Science and Law
26th June 2013

Causality Assessment in Pharmaceutical Litigation: 6 Types of Information that Should be Assessed in Drug and Device Cases

The controversy surrounding incretin-based diabetes therapies presents an ideal opportunity to examine how a proper evaluation of the evidence should be conducted, including arriving at a reliable and appropriate assessment of causality.

Causality Assessment in Pharmaceutical Litigation:  6 Types of Information that Should be Assessed in Drug and Device Cases

This post was drafted by David H. Schwartz, a principle at Innovative Science Solutions, and John Clark, MD,MSPH, who provides expertise to ISS in litigation involving pharmacovigilence and adverse event reporting. Dr. Clark has consulted with corporations and defense counsel on mass tort and complex litigation for the past three decades. He has worked with individual clients and industry groups on management of some of the country’s largest and most prominent pharmaceutical issues. Dr. Clark is the president and chief medical officer at PCSglobal, a pharmaceutical, biotechnology and medical device industries consulting company specializing in risk-reduction services.  

The controversy surrounding incretin-based diabetes therapies presents an ideal opportunity to examine how a proper evaluation of the evidence should be conducted, including arriving at a reliable and appropriate assessment of causality.

In our last post on this topic, we highlighted the flaws in the assertion that glucagon-like peptide 1 (GLP-1)-based medications pose a safety risk. In this post, we use this controversy as a case study to set forth the steps that should be taken when conducting a proper evaluation of the scientific evidence.

Defense counsel can refer to this approach in any drug or device case involving an exposure and allegations of an adverse health outcome.

A comprehensive evaluation of any proposed relationship between a pharmaceutical exposure and an AE requires a systematic review of all pertinent risk information. Below we set forth six types of information that should be assessed in drug and medical device cases.

1.       Comprehensive AERS Signaling Analysis

As we discussed in that earlier post, arguments on both sides of the issue are set forth in a pair of dueling editorials published ahead of print in the journal Diabetes Care:  an article by Peter Butler and colleagues sets forth the case that GLP-1 therapies pose an undue health risk, while an article by Michael A. Nauck takes the position that the benefits of these drugs outweigh the potential risks.

As Dr. Butler points out, FDA’s FAERS Database provides a unique, initial resource for gauging whether or not a drug exposure could be related to an AE. It serves as an excellent and important hypothesis generation tool. However, this type of analysis should not be considered evidence of causality.

The standard methods for showing disproportionality using FAERS are called “signal of disproportionate reporting” (SDR) algorithms. It is well accepted in the pharmacovigilence community that SDR algorithms produce far more false positives than false negatives. Butler’s use of FAERS to establish causality is therefore not appropriate. Rather, FAERS should be used as a screening tool to determine whether a signal should be followed up with more rigorous tools. More definitive analyses, such as the observed to expected (OTE) or other standard, controlled epidemiological designs (see below) should be used to more rigorously assess causality.

2.       Observed to Expected (OTE) Analyses

OTE analyses go a step beyond AERS-based signaling by comparing the actual number of reported cases (observed count) for a particular AE to the number of cases that would have been expected in the absence of exposure (expected count). OTE analyses can be carried out using either surveillance data (such as the FAERS Database or the manufacturer’s database) or more fully ascertained data sources (such as claims data, registries, or electronic medical records).

If a well conducted OTE analysis suggests the presence of a drug-AE effect, we then look for additional sources of data that may be amenable to epidemiological analysis. However, an AE cannot be studied using the epidemiological designs explained below if sufficient data is not available, or measurement of exposure, event or both is unreliable.

3.     Summary of Published Literature Analysis

For most purported drug-AE associations, a reasonable set of animal and human clinical data is typically published, including the present example of GLP-1 agents and pancreatitis or pancreatic cancer. All of the relevant literature must be identified, compiled, reviewed, and carefully summarized.

4.      Summary and Analysis of Unpublished Company Data

In addition to the published literature, sources of unpublished data — compiled by the manufacturer — are frequently available. This company data must also be carefully reviewed and summarized.

5.      Epidemiological Analyses

Occasionally it may be appropriate to initiate a well designed case-control or cohort study (the two primary epidemiological study designs) to supplement the published and unpublished data reviews, particularly if the existing postmarketing case data is ambiguous or sparse. These types of studies can often be performed using existing data sets such as electronic hospitalized records databases or even claims databases.

6.       Systematic Review of the Data

A systematic review and summary of the published and unpublished data will serve as the ultimate basis for the assessment of causality. Such an assessment must adhere to an objective and systematic evaluation of all the available data, including clearly defined inclusion and exclusion criteria. This review must be conducted by individuals of varying scientific and medical disciplines, including epidemiologists, clinical trialists, toxicologists, pathologists, and endocrinologists.

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