not always give good answers. Well known reasons are  the lack of randomization or blinding. However Rob focused on a less obvious reason. Despite its high level of evidence, a Randomized Controlled Trial (RCT) may not always be suitable to provide good answers applicable to all patients, because RCT’s often fail to reflect the true clinical practice. Often, the inclusion of patients in RCT’s is selective: middle-aged men with exclusion of co-morbidity. Whereas co-morbidity occurs in > 20% of the people of 60 years and older and in >40% of the people of 80 years and older (André Knottnerus in his speech).

Usefulness of a Nested Trial Cohort Study coupled to an EHR to study interventions.

Next, Rob showed that a nested Trial cohort study can be useful to study the effectiveness of  interventions. He used this in conjunction with an EHR (electronic health record), which could be accessed by practitioner and patient.

One of the diseases studied in this way, was Intermittent Claudication. Most commonly Intermittent Claudication is a manifestation of  peripheral arterial disease in the legs, causing pain and cramps in the legs while walking (hence the name). The mortality is high: the 5 year mortality rates are in between those of colorectal cancer and Non-Hodgkin Lymphoma. This is related to the underlying atherosclerosis.

There are several risk factors, some of which cannot be modified, like hereditary factors, age and gender. Other factors, like smoking, diet, physical inactivity and obesity can be tackled. These factors are interrelated.

Rob showed that, whereas there may be an overall null effect of exercise in the whole population, the effect may differ per subgroup.

15-6-2009 3-06-19 CI 1

  • Patients with mild disease and no co-morbidity may directly benefit from exercise-therapy (blue area).
  • Exercise has no effect on smokers, probably because smoking is the main causative factor.
  • People with unstable diabetes first show an improvement, which stabilized after a few weeks due to hypo- or hyperglycaemia induced by the exercise,
  • A similar effect is seen in COPD patients, the exercise becoming less effective because the patients become short of breath.

It is important to first regulate diabetes or COPD before continuing the exercise therapy. By individually optimizing the intervention(s) a far greater overall effect is achieved: 191% improval in the maximal (pain-free) walking distance compared to for instance <35% according to a Cochrane Systematic Review (2007).

Another striking effect: exercise therapy affects some of the prognostic factors: whereas there is no effect on BMI (this stays an important risk factor), age and diabetes become less important risk factors.

15-6-2009 3-35-10 shift in prognostic factors

Because guidelines are quickly outdated, the findings are directly implemented in the existing guidelines.

Another astonishing fact: the physiotherapists pay for the system, not the patient nor the government.

More information can be found on https://www.cebp.nl/. Although the presentation is not (yet?) available on the net, I found a comparable presentation here.

** (2009-06-15) Good news: the program and all presentations can now be viewed at: https://www.cebp.nl/?NODE=239








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