We’ve worked with many drug and device manufacturers over the years whose products were linked to negative health outcomes based on case report evidence or adverse event data. As we mentioned in our last post, scientists consider case reports to be among the least reliable forms of evidence.
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.
We’ve worked with many drug and device manufacturers over the years whose products were linked to negative health outcomes based on case report evidence or adverse event data. As we mentioned in our last post, scientists consider case reports to be among the least reliable forms of evidence. So how can the defense team respond when the plaintiff’s allegations are based on case reports? Below we discuss three strategies that can be used to cast doubt on causality.
1. Temporal Tests
Addressing each of the temporal tests listed below helps to provide some level of evidence that the exposure of interest is causally implicated in the observed reaction. While none of these factors (together or separately) is definitive evidence of causation, they are each important factors to be evaluated.
Determining answers to each of the temporality tests is a first step in assessing whether the exposure is causally implicated in the observed reaction. While none of the temporality tests is necessary or sufficient to prove causation, taken together, they help to support the view that there may be a causal relationship. A positive re-challenge (the last temporality test) is the only one of the four temporal tests that is highly predictive of a causal relationship.
2. Safety Signaling Algorithms
Safety signaling algorithms, such as the FDA’s MGPS algorithm or the WHO’s BCPNN algorithm, are mathematical procedures that identify potential causal relationships between exposures of interest and observed adverse reactions in large adverse event databases. A safety signaling algorithm should ideally be based on comparative drugs (referred to as a “background report set”) in which the indication and dosage form are consistent with the target drug. For example, in the case of the anti-malarial agent mefloquine, oral prophylaxis for malaria should be evaluated. This approach is important because the characteristics of a drug’s target population and its method of administration are often the most important factors in reported adverse events. Thus, performing a safety signaling algorithm using a background report set of other drugs prescribed for oral prophylaxis for malaria would tell us whether the observed reactions were a result of mefloquine specifically or if they were due to characteristics of the drug’s target population.
3. Observe to Expected Model
Observed to expected models (sometimes referred to as “O to E models”) are often constructed from adverse event case series. The concept behind an O to E model is to ask the following question: Now that I know how many cases were reported (number of observed cases), how many cases would I have expected by chance (number of expected cases)? As the observed number of cases begins to exceed the number of expected cases (i.e., the O to E ratio rises above 1), causality becomes increasingly likely.
When using the O to E model, you may face some difficulties. First, a vast amount of information is required, some of which may not be available. Second, O to E models are based on statistical and numerical assumptions, which make interpreting results difficult, especially when the O to E model suggests an elevated risk that is relatively low (O to E ratios between 1 and 2). Despite these difficulties, the development of O to E models from case series remains a valuable tool in case series interpretation because it helps to establish whether or not the signal is based on a true quantitative (rather than a purely case-based) argument.
From the three basic tools outlined above, all of which are derived from reported cases, manufacturers can assess case reports and develop a sense of whether there is evidence supporting the causality of a safety signal. The great majority of signals that have been ruled out as false (using the tools outlined above) can then either be tracked routinely or discarded as non-contributory.
If, after the measures described above have been applied, a signal continues to look reliable and consistent, the manufacturer should consider the possibility of conducting a rigorously designed epidemiological study, or, in more emergent situations, initiating an exposure registry with periodic assessments of the alleged side effects. Such studies are at the heart of good product stewardship and will help to demonstrate that your client did everything they could to learn about risks to their drugs or devices. If your client is already facing litigation, such studies can help to either rule out causation or provide a basis for next steps in the litigation decision-making process.
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