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Looking at the list of adverse events is really interesting, and leads me to agree with the conclusion that this study lies somewhere between "inept bumbling" a

Looking at the list of adverse events is really interesting, and leads me to agree with the conclusion that this study lies somewhere between "inept bumbling" and "deliberate lying with statistics." Forget the red herring of comparing across the Pfizer and Moderna studies (which clearly have very different sample properties); the within-study comparisons are also a little suspect. Most of the adverse effects have frequencies of 1-3, which is literally the lower limit of what kind of data we can use to estimate anything about anything (ask an actuary about modeling "rare events" or "extreme events"). Moreover, what is not reported is the number of affected individuals; there is no indication about whether or not the two poor souls who ended up with pancreatitis in the Moderna trial were also the two people who ended up with diarrhea in the same trial. I am very leery of looking at the sum of these effects and concluding that, in aggregate, the Moderna vaccine is more dangerous than Covid-19 infection (let alone more dangerous than the placebo) when it comes to these "rare" adverse events -- because it doesn't make a lot of sense to aggregate these numbers. What stands out among all this rare-events noise is the risk of cardiac injury and coagulation disorder. There might actually be some meat on that bone. In the Pfizer trial, we have 8.5 instances of coagulation disorder per 10k versus 5.3 instances per 10k in the placebo. That's not nothing, and might even be "statistically significant" depending on your decision criteria. But again, without a comparison to Covid itself on this particular metric (the "risk reduction" numbers are buried in the text and not clearly enumerated), there's not much you can conclude here.

deleted 4y ago
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nerdponx 4y ago
I actually went and plugged in the numbers for coagulation disorders into a frequentist likelihood ratio test for a difference in proportions, and got p = 0.23. Feel free to check my work (in R): coag_vax <- 16 p_vax <- 8.5 / 10000 num_vax <- round(coag_vax / p_vax) cat("#(vaccine) = ", num_vax, "\n", sep = "") cat("#(coagulation disorder | vaccine) = ", coag_vax, "\n", sep = "") cat("P(coagulation disorder | vaccine) = ", p_vax, "\n", sep = "") cat("\n") coag_plac <- 10 p_plac <- 5.3 / 10000 num_plac <- round(coag_plac / p_plac) cat("#(placebo) = ", num_plac, "\n", sep = "") cat("#(coagulation disorder | placebo) = ", coag_plac, "\n", sep = "") cat("P(coagulation disorder | placebo) = ", p_plac, "\n", sep = "") cat("\n") coag_same <- coag_vax + coag_plac num_same <- num_vax + num_plac p_same <- coag_same / num_same cat("#(null) = ", num_same, "\n", sep = "") cat("#(coagulation disorder | null) = ", coag_same, "\n", sep = "") cat("P(coagulation disorder | null) = ", p_same, "\n", sep = "") cat("\n") ## Frequentist likelihood ratio test that the proportions are identical # https://stats.stackexchange.com/a/373448/36229 # http://people.musc.edu/~bandyopd/bmtry711.11/lecture_02.pdf l_same <- dbinom(coag_vax, num_vax, p_same) * dbinom(coag_plac, num_plac, p_same) l_diff <- dbinom(coag_vax, num_vax, p_vax) * dbinom(coag_plac, num_plac, p_plac) t <- -2.0 * log(l_same / l_diff) p <- 1 - pchisq(t, 1) cat("LR test statistic = ", t, "\n", sep = "") cat("LR test p-value = ", p, "\n", sep = "") Output: #(vaccine) = 18824 #(coagulation disorder | vaccine) = 16 P(coagulation disorder | vaccine) = 0.00085 #(placebo) = 18868 #(coagulation disorder | placebo) = 10 P(coagulation disorder | placebo) = 0.00053 #(null) = 37692 #(coagulation disorder | null) = 26 P(coagulation disorder | null) = 0.0006898015 LR test statistic = 1.412184 LR test p-value = 0.2346942 And for a 95% CI around the difference in proportions, I got [0, 8.5]: # https://pubmed.ncbi.nlm.nih.gov/25163425/ # var_vax <- num_vax * p_vax * (1 - p_vax) # var_plac <- num_plac * p_plac * (1 - p_plac) var_pooled <- (p_vax * (1 - p_vax) / num_vax) + (p_plac * (1 - p_plac) / num_plac) std_pooled <- sqrt(var_pooled) ci_lo <- (p_vax - p_plac) - qnorm(0.975) * std_pooled ci_hi <- (p_vax - p_plac) + qnorm(0.975) * std_pooled cat("95% CI (Gaussian approximation) = [ ", max(ci_lo, 0.0) * 10000, " in 10k , ", min(ci_hi, 1.0) * 10000, " in 10k ]\n", sep = "") Output: 95% CI (Gaussian approximation) = [ 0 in 10k , 8.502481 in 10k ] I also ran it through a completely-un-tuned Bayesian model and got a similarly inconclusive result: library(brms) coag_data <- data.frame( category = c("vaccine", "placebo"), n_sample = c(num_vax, num_plac), n_coag = c(coag_vax, coag_plac)) fmla <- n_coag | trials(n_sample) ~ category prior <- get_prior(fmla, coag_data, family = binomial) cat("Prior:\n") print(prior) model <- brm( fmla, data = coag_data, family = binomial(link = "logit"), prior = prior, iter = 1000) cat("Model:\n") print(model) Output (truncated): Population-Level Effects: Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS Intercept -7.58 0.32 -8.23 -6.98 1.00 1000 1278 categoryvaccine 0.49 0.41 -0.27 1.32 1.00 1319 1239 Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS and Tail_ESS are effective sample size measures, and Rhat is the potential scale reduction factor on split chains (at convergence, Rhat = 1). Conclusion: It's suggestive that the vaccine is more dangerous than the placebo but not at all a strong confirmation. I did skim back over the paper and it looks like they categorized "serious" in such a way that maybe hospitalization is not a terrible baseline for comparison. But it's still pretty questionable that the incidence of "serious" coagulation disorders in the placebo group is higher than the incidence of hospitalization in the placebo group, and it makes me think that they're making an invalid comparison there. So this is an inconclusive result, presented in what is at best a hand-wavey framework.

deleted 4y ago

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nerdponx 4y ago

I actually went and plugged in the numbers for coagulation disorders into a frequentist likelihood ratio test for a difference in proportions, and got p = 0.23. Feel free to check my work (in R): coag_vax <- 16 p_vax <- 8.5 / 10000 num_vax <- round(coag_vax / p_vax) cat("#(vaccine) = ", num_vax, "\n", sep = "") cat("#(coagulation disorder | vaccine) = ", coag_vax, "\n", sep = "") cat("P(coagulation disorder | vaccine) = ", p_vax, "\n", sep = "") cat("\n") coag_plac <- 10 p_plac <- 5.3 / 10000 num_plac <- round(coag_plac / p_plac) cat("#(placebo) = ", num_plac, "\n", sep = "") cat("#(coagulation disorder | placebo) = ", coag_plac, "\n", sep = "") cat("P(coagulation disorder | placebo) = ", p_plac, "\n", sep = "") cat("\n") coag_same <- coag_vax + coag_plac num_same <- num_vax + num_plac p_same <- coag_same / num_same cat("#(null) = ", num_same, "\n", sep = "") cat("#(coagulation disorder | null) = ", coag_same, "\n", sep = "") cat("P(coagulation disorder | null) = ", p_same, "\n", sep = "") cat("\n") ## Frequentist likelihood ratio test that the proportions are identical # https://stats.stackexchange.com/a/373448/36229 # http://people.musc.edu/~bandyopd/bmtry711.11/lecture_02.pdf l_same <- dbinom(coag_vax, num_vax, p_same) * dbinom(coag_plac, num_plac, p_same) l_diff <- dbinom(coag_vax, num_vax, p_vax) * dbinom(coag_plac, num_plac, p_plac) t <- -2.0 * log(l_same / l_diff) p <- 1 - pchisq(t, 1) cat("LR test statistic = ", t, "\n", sep = "") cat("LR test p-value = ", p, "\n", sep = "") Output: #(vaccine) = 18824 #(coagulation disorder | vaccine) = 16 P(coagulation disorder | vaccine) = 0.00085 #(placebo) = 18868 #(coagulation disorder | placebo) = 10 P(coagulation disorder | placebo) = 0.00053 #(null) = 37692 #(coagulation disorder | null) = 26 P(coagulation disorder | null) = 0.0006898015 LR test statistic = 1.412184 LR test p-value = 0.2346942 And for a 95% CI around the difference in proportions, I got [0, 8.5]: # https://pubmed.ncbi.nlm.nih.gov/25163425/ # var_vax <- num_vax * p_vax * (1 - p_vax) # var_plac <- num_plac * p_plac * (1 - p_plac) var_pooled <- (p_vax * (1 - p_vax) / num_vax) + (p_plac * (1 - p_plac) / num_plac) std_pooled <- sqrt(var_pooled) ci_lo <- (p_vax - p_plac) - qnorm(0.975) * std_pooled ci_hi <- (p_vax - p_plac) + qnorm(0.975) * std_pooled cat("95% CI (Gaussian approximation) = [ ", max(ci_lo, 0.0) * 10000, " in 10k , ", min(ci_hi, 1.0) * 10000, " in 10k ]\n", sep = "") Output: 95% CI (Gaussian approximation) = [ 0 in 10k , 8.502481 in 10k ] I also ran it through a completely-un-tuned Bayesian model and got a similarly inconclusive result: library(brms) coag_data <- data.frame( category = c("vaccine", "placebo"), n_sample = c(num_vax, num_plac), n_coag = c(coag_vax, coag_plac)) fmla <- n_coag | trials(n_sample) ~ category prior <- get_prior(fmla, coag_data, family = binomial) cat("Prior:\n") print(prior) model <- brm( fmla, data = coag_data, family = binomial(link = "logit"), prior = prior, iter = 1000) cat("Model:\n") print(model) Output (truncated): Population-Level Effects: Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS Intercept -7.58 0.32 -8.23 -6.98 1.00 1000 1278 categoryvaccine 0.49 0.41 -0.27 1.32 1.00 1319 1239 Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS and Tail_ESS are effective sample size measures, and Rhat is the potential scale reduction factor on split chains (at convergence, Rhat = 1). Conclusion: It's suggestive that the vaccine is more dangerous than the placebo but not at all a strong confirmation. I did skim back over the paper and it looks like they categorized "serious" in such a way that maybe hospitalization is not a terrible baseline for comparison. But it's still pretty questionable that the incidence of "serious" coagulation disorders in the placebo group is higher than the incidence of hospitalization in the placebo group, and it makes me think that they're making an invalid comparison there. So this is an inconclusive result, presented in what is at best a hand-wavey framework.