Saturday, March 9, 2019

Transparent Citation Kiting in the 2013 and 2014 EAS “consensus” of experts: how to carry a conclusion without having to reach it.

For full PDF reports see fhprevalence.com

Transparent citation kiting in the 2013 EAS “consensus statement”

The 2013 “consensus statement of the European Atherosclerosis Society” was published in the European Heart Journal. It can be seen below, on the right. It is the most influential statement of FH prevalence in the industry, found in FDA documents, investor presentations, patient brochures, and even in SEC 10-K filings. It puts FH prevalence at 1/200. This number is converted in the 2014 EAS statement through the “Hardy-Weinberg” equation to the HoFH population of 1/160,000.  I found many shenanigans in these reports, and I hope to go public with those in separate presentations. For the present purposes, we’ll focus on the linguistic manipulation executed by way of citation kiting. On the left is Dr. Rader’s 2003 report, with the established definition of FH: it is distinct from the APOB and PCSK9 carriers. On the right is the 2013 EAS report which cites Dr. Rader’s paper, but it conflates the diseases together.  Combining FH, FDB, and FH3 in the 2003 report on the left leaves us with 1 in 300.  Most of the 1 in 200 in the 2013 report on the right is due to this linguistic maneuver.  (As for getting from 1 in 300 to 1 in 200, I will cover that in a separate presentation.)


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Pharma-funded publications are using readers’ suspended attention between publications to leave out facts, definitions, and even key numbers.  I’ve referred to this removal during the researchers’ transfer of information as a “fact-ectomy.” As for “citation kiting,” like check kiting, it claims a value on paper which persists as a value only as long as that claim remains unreconciled with its source. The scheme is easy to see, once we’re looking for it. We just trace the “citation” back to its source, match up quantities claimed in each, account for “innovative” definitions, and then set up their respective values and terms side by side. With citation kiting, what we see is something like a relay team that cheats by switching batons, instead of passing on the original. In SEC 10-K filings this 2014 EAS report is the source for the HoFH prevalence of 1/300,000. (It also takes the 2013 EAS HeFH number and through derivation cites 1/160,000 for HoFH.)  It’s not epidemiology.  It’s a gimmick. 

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Is there a good reason for blending different diseases under the name of one of them? Compound HeFH will soon be HoFH. What happens when we hold to the historical record, and tease the underlying components out from their new names? As with all of the FH studies that I have found, the claim of doubled, tripled, even sextupled prevalence is not only refuted by the studies’ own raw data, but the old numbers are confirmed by the very data used to claim a refutation. Restoring citation and linguistic integrity alone recovers the underlying math. Prevalence for HoFH was said to be 1 in 1,000,000. In the Dutch study, there were 20 HoFH found. But 4 of those were said to “inflate the prevalence,” so they were explicitly removed, leaving 16 employed in off-text calculations: this key HoFH number “16” is nowhere to be found in the entire report. And this report is on the homozygous, yet a prevalence number for the true homozygous FH is not in the text. True HoFH is 1/1,045,149, astonishingly close to the established number. Yet this result will not be mentioned, not here, not in the 2014 EAS. This Dutch study will blend in compound heterozygous FH and call both of them combined, the “HoFH.”  Then 2014 EAS will take that number and add in the HoFDB, and simply call of these, “HoFH.”


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Which Wall Street companies are interested in FH Prevalence?

Big players like Amgen, Regeneron, Sanofi, Merck, and others are financially interested in FH prevalence. However, Novelion (NVLN), Gemphire (GEMP), Madrigal (MDGL) and Esperion (ESPR) are also valued according to how large or small the FH population might be. These four companies go the extra mile and actually claim prevalence estimates in their annual reports filed with the SEC. Below are screenshots I took last year of the 2017 10-K’s. The same claims are made in the 2018 10-Ks.  All four claims depend upon “consensus" reports, which actually conducted no prevalence studies of their own. After chasing the literary references down to their sources, the stones upon which these prevalence claims are built are not scientific. They are linguistic and depend upon preserving asymmetry between what the authors and their readers know.  The 2014 EAS HoFH prevelance estimates – 1 in 300,000 and 1 in 160,000 – are derived from citation kiting. This results in the the equivocation of the (1) genetic definition of the disease and (2) diagnosed FH. Of these two, we will first look into the former, the linguistic equivocation of the genetic defintion of FH. In a separate presentation, I will provide evidence of the equivocation of “FH” through the removal of key elements/steps in the diagnostic procedure.


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Here is what an increased prevalence means ... and doesn't mean. For prevalence of homozygous FH, the difference between the Danish result of 1/160,000 and the Dutch result of 1/300,000 is large, but we must also remember that the original estimate by the Nobel Prize winner was 1/1,000,000. Even if we just consider Novelion, and not the entire industry, we can see what is at stake. What will shares of Novelion be worth with this or that prevalence figure? When raising money from investors, what value will investors place on such a company after the Dutch report? The annual price of Aegerion/Novelion’s drug, Juxtapid, has ranged from $290,000 to well over $300,000 during the last 5 years. To create a rough sketch, I used $300,000 to represent the price over these years and 320 million for the U.S. population. Then I calculated the annual addressable market per prevalence estimate according to the Danish and Dutch reports.  I set these two estimates between Aegerion/Novelion’s actual Juxtapid U.S. sales during the last 5 years and an addressable market in accordance with the Nobel Prize winners’ estimate. 




Tuesday, March 5, 2019

Intro: Citation Kiting in Peer Reviewed FH Literature.

Introductory explainer video:


Citation Kiting in Peer Reviewed FH Literature
Introductory Explainer Video: a follow-up to yesterday's newsletter. (See sign up, on the right.)


Here is the first youtube videohttps://www.youtube.com/watch?v=fKHYjnfHSjo


The explainer video above illustrates the consequences of citation kiting -- as if they were "Before" and "After" photos.  The 2003 "Rader report" and the 2016 "Regeneron report" expose the linguistic and mathematical "conclusion drift" that took place in the interim. But how was this equivocation scheme executed? In a future presentation, I will display screenshots of specific acts of citation kiting and the resulting equivocation. 

Below is a text version of the above, with a little more detail. 


Citation Kiting in Peer Reviewed FH Literature
Background: The disease “Familial Hypercholesterolemia” is caused by a mutation in a cholesterol receptor (LDLR). To express this relationship in acronyms, FH is caused by LDLR.
 Recently, peer reviewed medical journals have published papers declaring that FH prevalence is twice what it was once thought to be.
 Of course, funding by pharmaceutical companies surrounds these peer reviewed publications, payments to both the authors and the journals.
Here are a few Wall Street traded companies who have (or had) a financial interest in the size of the FH population:
·         NVLN      Novelion Therapeutics Inc. (Aegerion Pharmaceuticals)
·         MDGL     Madrigal Pharmaceuticals Inc.
·         GEMP     Gemphire Therapeutics Inc.
·         ESPR       Esperion Therapeutics Inc.
·         AMGN    Amgen Inc.
·         REGN      Regeneron Pharmaceuticals
·         MRK        Merck & Co. (Had hopes for Anacetrapib 2011 ~ 2017)
·         Companies which market statins also have an interest.

(I have no short positions in any of the companies related to my FH research. The reason for this is that I had declared to certain regulators that I had no financial position in any of these companies. Although I last made that declaration in 2017 and although it appears that regulators are not interested, I will hold myself to that statement going forward.)
I will break down my presentation into two parts. In part 1, I will make the case that the “increased” prevalence number is the result of a linguistic gimmick. As a consequence, the integrity of the scientific record is compromised and the addressable market for FH-targeted drugs is inflated.
How far does this scheme go? In any event, I cannot say that exposing peer reviewed medical literature would be the next “Big Short” – because that institution seems to be untouchable. However, what I will outline is a transparent equivocation scheme, carried out by what I call, “Citation Kiting” – with very serious consequences.
The Procedure for Equivocation: An established historical record receives a “fact-ectomy” and authors carry over the truncated content to a new report and then simply cite the purported source. The fact that the content in the source does not match the content in the destination is conspicuous – but only if readers stop trusting peer reviewers and actually reconcile the citations with the sources. (Fact: the industry’s most authoritative, most highly cited claim of doubled FH prevalence has no external, contemporary source for the number it uses! In a multi-billion-dollar industry, the number just … appears. It takes some time to rub one’s eyes and get past the doubt, “It can’t be this simple.” Stay tuned. It is..)
Citation kiting has two consequences, one of them seemingly trivial (although it is not), and the other obviously serious.
Part 1: “FH” as a genetically inherited disease: A fact-ectomy is performed on category headings and neighboring subcategories.(I will demonstrate how this works in my first two explainer videos.)
Part 2: “FH” as diagnostic procedure: A fact-ectomy is performed on elements and/or steps within the already established diagnostic procedures and screening strategies. 


Citation Kiting consequence: Part 1 Conflate to Bait: “Twice as many FH carriers found!”

Linguistics
Blending the underlying objects under one name.
 Epidemiology
Tracking the underlying objects of the original names.
Past


Call LDLR mutation carriers, “FH”
Call the APOB, “FDB”
Call the PCSK9, “FH3”
Call the entire group, “ADH”
Call p.Arg3558Cys “harmless APOB”
Past “ADH” ≠ past “FH”.
“FH” = 1:500
LDLR = 1:500
APOB = 1:1,000
PCSK9 = 1:2,500
p.Arg3558Cys = 1:1,103
Math Total 1:232
Present
Drop usage of “ADH” and call LDLR, APOB, PCSK9, and p.Arg3558Cys, “FH”
Past “ADH” + p.Arg3558Cys = Present “FH”
“FH” = 1:200~1:250
LDLR = 1:500
APOB = 1:1,000
PCSK9 = 1:2,500
p.Arg3558Cys = 1:1,103
Math Total 1:232
Rational Conclusion
The change is only due to linguistics.
No change or discovery.

Citation Kiting Consequence: Part 2: Switch identification procedures
After conflation of the names of genetic mutations, add a second step -- switch counting procedures to identify different patients.


Screening and Diagnosis -- familial hypercholesterolemia








There is a lot more to this case, and some of it requires an elaborate breakdown and detailed presentation. Other parts are knee-slapping comedy. There is also tragedy.

I hope to present the whole of it, step-by-step, where each unit of the presentation will be undeniably clear. Toward this end, I am trying to put together short explainer videos, walking through not only the evidence, but exposing the actual engineering of equivocation … at the point of commission.
·    
     I will also be posting detailed PDF reports online. Please visit 3footcrowbar.com for announcements.

For now, here is a brief introduction to Part 1 of my future presentations:

How can one increase a prevalence rate without having to find more people?

Linguistics. 

If zebras were suddenly called “horses,” would we have more of either or both in the world? Industry-funded reports on FH are more aptly called linguistic strategies than prevalence studies. Their claim of a higher than expected prevalence is necessary to sound the alarm of “underdiagnosis.”

Here is only one example. The illustration below shows the consequences of citation kiting -- as if they were "Before" and "After" photos.  The 2003 "Rader report" and the 2016 "Regeneron report" expose the linguistic and mathematical "conclusion drift" that took place in the interim. But how was this equivocation scheme executed? In a future presentation, I will display screenshots of specific acts of citation kiting and the resulting equivocation.  Here, I’ve taken screenshots from two FH reports and put them together in the presentation below. 

On the left is a report from 2003, and on the right, Regeneron’s report from 2016. In 2003, FH referred to the presence of an LDLR mutation; FDB was different and referred to an APOB mutation, and FH3 was yet another disease name, and referred to PCSK9. These diseases were all under the umbrella acronym, “ADH” – which spells out to “Autosomal Dominant Hypercholesterolemia.” Now Big Pharma has funded reports which drop the umbrella, “ADH,” and take the subset of ADH named “FH” and promote it to serve as the umbrella term for the other two diseases. FH is no longer alongside FDB and FH3, and the terms to distinguish FDB and FH3 are dropped, and their respective mutations, APOB and PCSK9, are no longer referred to as subsets to “ADH,” but to “FH.” It is as if the peas under the shells labeled “FDB” and “FH3” have been palmed and are next found under the FH “shell,” which now houses all … the LDLR, the APOB and the PCSK9. FH becomes the main set … the entire set.

Illustration of a conclusion drift over the years -- prevalence of FH familial hypercholesterolemia

In Truisms:
·         A whole pie is larger than one of its slices.
·         Recent “FH” as LDLR + APOB + PCSK9 is greater than Nobel Prize winners’ “FH” as LDLR alone.


Bottom right, of the illustration below is a tweet by the lead author of the Regeneron report.
Linguistics and FH prevalence - Familial Hypercholesterolemia


Note that FH-as-LDLR is 1:518 … still roughly the 1:500 estimated by the Nobel Prize winner, Joseph Goldstein. Nonetheless, this report became a jumping point for the lead author to claim on 
Twitter:

“FH is ~twice as common as it was thought to be.” (See illustration above.)

And here is a co-author in a press release. (Orchestrated? Note that the phrase is a virtually identical.)
“The study shows us that FH is about twice as common as it was once thought to be …” 

['Geisinger and Regeneron study finds life-threatening genetic disorder is substantially underdiagnosed,' Dec. 22, 2016]

But FH "was thought to be" FH-as-LDLR, which was estimated to be around 1:500 to begin with, and in this study FH-as-LDLR is still around 1:500.


More to come.