Clarification: Pfizer and Moderna’s “95% effective” vaccines—we need more details and the raw data

Post-publication clarification to Peter Doshi’s 4 January 2021 Opinion piece.

In response to feedback received following publication, I would like to clarify certain aspects of my article.

First, regarding the 3410 “suspected covid-19” cases and my calculations of 19% and 29% vaccine efficacy, readers have posited that this relied on the assumption that all 3410 cases were false negatives and therefore actually true covid-19 cases. While it is correct to say that one could arrive at these figures by making that assumption, and also that others have made similar calculations in discussing the potential implications of the 3410 suspected covid-19 cases, the 19% and 29% calculations I made in my article did not rely on assuming anything about false negatives. My calculations were of vaccine efficacy against an endpoint of “covid-19 symptoms, with or without a positive PCR test result” (i.e. irrespective of what is causing those covid-19 symptoms, whether that be SARS-CoV-2 or something else). This was not the primary endpoint used in the trials, which was laboratory confirmed covid-19 (symptoms plus positive lab test). The rationale for considering vaccine efficacy against the syndrome people wish to avoid (and not simply against the proportion of that syndrome caused by one particular virus) may be made by way of analogy with influenza: Cochrane reviews influenza vaccines have long reported on vaccine performance against not only influenza, but also influenza-like illness (ILI) which is a syndrome defined by symptoms and not based on laboratory tests.  

Regarding the impact of false negative PCR test results, which is a longstanding concern about the tests, I wrote, “If many or most of these suspected cases were in people who had a false negative PCR test result, this would dramatically decrease vaccine efficacy.” What I should add is that, even if it was just some (i.e. not many or most) of the 3410 cases, this too would have the effect of reducing vaccine efficacy against covid-19, but certainly less dramatically than if “many or most” were false negatives. Obviously, the higher the rate of false negatives, the larger the reduction in efficacy against covid-19, and nobody knows the true false negative rate in the trials, nor is it clear to me whether all 3410 suspected covid-19 cases were even tested (I would hazard a guess that the vast majority were tested, but we only know they “were not PCR-confirmed”). Ultimately, the calculations and statements about reduced vaccine efficacy were meant to help illustrate why I think there is a pressing need to better understand the “suspected covid-19” category, and this applies to all covid-19 vaccine trials, not just Pfizer’s trial and not just mRNA vaccines, as I think it is fair to assume that similar cases will occur in all trials.

Second, in reference to the final section of my article, readers have commented that the European Medicines Agency (EMA) is not likely to release individual participant datasets from covid-19 vaccine trials. I agree with this comment and did not intend to suggest that the EMA would do this, for the simple reason that the EMA does not itself routinely receive individual participant data from industry, so the agency has none to release.

Third, the views and opinions expressed are mine and do not necessarily reflect official policy or position of the University of Maryland, my employer.

Peter Doshi, associate editor, The BMJ

Competing interests: I have been pursuing the public release of vaccine trial protocols, and have co-signed open letters calling for independence and transparency in covid-19 vaccine related decision making.