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

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.)

Click to enlarge

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. 

Click to enlarge

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 video

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!”

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

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
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 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?


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 

“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. 

Tuesday, February 12, 2019

I'll be posting a new report soon

I'll be posting a new report soon. If interested, join the mailing list (on the right). I plan to post there first, then here.

Friday, February 12, 2016

Long Bank of America

Either Bank of America investors are walking away from cash or they predict a serious deterioration of the balance sheet.  Yesterday BAC was trading just above $11 per share. Tangible book value is $15.62. A return to tangible book would be a gain of over 40%.   Standard book value is $22.54.  This fifty percent discount at $11 has one hundred percent upside, but only if that book value is solid. Do selling investors know something that buying investors don’t?  We know this much at least, with all of the forced changes the banks are stronger than they were pre-crisis.   Is that enough?  Evidently, the market is winning the debate over price, as it always does, but only time will tell whether or not it is winning the debate over value.

“Tangible book value per share(F) increased 8% to $15.62; book value per share increased 6% to $22.54” ~

With 40% upside to tangible book investors are running away in a panic.  This is like deciding not to buy a taxi and its business for $100,000 because the cash flows and customer base are questionable --- while there is $140,000 cash in the trunk.   Either there is a belief that that cash will no longer be there, for some reason, or this is a just pure panic.
There’s something very wrong here. Scratch the taxi ... take out the engine … as far as profitability is concerned what difference would it make?  That $140,000 cash is still in the trunk of the taxi. If you buy it at $100,000, you will still realize a 40% profit when you find a more appropriate market. Wait for the calm ... that "what was I thinking" moment will come.  What does a “slow business” environment really matter when the growth is free and even the cash is sold at a discount?  If there is a fear that the suitcase of cash will not be there after you make your purchase, then what is the rationale?  Slow growth … ? ... but the wager on growth costs nothing.

Monday, October 5, 2015

Clearsign Tech rejected from the Kern County proposal: it is “neither technologically nor economically feasible.”

Clearsign Combustion Tech has been rejected from the Kern County proposal because it is “neither technologically nor economically feasible.”

Ticker: CLIR

For those who haven't been following the Clearsign saga, you will want to read Lou Basenese's piece on Seeking Alpha: "Clearsign Combustion: California Regulator Poised To Deliver A $156 Million Sales Windfall?" This, and PR on the Aera Energy project, have been moving the stock price.

Basenese: "Proposed mandates would require installation of CLIR’s Duplex on every new OTSG and on every existing OTSG within five years in Kern County, California. Based on the current installed base of 782 OTSGs, we’re talking about more than $150 million in sales."

Actually, Clearsign was never on the proposal to begin with. It was mentioned as an alternative to the proposal. If the proposal passed as originally written Clearsign would have been left out. No matter – “Duplex” tech has now been rejected even as an alternative because it is “neither technologically nor economically feasible.”

A Kern County meeting "to receive comments" will be held today at 5:00 PM. However a "Staff Report" was already published last Thursday. (At least, that's when I found it.) Clearsign has already been rejected due to technological and commercial infeasibility.  I finished up a report for the SEC whistle-blower program early Friday morning and was quite surprised that the news had not yet broken. (Most of my reports over the last 9 months have been dedicated to the SEC and have not been made public.)

Recap: Clearsign was originally highlighted in “Alternative 5” of a Kern County zoning proposal that would allow the oil industry to expand its territory.  My research confirmed that Clearsign's own promotional message was used as the basis for its inclusion in the draft proposal. After later review, even as an alternative to the proposal, Clearsign has just been rejected due to technological and commercial infeasibility.

“Under Alternative 5, all new and replacement steam generators for thermal EOR activities would be required to implement lower-emission steam generation technology, such as, by way of example, the ClearSign Duplex Tile combustion technology or similar technologies …. However, Alternative 5 is not technologically or economically feasible. Low-emission steam generation technologies are still in the demonstration and prototype phase.” …

“All technologies currently under consideration require additional testing and validation in actual operating conditions and environments before they can be considered field-proven.” …

“Long-term effectiveness and commercial viability of either technology still remain to be determined, and no company has deployed either technology to date.”  …

“The fact that the rates in the current SJVAPCD BACT guideline are higher than what is contemplated by Alternative 5 further supports the conclusion that the technology required by Alternative 5 has not yet been achieved in practice, has not been shown to be presently achievable and is not technologically feasible. Alternative 5 is therefore properly rejected as an alternative.” …

“As such, the technology is not yet readily available on a commercial scale for implementation in oil and gas production in Kern County. The technological infeasibility of Alternative 5 is further demonstrated by the current Best Available Control Technology (BACT)” …

“However, for the reasons discussed above, Alternative 5 is neither technologically nor economically feasible.” …

See original document pgs 158-160 (PDF pages 321-323):

From the beginning, I have been arguing that the Clearsign IPO was a scam and that investors are at risk. For my previous research on Clearsign see
For specific research on the original inventor of Clearsign’s "revolutionary" ECC technology, see!181&app=WordPdf&authkey=!AC03KkhaDDf8QJ4

Trust fund of original inventor of CLIR’s ECC
23-Sep-15BD & DBG LIVING TRUSTDirector8,400DirectSale at $7.36 per share.61,824
22-Sep-15BD & DBG LIVING TRUSTDirector1,600DirectSale at $7.35 per share.11,760
18-Sep-15BD & DBG LIVING TRUSTDirector10,000DirectSale at $6.18 per share.61,800
5-Dec-14BD & DBG LIVING TRUSTBeneficial Owner (10% or more)3,100DirectAutomatic Sale at $8.04 per share.24,923
4-Dec-14BD & DBG LIVING TRUSTBeneficial Owner (10% or more)13,700DirectAutomatic Sale at $8.03 per share.110,010
1-Dec-14BD & DBG LIVING TRUSTBeneficial Owner (10% or more)66DirectAutomatic Sale at $8 per share.528
28-Nov-14BD & DBG LIVING TRUSTBeneficial Owner (10% or more)2,134DirectAutomatic Sale at $8 per share.17,072
26-Nov-14BD & DBG LIVING TRUSTBeneficial Owner (10% or more)2,264DirectAutomatic Sale at $8 per share.18,112
20-Aug-14BD & DBG LIVING TRUSTBeneficial Owner (10% or more)3,800DirectAutomatic Sale at $8.90 per share.33,820
19-Aug-14BD & DBG LIVING TRUSTBeneficial Owner (10% or more)12,700DirectAutomatic Sale at $8.23 per share.104,521
1-Aug-14BD & DBG LIVING TRUSTBeneficial Owner (10% or more)166DirectAutomatic Sale at $8 per share.1,328
1-Jul-14BD & DBG LIVING TRUSTBeneficial Owner (10% or more)16,666DirectAutomatic Sale at $8.98 per share.149,660
2-Jun-14BD & DBG LIVING TRUSTBeneficial Owner (10% or more)16,666DirectAutomatic Sale at $8.34 per share.138,994
28-May-14BD & DBG LIVING TRUSTBeneficial Owner (10% or more)4,771DirectAutomatic Sale at $8.84 per share.42,175
27-May-14BD & DBG LIVING TRUSTBeneficial Owner (10% or more)11,895DirectAutomatic Sale at $8.97 per share.106,698


It should always be assumed that I am short CLIR.  This is not a recommendation to buy or sell.  I am not infallible. Everyone must do their own due diligence and take their own risks.  Clearsign Combustion’s market cap is under $100 million. There is an organized and funded promotional machine at work here. Very Risky, either way.

Thursday, December 18, 2014

Aegerion’s Dropout Rate May Actually Be Closer to 50% or 60%

Dropout rates:

·         Through direct communication and omission of material fact, Aegerion led investors to believe that the 2013 patient dropout rate was a stable 15%.

·         Aegerion finally updated investors in late 2014, claiming a “blended” dropout rate of 36%. The stock price dropped 40% on the next day of trading.

·         To reach that 36%, Aegerion distorted the rate downward when it “blended” the patients acquired in 2013 with new patients acquired in 2014.

·         If 2013 patient dropout rates are reliable and if such also applies to future first year experiences, simple high school math shows that Aegerion’s relevant dropout rates are much worse than we have been led to believe, most likely between 50% and 60%.Aegerion’s disclosures explicitly admit that the dropout rate is material information, that they withheld this information, and that they are continuing to withhold this information.If we accept Aegerion’s claims that the majority of dropouts occur within the first two months of therapy, then we must abandon our dropout estimate for first year patients in 2014 and we must accept that Aegerion’s commercial enterprise is presently in the midst of an undisclosed marketing crisis.


Solve the Aegerion Puzzle:

The following puzzle rests upon a few assumptions:

1.       That Aegerion did not misrepresent dropout rates for first year experience with its drug in 2013.2

2.       That that rate would serve as sample for the first year experience of new patients in 2014. (Later, we will see what happens if we let go of this estimate.)

3.       Thus, dropout for first year experience in 2013 was 15%, and we use that percentage to estimate dropout for patients in their first year in 2014.3

4.       Given that Aegerion has not given investors an update on patient counts, we use a revenue derived estimate of 508 for the third quarter 2014.5

All other data points have been provided by Aegerion or are mathematically derived therefrom.

Skip to the next section to continue the report. For those who enjoy puzzles, on the right I present the kind of algebra required of any investor who wishes to understand the true risks involved with Aegerion. (Scroll down for the solution.)
Aegerion dropout rate puzzle
Aegerion dropout puzzle

Basic Principle of Deception

Suppose an athlete ran the mile yesterday in 6 minutes. Today he stopped halfway at 3 minutes. Could I really convince anyone that his “blended” time of 4.5 minutes makes him a world class runner?  Why wouldn’t I isolate yesterday’s distance and time from today’s half-distance unless I wished to mislead someone?  If I wanted to include times for both days I must divide the data into three distinct time periods. 

Yesterday’s race
·         1st half of race: 3 minutes
·         2nd half of race: 3 minutes
Today’s race
·         1st half of race: 3 minutes.

Similarly, Aegerion is working with two distinct cohorts over three time periods:

2013 Cohort
·         12 months: Patients acquired in 2013 tracked through 2013. The dropout rate for the first year was said to be 15%.[2]
·         9 months: Patients acquired in 2013 tracked through the first nine months of 2014.
2014 Cohort
·         9 months: Patients acquired in the first nine months of 2014 and tracked through the first nine months of 2014.  If Aegerion’s claims with the first year of the 2013 cohort are credible, we might use a 15% dropout rate for the 2014 cohort in their first nine months. (See Endnotes 1 and 3 for a detailed explanation.[3])

But even without using specific numbers we know that something is not right.

·         Because dropouts occur over time, blending new patient additions with old patient additions necessarily distorts the dropout average downward and misleads investors into thinking that the problem is less than it really is.

Retailers, for example, use same store sales to isolate the mature portion of their business from the immature. Blending this year’s newly opened stores with last year’s established stores would necessarily distort the sales metric downwards.

Breaking down the case.

Aegerion’s Investors must resort to puzzle-solving in order to fill in the missing pieces.  In the puzzle below, I will start with one set of inputs, as an illustration, but I encourage the reader to estimate a range of inputs and plug them into the equations. One will soon observe that even a wide range of estimates suggest that Aegerion may have misled investors.

Numbers provided by Aegerion

·         There were 430 patients in the USA by the end of 2013.[4]

·         The patient dropout rate through the first year, 2013, was 15%.2

·         USA Revenue for the 3rd Quarter 2014: $39,767,000.5 (The USA portion was 91% of total revenue, where product is sold month to month; international orders are multi-month shipments and would unnecessarily complicate the estimates.)

·         Quarterly net cost of the drug at the end of September 2014: $78,277 per US patient.[5]

·         The cumulative dropout for all patients over the entire time period was 36%.1

Numbers derived from Aegerion’s reported numbers

·         Dropout rate for 2014 patients, 9 months into 2014 (Green Cell, between cells F and G): A cumulative 15% dropout was claimed for first year patients at the end of 2013.2  If Aegerion’s claim was credible, we can use this to estimate dropout for those patients acquired in 2014. (See Endnote 3 for detailed considerations.)3

·         Remaining patients at the end of the cumulative (“blended”) time period from 1/2013 to 9/2014 (Last cell, Brown/orange, after cell J): Because Aegerion stopped reporting patient counts, we derive an estimate by dividing the third quarter 2014 revenue by the marketed price of the drug (IE, disclosed price minus disclosed discount). Sales in the USA are month to month.5

o   $39,767,000 in revenue for the quarter divided by $78,277 cost per patient per quarter = 508 estimated patients at the end of the third quarter, 2014.5  (“Value Insight” recently estimated Aegerion’s patient count to be 533 in a SeekingAlpha article.[6])



Solving the Puzzle that Aegerion has left investors
Basic algebra in 8 steps:


1.   A - (A x .15) = 430

      A = 430/.85

      A x .15 = B

To say that 430 patients remain after a 15% dropout would be consistent with saying that the 430 remaining patients were 85% of the total patients introduced in 2013.  A x .85 = 430 is the same as 430/.85 = A.  A = 506. Of 506 patients introduced, 15% dropped out: 76.

·         430/.85 = 506

·         506 x .15 = 76.

·         506 – 76 = 430.

2.   I - (I x .36) = 508

      I = 508/.64

      I x .36 = J

Step 2 follows the same procedure as step 1. If 508 patients remain after a reduction of 36%, then we would say that 508 was 64% or the original total.  I x .64 = 508 is consistent with 508/.64 = I. I = 794.  36% of 794 means that we estimate 286 total dropouts over both 2013 and 2014.

·         508/.64 =794

·         794 *.36 = 286

·         794 – 286 = 508

3. I –A = F

If we have a total of 794 introduced to the drug over the entire time period and 506 of them were introduced in 2013, then how many were introduced in 2014? 288.

·         794-506= 288 new patients introduced in 2014. 

4. (F x .15) = G

     F - G = H

Aegerion claimed a stable 15% dropout among first year patients in 2013.2  If this is reliable then in turn we use a 15% dropout rate for the first year patients in 2014. (See Endnote 3 for detailed considerations.3) If we’ve estimated that 288 patients were acquired in 2014, we then calculate that 43 dropped out and 245 remained.

·         288 * .15 = 43

·         288 – 43 = 245

5. 508 – H = E

If there were a total of 508 remaining patients at the end of September 2014, and 245 of them were from the 2014 cohort, then how many from the 2013 cohort remained in 2014?

·         508 -245 = 263.

6. J – G – B = D

Without “blending” in new patients acquired in 2014, how many of the patients who tried the drug beginning in 2013 dropped out in 2014? We have a cumulative 286 dropouts by the end of the third quarter 2014. We estimated that 43 of the patients acquired in 2014 dropped out in 2014, and 76 of the patients acquired in 2013 dropped out in 2013, which means that 167 of the patients acquired in 2013 dropped out in 2014.

·         286 – 43 – 76 = 167

7. D / 430 = C

So what percentage of the patients who remained on the drug at the end of 2013 dropped out by September 2014? Of the 430 patients remaining on the drug by 2013, 167 dropped out in 2014. That is a 39% dropout rate for the second year of use and in addition to the 15% reported dropout for the first year.

167/430 = .39

8. (D + B) / 506

If we isolated the patients acquired in 2013 and did not “blend” in new patients from 2014, how many of the patients acquired in 2013 dropped out by September 2014?  We estimated that 76 dropped out in 2013 and 167 in 2014. That’s 48% of the 506 introduced in 2013.


·         It is easy to understand why Aegerion presented the cumulative rate of 36% rather than rates from each isolated cohort.  A responsible tally demonstrates that Aegerion’s 2013 cohort dropout rate has accelerated to 50%.

Lots of room for error

Although the laws of mathematics are immutable, the results are only as focused as the estimates we plug into them. Nonetheless, in Aegerion’s case the room for error is very wide.

Two variables which are lacking but which would render the puzzle a mathematically contained system are (1) the patient count as of September 2014 and (2) the dropout rate for patients acquired in the first nine months of 2014. If we found two reliable numbers here, the other numbers would yield to the inexorable laws of mathematics. I invite the reader to plug in a wide range of responsible estimates. It will become obvious that Aegerion has clearly distorted the relevant dropout numbers by “blending” the 2013 cohort with the newer patients acquired in 2014. There is a lot of room for error here: 

# of estimated Patients Sept 2014
Dropout rate over 21 months  for patients acquired in 2013

There is some suspicion in regards to patient counts. For example, Aegerion claimed to have 430 US patients on the drug at the end of 2013. However, deriving a patient count from revenue leaves us with an estimate of 304.[7]  The difference, however, matters little. Either number – 430 or 304 -- shows a severe loss of patients over time. If we accept 430 as the number of patients at the end of 2013, then the drop-out rate for these patients over the 21 months to September 2014 would be approximately 50%; if we estimate there were approximately 304, then the drop-out rate would be approximately 60%.

I encourage the reader to take Aegerion’s disclosures and attempt any responsible input. 


Aegerion omitted these material facts in 2013 and continues to do so in 2014

Although Aegerion suggested, and still suggests, that they know the dropout rate well enough to make stable projections they have not provided investors with the relevant information.  The omission was deliberate. Here is a rejection by Aegerion of a request for crucial data:

“Yes, Tazeen, as Craig indicated, at the end of our Q4 call, we'd like to get away from quarterly quantification or qualitative statements around dropout compliance or non-patient starts.” ~ Aegerion Pharmaceuticals' (AEGR) CEO Marc Beer on Q1 2014 Results - Earnings Call Transcript

It is mathematically challenging for most dropouts to occur in the first two months


Here are Aegerion statements about dropouts occurring within two months of therapy:
“Mirroring what we saw in our Phase III study, we see drop-offs happen most frequently during the first 1 to 2 months of treatment.” ~ Aegerion Pharmaceuticals Management Discusses Q4 2013 Results - Earnings Call Transcript
Craig E. Fraser - President of US & International and Global Supply:  “…. As Mark mentioned, the majority certainly do come off early when they do drop, particularly between shipment 1 and shipment 2 and then some more between shipment 2 and shipment 3, and then it becomes a much lower factor as time goes on. So you do have a bit of a factor of time. Most of that is frontloaded, though, when you are getting the drop-offs. Marc, if you have any …”
Marc D. Beer - Chief Executive Officer and Director: “That's my answer, my complete answer.” ~ Craig E. Fraser,  Aegerion Pharmaceuticals' (AEGR) CEO Marc Beer on Q4 2013 Results - Earnings Call Transcript

If we accept that Aegerion’s disclosures in 2013 about patient dropouts were true and that they serve as a reliable measure of future first year patients, then the dropout for patients acquired in 2013 must have accelerated through 2014.  If I say that the first slice of a pie is larger than the second slice, that second slice cannot be more than half of the pie. A responsible set of inputs shows that Aegerion’s dropouts in the second year exceed the first by more than 200%.  Returning to the 2013 cohort highlighted in this report, we estimate that 167 dropped in 2014, only 76 dropped out in 2013. Again, it is mathematically impossible for the first slice of the 2013 cohort to be larger than the second slice when that second slice is more than half of the pie.
76 dropouts / 12 months in 2013 = 6 per month.
167 dropouts / 9 months in 2014 = 19 per month
If I have a tendency of 6 per month in 2013, how can I then say that it is more than the tendency of 19 per month in 2014? 
To make Aegerion’s claim possible, at a minimum, the dropout rate for the 2013 cohort in 2014 would have to be less than it was in 2013, not more.



Letting go of the dropout rate for the 2014 cohort

We shall see what can happen if we do not accept the 15% dropout rate for the 2014 cohort.  If we take Aegerion’s claim that the majority of dropouts occur in the first two months, then we would have to lower our estimated dropout rate for the 2013 cohort’s experience in 2014.  As we saw in the previous section of this report, it would have to be lower than the first year’s rate of 15%. 
There are several problems with this.
First, if we lower the dropout rate for the 2013 cohort in its second year to 10%, then in order to be consistent with the cumulative 36% rate, we must significantly raise the rate for the 2014 cohort -- to nearly 60%.

Introduced to Juxtapid

Dropout rate

Dropout count

Remaining  patients

2013 patients in 2013





US Patient count as claimed in 4th quarter & full year financial results

2013 patients in 2014

not applicable




2014 patients in 2014










US patients, Revenue derived from 3rd Quarter 2014 income
Second, such an admission would mean that Aegerion’s current growth story is false: it would actually be a marketing catastrophe in the making.
Third, such an extreme and recent dropout acceleration would require specific disclosures of both the sudden acceleration of the dropout rate itself and the factors accounting for the extreme difference between this year’s experience and last year’s. I have found no such disclosure.






1.       Aegerion’s use of a 36% “cumulative” rate distorts dropout rates downwards.  Dropouts are much worse than Aegerion has made them appear.  Relevant dropouts are probably closer to 50% or 60%.

2.       Aegerion’s failure to disclose the relevant dropout rate constitutes an omission of material fact.

3.       If we accept Aegerion’s claims that the majority of dropouts occur within the first two months of therapy, then we must abandon our dropout estimate for first year patients in 2014 and we must accept that Aegerion’s commercial enterprise is presently in the midst of an undisclosed marketing crisis.

What is the purpose of disclosing patient metrics if not to aid the investor in evaluating the business? Of two metrics available why provide the one which decreases awareness of risk unless one’s aim is to mislead investors?

In this last reporting period, Aegerion “blended” its 2014 patient data with ongoing data from patients acquired in 2013 and claimed a “cumulative” drop-out rate of 36%.  Using basic algebra, one can deduce the reason why different cohorts and time periods were “blended” together: isolating patients acquired in 2013 and following them from the onset to the first 9 months of 2014, we calculate a relevant drop-out rate between 50% and 60%.  

Aegerion had previously led investors to expect the drop-out rate to be 15%. Being ill-prepared for even the 36% blended rate, investors sold off their shares. The stock dropped over 40% on the day following the announcement. Obviously investors regarded this news as material. And given the facts presented in this report, it is clear that Aegerion has omitted material facts in previous reporting periods and is continuing to do so now.


[1]  36% dropout:
Currently, the cumulative dropout rate for all patients who have started therapy from the launch in January 2013 to the end of this September is 36%.” Aegerion Pharmaceuticals' (AEGR) CEO Marc Beer on Q3 2014 Results - Earnings Call
[2] 2013 10% and 15% dropout rates:
“Approximately six months into the U.S. commercial launch of JUXTAPID, the company has experienced a patient dropout rate of less than 10 percent …”
Aegerion Pharmaceuticals Announces Second-Quarter 2013 Financial Results
In the fourth quarter 2013 report, it updated the number:
“At the outset of the launch, we assumed an average dropout rate of 15% and we were successful in maintaining a dropout rate at the end of 2013 that was consistent with this estimate that we began the year with.” ~ Aegerion Pharmaceuticals Management Discusses Q4 2013 Results - Earnings Call Transcript
[3] Cumulative dropout rates have been accelerating as time passes. Thus, it is probable that, contrary to Aegerion’s claims, dropouts within a single cohort increase over time as adverse events accumulate.  We follow this logic in the reconstruction of what may be happening with Aegerion’s 2014 dropouts. If the first year experience in 2013 was 15%, we use this rate to estimate the new patients in their first nine months in 2014. With these estimates we try to fill in the blanks: What is happening with the second year on the drug?  What can we say about the 2013 cohort over 21 months?
Because dropouts are obviously accelerating and because the numbers for 2014 represent a shorter time period, at first glance it is tempting to use a percentage smaller than 15% to estimate dropouts for the 2014 cohort.  However, a second glance suggests that 15% might be the better number after all.  15% is made up of an extremely small sample of two elements.  Dropouts for the first six months of 2013 was said to be 10%.  The cumulative rate for all of 2013 was said to be 15%.  This means that dropouts for the second half of 2013 must have been around 20%. (10%+20%)/2 = 15%.  In 2014 we are only working with 9 months, so it would seem reasonable to work with a number between 10% and 20%, given that dropout appears to be accelerating as time passes. Note however that if we do lower the dropout rate below 15% (green cell in the puzzle), then the dropout for patients acquired in 2013 and tracked in 2014 (Cell C) increases in proportion. For example, a 10% cumulative dropout for patients acquired in 2014 calculates a 42% dropout in 2014 for those patients who were acquired in 2013, while 15% calculates 39%.  15% here puts Aegerion in the better light when we get to the final tally for the 2013 cohort. 
Introduced to Juxtapid
Dropout rate
Dropout count
Remaining  patients
2013 patients in 2013
US Patient count as claimed in 4th quarter & full year financial results
2013 patients in 2014
not applicable
2014 patients in 2014
US patients, Revenue derived from 3rd Quarter 2014 income
On the other hand, if this dropout rate is higher than 15%, then either Aegerion’s 2013 disclosures are suspect or Aegerion has not fully disclosed new factors in 2014 dropouts.
[4] 430 Patients:
“When we ended 2013, we ended with over 430 net revenue U.S. patients on therapy, and 37 x U.S. net revenue patients on therapy.” ~ Aegerion Pharmaceuticals Management Discusses Q4 2013 Results - Earnings Call Transcript
“We ended 2013 with over 430 net revenue U.S. patients on therapy …” ~ AEGERION PHARMACEUTICALS ANNOUNCES FOURTH-QUARTER AND FULL-YEAR 2013 FINANCIAL RESULTS
[5] Patient Count estimate for 3rd Quarter 2014:
Net product sales for the third-quarter ended September 30, 2014 were $43.7 million, compared with $16.3 million in the third-quarter ended September 30, 2013. Net product sales for the nine months ended September 30, 2014 were $106.7 million, compared with $24.1 million for the nine months ended September 30, 2013. 91% of net product sales in the third quarter of 2014 were from prescriptions written for U.S. patients, while 9% came from ex-U.S. countries, primarily named patient sales in Brazil.” ~ Aegerion Pharmaceuticals Announces Third-Quarter 2014 Financial Results
Price Hike mentioned in Jefferies presentation: