The WAVE trial: The Canadians set the standard once again

Today's NEJM contains the report of an exemplary trial (the WAVE trial) comparing aspirin to aspirin and warfarin combined in the prevention of cardiovascular events in patients with peripheral vascular disease (http://content.nejm.org/cgi/reprint/357/3/217.pdf). Though this was a "negative" trial in that there was no statistically significant difference in the outcomes between the two treatment groups, I am struck by several features of its design that are worth mentioning.

Although the trial was the beneficiary of pharmaceutical funding, the authors state:

"None of the corporate sponsors had any role in the design or conduct of the trial, analysis of the data, or preparation of the manuscript".

Ideally, this would be true of all clinical trials, but right now it's a precocious idea.



One way to remove any potential or perceived conflicts of interest might be to mandate that no phase 3 study be designed, conducted, or analyzed by its sponsor. Rather, phase 3 trials could be funded by a sponsor, but are mandated to be designed, conducted, analyzed, and reported by an independent agency consisting of clinical trials experts, biostatisticians, etc. Such an agency might also receive infrastructural support from governmental agencies. It would have to be large enough to handle the volume of clinical trials, and large enough that a sponsor would not be able to know to what ad hoc design committee the trial would be assigned, thereby preventing unscrupulous sponsors from "stacking the deck" in favor of the agent in which they have an interest.

The authors of the current article also clearly define and describe inclusion and exclusion criteria for the trial, and these are not overly restrictive, increasing the generalizability of the results. Moreover, the ratinoale for the parsimonious inclusion and exclusion criteria are intuitively obvious, unlike some trials where the reader is left to guess why the authors excluded a particular subgroup. Was it because it was thought that the agent would not work in that group? Because increased risk was expected in that group? Because study was too difficult (ethically or logistically) in that group (e.g., pregnancy). Inadequate justification of inclusion and exclusion criteria make it difficult for practitioners to determine how to incorporate the findings into clinical practice. For example, were pregnant patients excluded from trials of therapeutic hypothermia after cardiac arrest (http://content.nejm.org/cgi/reprint/346/8/549.pdf) for ethical reasons, because of an increased risk to the mother or fetus, because small numbers of pregnant patients were expected, because the IRB frowns upon their inclusion or for some other reason? Without knowing this, it is difficult to know what to do with a pregnant woman who is comatose following cardiac arrest. Obviously, their lack of inclusion in the trial does not mean that this therapy is not efficacious for them (absense of evidence is not evidence of absense). If I knew that they were excluded because of a biologically plausible concern for harm to the fetus (and I can think of at least one) rather than because of IRB concerns, I would be better prepared to make a decision about this therapy when faced with pregnant patient after cardiac arrest. Improving the reporting and justification of inclusion and exclusion criteria should be part of efforts to improve the quality of reporting of clinical trials.

Interestingly, the authors also present an analysis of the composite endpoints (coprimary endpoints 1 and 2) that excludes fatal bleeding or hemorrhagic stroke. When these side effects are excluded from the composite endpoints, there is a trend favoring combination therapy (p values 0.11 and 0.09 respectively). Composite endpoints are useful because they allow a trial of a given number of patients to have greater statistical power, and it is rational to include side effects in them, as side effects reduce the net value of the therapy. However, an economist or a person versed in expected utility theory (EUT) would say that it is not fair to combine these endpoints without first weighting them based on their relative (positive or negative value). Not weighting them implies that an episode of severe bleeding in this trial is as bad (negative value or utility) as a death - a contention that I for one would not support. I would much rather bleed than die, or have a heart attack for that matter. Bleeding can usually be readily and effectively treated.

In the future, it may be worthwhile to think more about composite endpoints if we are really interested in the net value/utility of a therapy. While it is often difficult to assign a relative value to different outcomes, methods (such as standard gambles) exist and such assignment may be useful in determining the true net value (to society or to a patient) of a new therapy.