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LDL-Lowering Genetic Variants Linked to Increased Risk of Diabetes

–What can genetic studies tell us about the risk of developing diabetes with cholesterol-lowering therapies?

This post was first published on MedPageToday and

 In a study published in the Journal of the American Medical Association, several LDL-lowering genetic variants were found to be associated with a reduced risk of coronary artery disease and an increased risk of type 2 diabetes. The study investigated LDL-lowering alleles in or near Niemann-Pick C1-Like 1 (NPC1L1), HMG-CoA reductase (HMGCR), PCSK9, ABCG5/G8, and low density lipoprotein receptor (LDLR). NPC1L1 is the target of ezetimibe, while HMGCR is the target of statins and PCSK9 is the target of PCSK9 inhibitors.

Luca Lotta, of the University of Cambridge, and colleagues conducted meta-analyses of genetic association studies, and included 50,775 individuals with type 2 diabetes and 270,269 controls and 60,801 individuals with CAD and 123,504 controls.

The study found that for a 38.7 mg/dL reduction in LDL-cholesterol, the genetic variants were associated with a similar reduction in risk of coronary artery disease, with odds ratios ranging from 0.54 to 0.62. However, genetic variants at the NPC1L1 locus were associated with a higher risk of diabetes (odds ratio 2.42) as compared to controls than the HMGCR and PCSK9 genetic variants (odd ratios of 1.39 and 1.19, respectively). The type 2 diabetes findings for NPC1L1 and HMGCR were highly significant (p = 9 x 107 and p = .003, respectively), but the p value for the type 2 diabetes finding for the PCSK9 variants was .03. The associations with type 2 diabetes for ABCG5/G8 and LDLR were not significant.

Treatment with statins is known to be associated with a higher incidence of new-onset diabetes, as is treatment with niacin, but the effect of ezetimibe and PCSK9 inhibitors on new-onset diabetes is unclear. An analysis of the IMPROVE-IT trial showed a small increase in new-onset diabetes in the ezetimibe group, but the difference was not statistically significant. The published data for the PCSK9 inhibitors have not shown statistically significant increases in blood sugar or new-onset diabetes (see here and here), but much more data will be available when the PCSK9 inhibitor outcomes trials are completed, starting next year.

One reason to wonder why PCSK9 inhibitors might increase blood sugar is that both statins and PCSK9 inhibitors have mechanisms of action that involve the removal of LDL from the bloodstream through upregulation of the LDL receptors. A recent study showed that patients with familial hypercholesterolemia, a disease that involves dysfunction of the LDL receptors, have a lower prevalence of type 2 diabetes as compared to their unaffected relatives. The study suggests that LDL receptor function may be involved in glucose homeostasis. I asked several experts in clinical trials or cardiovascular genetics to comment by email.


Sanjay Kaul, MD, of Cedars Sinai Medical Center in Los Angeles, sent the following comment by email:

The association of LDL-lowering alleles with CV risk is consistent across the 5 alleles. However, the association of LDL-lowering alleles with risk of T2DM is confined to NPC1L1 (large effect size, HR 2.4 and statistically robust p value) and to a lesser extent to HMGCR (HR 1.4, p = 0.003). This finding suggests an increased risk of incident diabetes with ezetimibe that targets NPC1L1. However, the incidence of new onset DM (defined as initiation of anti-diabetic medication during trial or two consecutive fasting glucose ≥126 mg/dL) in IMPROVE-IT trial was 720/5297 (13.6%) in EZ/SV vs. 694/5341 (13.0%) in SV, HR 1.04 95% CI (0.94, 1.15). The additional LDL lowering with ezetimibe was approximately 16 mg/dL which translates into a HR of 1.10 per mmol/L LDL lowering (1.04/16) x 38.7). This is considerably lower than the HR of 2.4 observed in the gene association study. Of course the median exposure in IMPROVE-IT was only 7 years when about 42% of subjects had discontinued treatment compared with the lifetime exposure in the gene association study. One would need a larger data set (meta-analysis of SHARP, SEAS, IMPROVE-IT, ARBITER-6, etc.) to better characterize the risk of incident T2DM. Even if we assume the association to be causal, remember the treatment effect in IMPROVE-IT was exclusively confined to the diabetic cohort who comprised 27% of the overall cohort (HR 0.86, 95% CI 0.78, 0.84 vs HR 0.98 for the non-diabetic cohort).

Joshua Knowles, MD, PhD, of Stanford University in California, sent the following comment:

This is an important paper by a very good group of investigators. The overall results of this Mendelian randomization study are not that surprising but are still very important, that there is an inverse relationship between LDL-C lowering genetic alleles and risk of type 2 diabetes.

There has been a lot of evidence emerging about this from the large statin trials to studies of [familial hypercholesterolemia (FH)] patients to prior Mendelian randomization studies.

The fact that they observe some heterogeneity of effect is interesting in that it might suggest that different ways of lowering LDL-C might result in different levels of risk for type 2 diabetes.

The overall effect they see for NPC1L1 genetic variants (a risk of 2.42 for type 2 diabetes for every 1 mmol/L reduction in LDL-C) suggests that this mechanism might theoretically be more potent for causing T2D risk.

However, in practice, ezetimibe does not lower LDL-C by 1 mmol/l but more like by ~0.5 mmol/L (or even less) so the actual effect size in ezetimibe trials (like IMPROVE-IT) will be less than 2.4 (probably more like 1.2). And in IMPROVE IT the effects might be masked to some extent as everyone was also on a statin and we don’t know if the effects would be additive.

The large scale PCKS9 trials will be revealing for their risk. Certainly these studies do not suggest that there will be a big effect which is good for the patients taking them now.

Please emphasize that overall message remains that for high risk patients (like FH) the beneficial effect of LDL-C lowering will greatly trump the increased risk of type 2 diabetes.

What is fascinating for me is we really have no idea whether the increased T2D risk is because the drugs decrease insulin secretion or increase insulin resistance. Knowing this will be critically important.

I am very interested in this topic and have a Doris Duke Clinical Investigator Grant to study it in a randomized trial. We will be measuring (with gold standard measures) insulin secretion and insulin resistance pre and post statin.

Remember that T2D is simply defined as an increase in blood glucose. These drugs seem to mostly push people that are ALMOST diabetic just over the threshold (see our recent paper published in the American Journal of Cardiology). Simply having an average blood glucose level go from 123 mg/dl to 127 mg/dl probably is not that important to a single person’s individual risk of downstream bad outcomes (though that person would go from being a pre-diabetic to a diabetic with that small change in blood glucose). What may be more important is HOW and WHY that blood glucose level rose. If there is not enough insulin being made, the treatment would potentially be different than if the body is not responding to insulin.

Another key message is to reemphasize the importance of exercise and maintenance of a healthy weight to potentially counteract the effect of these LDL-C lowering drugs on T2D. We should continue to advocate those important lifestyle choices for our patients. If you look at the data in [our paper] the risk of T2D with a statin is EXTREMELY low (3%) in those with normal fasting glucose and normal triglycerides (or weight) but very high in patients with pre-diabetes and high triglycerides (or overweight)– 23%!

I also received this comment from Daniel Swerdlow, MBBS, PhD, of Imperial College London:

This is a well-designed analysis that uses methods that are now established for using genetics to explore the effects of drug target modulation. The associations of the variants in NPC1L1 and PCSK9 on type 2 diabetes risk are not unexpected, as it appears from other large genetic analyses published recently that LDL-C lowering associates with higher diabetes risk, regardless of the mechanism through which this is achieved. This has been borne out in trials of statins and niacin, though the IMPROVE-IT trial of ezetimibe did not demonstrate an increased risk of diabetes in the treatment arm. The effect sizes in studies such as this are less informative than the direction of the effect, since direct comparison of the magnitudes of genetic effects and drug treatments is restricted by the differences in duration and potency of the two ‘interventions’. The biological analogy, however, allows the directions and scope of genetic associations to be interpreted as proxies for drug effects on the target encoded by the gene in question. The issue of new onset diabetes risk is pertinent for the PCSK9 inhibitors, and although analyses of trial data to-date has shown no association, they have been limited by duration of follow-up and sample size. The large phase 3 outcome trials are expected to focus carefully on diabetes risk with these new agents.

The over-riding message that analyses such as this in JAMA emphasize is that lipid-modifying treatments are only one part of cardiovascular risk reduction, and must be accompanied by lifestyle modification, such as appropriate diet and higher physical activity, in order to optimize risk reduction and mitigate against the small increase in diabetes risk that has been shown to be associated with some lipid-modifying drug treatments.

Assessing Drug Safety Post Approval: Lessons from Vioxx, Avandia, and Meridia – Part 2

In a recent post, I discussed a panel discussion on May 14, 2011, at the American Heart Association Quality of Care and Outcomes Research in Cardiovascular Disease and Stroke conference. The discussion addressed lessons from experiences with three drugs that were withdrawn or greatly restricted because they caused cardiovascular (CV) harm — rofecoxib (Vioxx), rosiglitzone (Avandia) and sibutramine (Meridia). I summarized the introduction by Sanjay Kaul and the presentations by Steve Nissen and Milton Packer. In this post I will discuss the presentations by statistician Dean Follmann of National Institute of Allergy and Infectious Diseases, NIH, and Ellis Unger of the Center for Drug Evaluation and Research, FDA.

Follmann’s presentation was similar to one he gave at the July 2010 joint meeting of the Endocrinologic and Metabolic Drugs Advisory Committee and Drug Safety and Risk Management Advisory Committee that was held to discuss Avandia. Follmann discussed the hierarchy of study designs, with randomized controlled trials (RCTs) that are double blind superiority trials being at the top. In such a design, randomization ensures that the groups are similar and double blinding ensures that the investigators can’t favor one arm over another. In addition, in a superiority trial the incentives encourage good study conduct because sloppiness (e.g. missing data, loose inclusion criteria, lack of adherence) makes it more difficult to show that the drug is effective. At the next level of reliability, according to Follman, are RCT noninferiority trials and meta-analyses. In a noninferiority trial, the goal is to conclude that a drug is not “unacceptably worse” than a comparator. In Follmann view, the incentives in a noninferiority trial “encourage sloppiness,” since sloppiness will tend to make the two arms more similar and thus meet the goal of noninferiority. (The RECORD trial was a noninferiority trial and was used to assess the safety of Avandia.) A meta-analysis is a quantitative synthesis of RCTs. In Follmann’s view, the quality of evidence of a meta-analysis is a bit less than a RCT, because (1) there may be unpublished trials that are not available for inclusion in the meta-analysis, (2) studies may be heterogeneous in population, endpoints, and comparators, and (3) the decisions on how to conduct the meta-analysis (e.g., what to include, how to analyze, endpoint definition) are made with knowledge of the potential safety signal. For example, to counter the Nissen-Wolski and FDA Avandia meta-analyses, which used myocardial infarction (MI) as the endpoint, GlaxoSmithKline chose a wider endpoint of serious and nonserious ischemia, resulting in a smaller hazard ratio. In addition, GSK used a “very unconventional and some would say illegitimate method of analyzing the data,” according to Follmann. Follmann also stated that it was a “revelation” to him to learn from Nissen’s presentation that GSK had done previous meta-analyses that had similar results as the Nissen-Wolski meta-analysis.

Follmann stated that the next study type in the hierarchy is observational studies. Because, observational studies are not randomized, drug choice may be based on patient characteristics, doctor preference, and unquantifiable factors. Statistical adjustment is done, but the result is less reliable than a RCT. Below observational studies are the FDA’s Adverse Event Reporting System (AERS) and data collected for other purposes, such as data collected by HMOs or the Centers for Medicare & Medicaid Services (CMS). In summary, Follmann stated that assessing a post marketing safety signal is difficult. RCTs are the best data source but are not always available.

Ellis Unger’s first remark was that Nissen had a “retrospectoscope in his back pocket” and was being a “Monday morning quarterback” with respect to the FDA’s actions concerning Vioxx and Avandia. He pointed out that the FDA has to make decisions in real time, which is not so easy, and he is not convinced that the FDA did the wrong thing, based on what it knew at the time. He does agree with the ultimate outcome for Vioxx, Avandia and Meridia. 

With respect to Vioxx, Unger stated that at the time of approval it was known that there were associations between Vioxx and hypertension and edema, but in the preapproval trials there were no differences with respect to MI and stroke. The VIGOR trial showed a hazard ratio of 1.94 for the composite endpoint of death, MI and stroke. For non-fatal MI, the hazard ratio was 4.51 (p < 0.05). He does not believe the VIGOR data were enough that Vioxx should have been removed from that market at that point (2000). Unger next discussed the APPROVe trial, which was stopped two months early due to an excess in serious thrombotic events in the Vioxx group (RR 1.92), and resulted in the voluntary removal of Vioxx from the market. In the wake of Vioxx’s withdrawal, the FDA held a joint meeting of the Arthritis and Drug Safety and Risk Management Advisory C0mmittees on February 16-18, 2005 to discuss Cox-2 inhibitors. Unger summarized the data presented at the meeting as follows: (1) “all Cox-2-selective agents seem to increase CV risk (no ranking)” and (2) “available data do not support greater CV risk for selective agents as compared to non-selective agents.”  After the meeting, the FDA added labeling warning of the potential for increased risk of CV thrombotic events to all NSAIDs.

With respect to rosiglitazone, Unger stated that the evidence of cardiovascular risk is “neither robust nor conclusive” and “remains an open question,” while acknowledging that there were “multiple signals of concern from various sources of data, without reliable evidence to refute risk.” He stressed the limitations of the Nissen/Wolski meta-analysis, including that the results were based on a relatively small number of events. Interestingly, Unger said that the FDA was more worried about the finding for cardiovascular death (odds ratio 1.64, p = 0.06) than the finding for MI (odds ratio 1.43, p = 0.03), even though the result for CV death was not statistically significant. Unger views the ADOPT and DREAM trials as being neutral on cardiovascular death, with both showing trends for increased MI.

With respect to the RECORD trial, Unger criticized the open-label design and possibility of ascertainment bias but also stated that the results for all-cause death are “unlikely to be influenced by bias,” and showed a favorable trend for rosiglitazone. With respect to MI, the results were “inconclusive,” as neither the GSK nor the FDA analysis showed a statistically significant increase in MIs. Unger stated that viewed as a means to test the two hypotheses generated by the Nissen/Wolski meta-analysis — rosiglitazone causes MI and increases the risk of CV death — RECORD “does not substantiate the findings of the Nissen/Wolski meta-analysis.” (For more on Unger’s views on RECORD, see his slides from the 2010 advisory committee meeting on rosiglitazone here).  Finally, Unger noted that the David Graham epidemiological study of Medicare patients did not find a statistically significant higher risk of MI with rosiglitazone as compared to pioglitazone. Why didn’t the FDA take rosiglitazone off the market instead of leaving it on the market with restricted access? Unger cited conflicting data on the existence and magnitude of risk, the need for detailed re-adjudication and analysis of RECORD, the fact that some patients are currently taking rosiglitazone and want to stay on it even with knowledge of the risk.

With respect to sibutramine (Meridia), a weight loss drug that is an inhibitor of norepinephrine, serotonin and dopamine reuptake, Unger noted that at approval in 1997 the drug was known to increase blood pressure and heart rate and result in miscellaneous ECG changes, but the adverse effects were deemed “monitorable.” The European regulators, however, required a post-marketing cardiovascular outcomes study. This was the SCOUT trial, a large randomized, double-blind, placebo-controlled trial in obese patients over age 55 with a history of coronary artery disease, peripheral vascular disease, or stroke and/or Type 2 diabetes with at least one other risk factor. The primary endpoint was a composite of CV death, resuscitation after cardiac arrest, non-fatal MI and non-fatal stroke, which occurred in 11.4% of the patients on sibutramine and 10.0% of the patients on placebo (HR 1.16, p = 0.02). Following this trial, sibutramine was removed from the market in the U.S. and Europe.

Unger noted that post-marketing safety used to focus on rare, severe events that were detectable from spontaneous reporting. In recent years, there has been greater interest in small increases in common but serious events, such as MI, stroke, and CV death. Quantification of common risks is challenging with longer, larger studies required. If the drug is for a symptomatic condition such as depression or pain, it is difficult to keep patients from dropping out of the trial. It is difficult to interpret the results of a trial when there have been a lot of dropouts.

Unger stated that when the FDA reviews clinical trial data they are interested in imbalances in virtually any safety issue so we “always see safety signals because we look at 150 adverse events.”  They have to consider a number of issues in assessing causality:  whether there is a plausible mechanism of action, whether it has been observed in other related drugs, whether there is a dose-response relationship, etc.

Comment: I think the problem of post approval safety is not entirely solvable, because there will always be safety signals that crop up after drugs are approved. However, I am in sympathy with Dr. Nissen’s view that safety signals should be investigated and acted on as early as possible, and preferably before approval.

Also, on the topic of Vioxx specifically, I suggest the following for further reading:

Joseph S. Ross, MD, MHS; Kevin P. Hill, MD, MHS; David S. Egilman, MD, MPH; Harlan M. Krumholz, MD, SM. Guest Authorship and Ghostwriting in Publications Related to Rofecoxib: A Case Study of Industry Documents From Rofecoxib Litigation. JAMA. 2008;299(15):1800-1812.

Keven P. Hill, MD, MHS; Joseph S. Ross, MD, MHS; David S. Egilman, MD, MPH; Harlan M. Krumholz, MD, SM. The ADVANTAGE Seeding Trial: A Review of Internal Documents. Annals of Internal Medicine. 2008;149(4):251-258.

Joseph S. Ross, MD, MHS; David Madigan, PhD; Kevin P. Hill, MD, MHS; David S. Egilman, MD, MPH; Yongfei Wang, MS; Harlan M. Krumholz, MD, SM. Pooled Analysis of Rofecoxib Placebo-Controlled Clinical Trial Data: Lessons for Postmarket Pharmaceutical Safety Surveillance. Archives of Internal Medicine. 2009;169(21): 1976-1985.

Joseph S. Ross, MD, MHS; David Madigan, PhD; Marvin A. Konstam, MD; David S. Egilman, MD, MPH; Harlan M. Krumholz, MD, SM. Persistence of Cardiovascular Risk After Rofecoxib Discontinuation. Archives of Internal Medicine. 2010;170(22):2035-2036.

Snigdha Prakash, All the Justice Money Can Buy: Corporate Greed on Trial (2011) (book by former NPR reporter Snigdha Prakash on the Vioxx saga — focuses on a particular Vioxx trial).

Assessing Drug Safety Post Approval: Lessons from Vioxx, Avandia, and Meridia – Part 1

A panel discussion on May 14, 2011, at the American Heart Association Quality of Care and Outcomes Research in Cardiovascular Disease and Stroke conference addressed lessons from three drugs — rofecoxib (Vioxx), rosiglitzone (Avandia) and sibutramine (Meridia) — that were found post approval to increase cardiovascular risk and subsequently either withdrawn or severely restricted. The panelists were Steven Nissen of the Cleveland Clinic, Milton Packer of University of Texas Southwestern Medical Center, Dean Follmann of National Institute of Allergy and Infectious Diseases, NIH, and Ellis Unger of the Center for Drug Evaluation and Research, FDA and the moderator was Sanjay Kaul of Cedars-Sinai Medical Center.
In his introductory remarks, Kaul emphasized the asymmetry in the evidence base for assessing efficacy and safety in drug approval. Efficacy is assessed pre-approval by randomized controlled trials (RCTs) with prespecified, adjudicated endpoints. If a safety signal emerges in these efficacy trials, the adverse events are not prespecified and the studies are often not adequately powered to reliably determine risk. Few RCTs are conducted to assess safety pre-approval. Thus, safety is assessed post-approval with meta-analyses, observational databases, the FDA’s Adverse Event Reporting System (AERS), or RCTs that involve limited exposure and/or narrow populations. Thus, the evidence for determining efficacy is often much superior to the evidence for determining safety.
Steven Nissen outlined “critical lessons learned” from rofecoxib and rosiglitazone experiences. First, post-approval studies and spontaneous AE reporting are ineffective at detecting increased risk of common sources of morbidity and mortality, such as cardiovascular disease. Second, for the cardiovascular hazards of rofecoxib and rosiglitazone, strong signals suggesting harm appeared early, but were missed or actively concealed. Third, dedicated post-approval safety studies take many years, and are vulnerable to manipulation, mischief, and flaws in study design or conduct. Thus, the last line of defense against unsafe drugs is often the drug approval process, because “once the genie gets out the bottle, it is very hard to put it back.”
In the case of rofecoxib, which was approved in 1999, Nissen said the safety signal emerged the following year when the VIGOR trial was published. Buried in the “General Safety” section was a sentence stating that “Myocardial infarctions were less common in the naproxen group than in the rofecoxib group (0.1 percent vs. 0.4 percent; 95% confidence interval for the difference, 0.1 to 0.6 percent; relative risk 0.2; 95% confidence interval 0.1 to 0.7)” (emphasis added). Nissen called this phrasing “diabolical” and noted that “no one saw this.” Moreover, the table and Kaplan-Meier curves for thrombotic events were omitted from the manuscript. After the table showing the number of events and the Kaplan-Meier curves were made available in connection with an FDA advisory committee meeting, Nissen and colleagues published the data in JAMA, “creating a furor and lots of slings and arrows, but it didn’t do a thing,” according to Nissen. Vioxx sales continued to grow and ultimately a total of 105 million prescriptions were written, exposing 20 million Americans to the drug. In 2004, the APPROVe study was stopped by the Data Safety Monitoring Board when an excess of thrombotic events became evident, leading to the drug’s withdrawal. Nissen described how the APPROVe study too was published in a misleading way, making it appear that there was an 18-month delay before the excess risk became evident. Documents that were disclosed in litigation revealed a previously undisclosed intention-to-treat analysis. The ITT analysis showed an early hazard with no 18 month delay. In Nissen’s view, these misleading trial publications demonstrate that the medical community just can’t trust that industry-sponsored clinical trial data will be published in a way that is not misleading.
Nissen conducted a similar analysis of the history of rosiglitazone, arguing that a safety signal was evident in the pre-approval trials, in which there was an excess of ischemic myocardial events for rosiglitazone, as well as a worrisome 18.6% increase in LDL. Nissen also described corporate misconduct, including the intimidation of a leading diabetes researcher, buried data and GSK meta-analyses that were conducted in 2005 and 2006, before the 2007 Nissen/Wolski meta-analysis. The GSK meta-analyses showed an increased risk of ischemic myocardial events and were shared with the FDA but not with physicians or patients. (For a detailed rosiglitazone chronology, see Nissen’s 2010 editorial, “The rise and fall of rosiglitazone,” as well as Nissen’s slides from the July 2010 FDA advisory committee meeting on rosiglitazone). Of note, the Nissen/Wolski meta-analysis was only made possible because a settlement with the New York attorney general’s office had required GSK to make all its clinical trial data available. Nissen/Wolski found the data on a GSK website and published their meta-analysis. Without the disclosure required by the settlement with New York state, the meta-analysis would not have been possible, as 35 out of 42 clinical trials were unpublished. As for the RECORD trial, Nissen described it as a texbook example of “how not to perform a safety study.” The trial was completely unblinded to patients and physicians and there was unrestricted availability of treatment codes to the contract research organization and GlaxoSmithKline (GSK). In addition, the study leadership removed silent heart attacks (10 to 5, rosiglitazone vs. control) from the database after analyzing the data. Nonetheless, Nissen believes, based on the reanalysis of the RECORD data by the FDA’s Thomas Marciniak (see Marciniak’s slides) that RECORD didn’t show, as argued by GSK, that rosiglitazone was safe; “it demonstrated that the drug was unsafe.” The lesson Nissen believes we should learn from Vioxx, Avandia and Meridia is that “you’ve got to stop these things at the approval process, and when early safety signals are seen, it requires aggressive regulatory action, at the very least demanding that well-conducted safety trials be done. In these three cases, that didn’t happen. Drugs stayed on the market too long and too many people were harmed.”
Milton Packer gave a presentation that was a greatly condensed version of one he gave in February 2005 at the FDA advisory committee meeting on Cox-2s (slides here; transcript here). He emphasized the difficulty of interpreting observed differences in the frequency of events when the number of events is small.  The difficulty is that in an efficacy trial, the trial is sized for efficacy, not safety. Where the number of events is small, the point estimates will be extremely imprecise and the confidence intervals will be wide. Even if the result is statistically significant and the effect is biologically plausible, it is often not possible to be certain the effect is real. Packer gave the example of a Vioxx meta-analysis conducted by Peter Juni and colleagues and published in The Lancet in December 2004, after the withdrawal of Vioxx. Based on a cumulative meta-analysis, the authors concluded that by the end of 2000 the relative risk was 2.30 (95% CI 1.22-4.33, p=0.01) and that Vioxx should have been withdrawn at that time. Packer points out that this analysis was based on only 52 events. He believes that was not enough events to draw reliable conclusions. As evidence, Packer gave examples of small pilot trials that gave results that were not confirmed when larger definitive trials were done. Packer doesn’t disagree with the actions that were ultimately taken with respect to Vioxx, Avandia and Meridia, he just questions that it was possible to know what the risks were early on.
In Part 2 of this post, I will discuss the presentations by Dean Follman and Ellis Unger.