The use of 95% credible intervals presented in Table 2 of [8] are correct. However, in the introductory text to Table 2, the article mistakenly refers to them as confidence intervals. Specifically, on page 2,609 of the published article (Polack et al., 2020), it states:

“This case split corresponds to 95.0% vaccine efficacy (95% confidence interval [CI], 90.3 to 97.6; Table 2).”

The main difference between confidence and credible intervals lies in their interpretations and the evidence the two intervals represent. In particular, a 95% credible interval ( 90.3 % , 97.6 % ) for VE means that given the data, the probability that VE is greater than 90.3% but smaller than 97.6% is 0.95. In contrast, a 95% confidence interval ( 90.3 % , 97.6 % ) for VE is interpreted as the following: were the vaccine trial repeated numerous times, the fraction of the calculated confidence intervals (which would be different for each trial) that encompass the true VE would tend towards 95% [3]. Apparently, the Bayesian credible interval directly evaluates the probability of the true vaccine efficacy based on the observed trial data, and the frequentist confidence interval only provides an indirect assessment assuming the vaccine trial were to repeat numerous times [6].

This mis-labeling of Bayesian credible interval and frequentist confidence interval is pervasive in the literature. For example, in [4], the authors report results of Bayesian meta-analysis but refer to the intervals as confidence intervals. Similarly, [2] initially define “CI” as confidence interval for a frequentist meta-analysis, yet continue to use the same term when presenting results from a Bayesian meta-analysis, which should be reported using credible intervals instead. Likewise, [5] apply a Bayesian classifier, but refer only to confidence intervals throughout, failing to distinguish the Bayesian framework from frequentist terminology.

Therefore, we recommend that authors explicitly include the interpretation of the Bayesian credible interval when it is first introduced in a publication to avoid confusion between the Bayesian credible interval and the confidence interval. Since part of the confusion is due to the shared abbreviation “CI” for both terms, which may mislead investigators into assuming they are interchangeable, we suggest using the abbreviation “BI” for Bayesian credible interval in publications, to clearly distinguish it from “CI”.

Interestingly, the posterior probabilities of the BNT162b2 VE are only reported in the DISCUSSION section of [8] but not the RESULTS section. The EFFICACY subsection in RESULTS focused on the reporting of the credible intervals, although the credible intervals were mistakenly written as confidence intervals. In addition, credible intervals are not directly linked to the clinically meaningful effect such as having the VE of vaccine greater than a clinical threshold like 30%.

An advantage of the Bayesian modeling for the BNT162b2 vaccine trial data is the ability to report the vaccine efficacy with probabilistic statements, a feature that is not available through p-values or confidence intervals. Clinically, a direct answer to the vaccine efficacy based on clinical trial data is a statement like the following: “Given the observed efficacy data in the BNT162b2 trial, the probability that the true vaccine efficacy exceeds x% is greater than y,” in which x is prespecified by investigators as a meaningful clinical threshold, and y is calculated based on Bayesian models.

Bayesian results provide direct answers to clinical questions. To see this, we list four recommended Bayesian reporting elements for clinicians and decision makers.

The details of the Bayesian models and inference used in the BNT162b2 vaccine trial were not reported in either the trial protocol or the publication [8]. We reproduced the reported Bayesian results in the primary efficacy analysis in [8]. See Supplemental Material for details of our model that reproduced the results. It is not the first choice of a Bayesian model that we would use, however, since the model is not compatible with the sampling scheme based on the trial design. An alternative model that is more natural and compatible with the trial design is presented in Supplemental Material, where the BI from the alternative model is (90.9%, 97.9%), which is a bit shorter than the reported BI.

We report the following simple beta-binomial model that reproduces the BIs in [8]. This model assumes that the number of COVID cases in the vaccine group is sampled as a binomial random variable from the total number of COVID cases in both groups, vaccine and placebo. In mathematics, this means X ∣ N , θ ∼ Bin ( N , θ ) , where X denotes the number of cases in the BNT162b2 group and N the total number of cases in both groups. Therefore, ( N − X ) is the number of cases in the placebo group. Here, θ is interpreted as the probability that an observed COVID case is from the vaccine group and ( 1 − θ ) is the probability that it is from the placebo group, when a COVID case is observed. Note that the probability sampling space is restricted to only the COVID cases, not including any non-cases.

A beta prior Beta(0.700102,1) is proposed for θ in the BNT162b2 protocol [1]. Also, the trial protocol assumes θ = ( 1 − VE ) / ( 2 − VE ). This assumption means that VE = 1 − θ / ( 1 − θ ), which resembles the definition of VE = 1 − I R R. However, it is important to note that θ is not the probability of COVID rate in the vaccine group.

Following this model, with fixed N and X, the posterior distribution of θ is also a beta distribution, (3.1) θ ∣ N , X ∼ Beta ( 0.700102 + X , 1 + N − X ) . For the BNT162b2 trial, X = 8 and N = 170 for VE 1 and X = 9 and N = 178 for VE 2 , where VE 1 and VE 2 are the two primary efficacy endpoints of the trial. Therefore, the BIs of VE can be calculated via sampling θ from its beta posterior distribution (3.1) and are shown in Table S.1 below. They are identical to the reported BIs in [8]. In addition, using the posterior distribution of θ, we easily calculate the posterior probabilities of VEs. For example, Pr ( VE 1 > 30 % ∣ data ) > 0.9999 and Pr ( VE 2 > 90 % ∣ data ) = 0.98. See attached computer program for detail.

We also propose an alternative model that is more natural and compatible with the trial design. Recall that the design of the BNT162b2 trial first enrolls participants without COVID diagnosis, and then follows them for certain time period to observe disease occurrence. This means that for each of the two groups, vaccine and placebo, a binomial sampling is carried out, assuming a homogeneous disease rate within each group and not considering different surveillance time of each patient. In particular, let p 1 and p 2 denote the probabilities of COVID for the vaccine and placebo groups, respectively. Among the N 1 participants in the vaccine group and N 2 in the placebo group, let X 1 and X 2 represent the corresponding numbers of COVID cases.

Since participants in the vaccine and placebo groups are treated and followed independently, we assume the following independent binomial sampling distributions, i.e., X i ∣ N i , p i ∼ Bin ( X i ∣ N i , p i ) , i = 1 , 2 . Note that by definition, VE = 1 − p 1 / p 2 . Therefore, a Bayesian model and inference is completed by a prior and posterior distribution of ( p 1 , p 2 ).

We assume that p 1 and p 2 follow improper and independent prior distributions. In other words, f ( p i ) ∼ 1. This prior leads to proper independent posterior distributions, given by p i ∼ Beta ( X i , N i − X i ), i = 1 , 2. Using the two beta posterior distributions, the estimated VE and its BIs can be calculated by numerical methods with random sampling of the two beta distributions (see attached computer program). As a comparison to the results in [8], Table S.2 presents the reported BIs using the alternative model. The first BI (90.9, 97.9) in the table is slightly shorter than the one (90.3, 97.6) in the paper. More importantly, this model presents the posterior distributions of p 1 and p 2 , the two infection rates for the vaccine and placebo, as shown in Figure S.1.

The prior Beta(0.700102,1) is chosen to reflect a conservative assumption θ = 0.4118 (corresponding to VE = 30 %) while allowing for substantial uncertainty [1]. For comparison, we include additional prior settings to evaluate performance across the two models, as summarized in Table S.3. Specifically, we let p 1 ∼ Beta ( 0.35 , 0.65 ) and p 2 ∼ Beta ( 0.5 , 0.5 ) in the alternative model, reflecting the same conservative assumption of VE = 30% and keeping the prior weakly informative.

As shown in Table S.3, the posterior means and BIs under the improper prior are identical across the two models. Under the weakly informative priors—each encoding VE = 30 % as the prior mean—the posterior means from both models shift closer to 30%. Notably, the Beta(0.700102, 1) prior leads to a slightly smaller posterior mean due to its relatively larger impact compared with Beta(0.0700102, 0.1). However, the change in the posterior inference would not have altered the overall high efficacy of the vaccine.