BioNTech-Pfizer COVID-19 Vaccine Candidate Efficacy - Part 4

Today Pfizer and BioNTech released findings from their latest analysis of their COVID-19 vaccine candidate. The press release is based on more recently available data. There have been 41,135 participants who have received both doses and 170 confirmed cases of COVID-19; 162 cases occurred in the placebo group while 8 cases occurred in the treatment group.

Based on this information, I re-ran the model from my earlier post. The plots below show the posterior distribution for vaccine candidate efficacy and the group probabilities of COVID-19.

## Warning: It is deprecated to specify `guide = FALSE` to remove a guide. Please
## use `guide = "none"` instead.

## Inference for Stan model: 88c4792d2ae31f0d91937cb65a7a3c38.
## 4 chains, each with iter=6000; warmup=3000; thin=1; 
## post-warmup draws per chain=3000, total post-warmup draws=12000.
## 
##               mean se_mean    sd      2.5%       50%     97.5% n_eff Rhat
## theta1       0.000   0.000 0.000     0.000     0.000     0.001  9016    1
## theta2       0.008   0.000 0.001     0.007     0.008     0.009  9589    1
## efficacy     0.946   0.000 0.019     0.902     0.948     0.976  9061    1
## lp__     -1026.764   0.014 1.028 -1029.472 -1026.443 -1025.772  5087    1
## 
## Samples were drawn using NUTS(diag_e) at Thu Jan  6 18:49:48 2022.
## For each parameter, n_eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor on split chains (at 
## convergence, Rhat=1).

The vaccine candidate efficacy has a 95% credible interval from 90.2% to 97.6%, which looks quite good. The chance of a confirmed COVID-19 case within the treatment group has 95% credible interval from 0.02% to 0.07%, compared to 0.67% to 0.92% for the control group. Thus the results are looking excellent.