8 Bayesian Analysis of Simulated RCT with Two Endpoints

Treatments A,B
RCT for hypertension, n=1500
Covariate adjustment for baseline systolic bp
Outcomes
- incidence of death or stroke (DS) within 1y
  binary for illustration, logistic model
  B:A odds ratio 0.8
  1y SBP also predicts DS
- SBP at 1y, linear model, B:A treatment effect 3mmHg
SBP assumed to be normal with σ=7mmHg given baseline SBP
To estimate population parameters simulate a sample of size n=40,000

Code

# Simulate a single trial with sample size n
sim <- function(n) {
    trt  <- c(rep('A', n / 2), rep('B', n / 2))
    sbp0 <- rnorm(n, 140, 7)
    sbp  <- sbp0 - 5 - 3 * (trt == 'B') + rnorm(n, sd=7)
    logit <- -2.6 + log(0.8) * (trt == 'B') + 0.05 * (sbp0 - 140) +
             0.05 * (sbp - 130)
    ds   <- ifelse(runif(n) <= plogis(logit), 1, 0)
    data.frame(trt, sbp0, sbp, ds)
}

Code

set.seed(1)
d <- sim(n=40000)
require(rms)
ols(sbp ~ sbp0 + trt, data=d)

Linear Regression Model

ols(formula = sbp ~ sbp0 + trt, data = d)

	Model Likelihood Ratio Test	Discrimination Indexes
Obs 40000	LR χ² 28287.46	R² 0.507
σ 7.0270	d.f. 2	R²_adj 0.507
d.f. 39997	Pr(>χ²) 0.0000	g 8.038

Residuals

       Min         1Q     Median         3Q        Max 
-3.012e+01 -4.736e+00 -7.268e-04  4.737e+00  2.748e+01

	β	S.E.	t	Pr(>\|t\|)
Intercept	-4.2576	0.7026	-6.06	<0.0001
sbp0	0.9944	0.0050	198.58	<0.0001
trt=B	-2.9380	0.0703	-41.81	<0.0001

Code

lrm(ds ~ sbp0 + sbp + trt, data=d)

Logistic Regression Model

lrm(formula = ds ~ sbp0 + sbp + trt, data = d)

	Model Likelihood Ratio Test	Discrimination Indexes	Rank Discrim. Indexes
Obs 40000	LR χ² 2142.26	R² 0.113	C 0.717
0 36293	d.f. 3	R²_3,40000 0.052	D_xy 0.434
1 3707	Pr(>χ²) <0.0001	R²_3,10090.4 0.191	γ 0.434
max \|∂log L/∂β\| 4×10^-9		Brier 0.079	τ_a 0.073

	β	S.E.	Wald Z	Pr(>\|Z\|)
Intercept	-16.6218	0.3847	-43.21	<0.0001
sbp0	0.0517	0.0036	14.40	<0.0001
sbp	0.0522	0.0026	20.32	<0.0001
trt=B	-0.2594	0.0367	-7.07	<0.0001

Code

f <- lrm(ds ~ sbp0 + trt, data=d)
print(f)

Logistic Regression Model

lrm(formula = ds ~ sbp0 + trt, data = d)

	Model Likelihood Ratio Test	Discrimination Indexes	Rank Discrim. Indexes
Obs 40000	LR χ² 1719.66	R² 0.091	C 0.697
0 36293	d.f. 2	R²_2,40000 0.042	D_xy 0.394
1 3707	Pr(>χ²) <0.0001	R²_2,10090.4 0.157	γ 0.394
max \|∂log L/∂β\| 0.004		Brier 0.080	τ_a 0.066

	β	S.E.	Wald Z	Pr(>\|Z\|)
Intercept	-16.5559	0.3807	-43.49	<0.0001
sbp0	0.1019	0.0026	38.49	<0.0001
trt=B	-0.4045	0.0358	-11.31	<0.0001

Code

btrt <- coef(f)['trt=B']

Regression coefficient for treatment when don’t adjust for 1y SBP: -0.4045; B:A odds ratio: 0.6673
Trial could be run sequentially but we used fixed n=1500 with only one look
Traditional analysis:

Code

set.seed(7)
d <- sim(n=1500)
re <- round(cor(d$ds, d$sbp), 3)
dd <- datadist(d); options(datadist='dd')
f <- ols(sbp ~ sbp0 + trt, data=d)
f

Linear Regression Model

ols(formula = sbp ~ sbp0 + trt, data = d)

	Model Likelihood Ratio Test	Discrimination Indexes
Obs 1500	LR χ² 1087.47	R² 0.516
σ 7.0059	d.f. 2	R²_adj 0.515
d.f. 1497	Pr(>χ²) 0.0000	g 8.177

Residuals

    Min      1Q  Median      3Q     Max 
-24.580  -4.619   0.154   4.241  24.293

	β	S.E.	t	Pr(>\|t\|)
Intercept	-5.4630	3.6567	-1.49	0.1354
sbp0	1.0048	0.0260	38.62	<0.0001
trt=B	-3.1831	0.3620	-8.79	<0.0001

Code

summary(f)

Effects Response: `sbp`
	Low	High	Δ	Effect	S.E.	Lower 0.95	Upper 0.95
sbp0	135.2	144.5	9.296	9.341	0.2419	8.867	9.816
trt --- B:A	1.0	2.0		-3.183	0.3620	-3.893	-2.473

Code

f <- lrm(ds  ~ sbp0 + trt, data=d)
f

Logistic Regression Model

lrm(formula = ds ~ sbp0 + trt, data = d)

	Model Likelihood Ratio Test	Discrimination Indexes	Rank Discrim. Indexes
Obs 1500	LR χ² 53.82	R² 0.075	C 0.670
0 1357	d.f. 2	R²_2,1500 0.034	D_xy 0.339
1 143	Pr(>χ²) <0.0001	R²_2,388.1 0.125	γ 0.339
max \|∂log L/∂β\| 2×10^-5		Brier 0.083	τ_a 0.059

	β	S.E.	Wald Z	Pr(>\|Z\|)
Intercept	-15.1566	1.8989	-7.98	<0.0001
sbp0	0.0920	0.0132	6.96	<0.0001
trt=B	-0.2715	0.1807	-1.50	0.1330

Code

summary(f)

Effects Response: `ds`
	Low	High	Δ	Effect	S.E.	Lower 0.95	Upper 0.95
sbp0	135.2	144.5	9.296	0.8549	0.1229	0.6141	1.09600
Odds Ratio	135.2	144.5	9.296	2.3510		1.8480	2.99100
trt --- B:A	1.0	2.0		-0.2715	0.1807	-0.6256	0.08268
Odds Ratio	1.0	2.0		0.7623		0.5349	1.08600

Pearson’s r correlation between the SBP outcome and the death/stroke outcome: 0.22
If frequentist analysis is repeated 2500 times, correlation across the 2500 of the estimated treatment effects on DS and the estimated treatment effects on SBP is 0.142
Bayesian analysis below uses independent models for the two outcomes

Bayesian Analysis

Uses Stan and R rstan package
Regression coefficients have multivariate normal prior with means zero
SD of priors computed so that
- P(SBP reduction > 10mmHg) = 0.1
- P(OR < 0.5) = 0.05
Residual SD has flat prior on the positive line
Stan No-U-turn sampler in 4 chains with 5,000 post-warmup iterations
20,000 paired posterior draws, took 10m on 4-course machine

Code

require(rstan)
rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectCores())  # 4 CPUs used

model <- "
data {
    int       n;
    vector[n] x;
    real      y1[n];
    int       y2[n];
    vector[n] treat;
    vector[2] Zero;
    vector<lower=0>[2] sigma_b;
}

parameters {
    vector[2] alpha;
    vector[2] beta;
    vector[2] mu;
    real<lower=0> sigma_y;
    cholesky_factor_corr[2] L_b;
}

transformed parameters {
    vector[n]     theta1;
    vector[n]     theta2;
        
    theta1 = mu[1] + alpha[1]*x + beta[1]*treat;
    theta2 = mu[2] + alpha[2]*x + beta[2]*treat;
}

model {
    beta ~ multi_normal_cholesky(Zero, diag_pre_multiply(sigma_b, L_b)); 
    L_b ~ lkj_corr_cholesky(1);  // correlation matrix for reg. parameters, LKJ prior
        
    y1 ~ normal(theta1, sigma_y);
    y2 ~ bernoulli_logit(theta2);
}

generated quantities {
  matrix[2,2] Omega;
  matrix[2,2] Sigma;
  Omega = multiply_lower_tri_self_transpose(L_b);
  Sigma = quad_form_diag(Omega, sigma_b); 
}
"

s <- stan(model_code = model, iter=10000, seed=7,
          data=with(d, list(x=sbp0, treat=1*(trt == 'B'),
                            y1=sbp, y2=ds,
                            sigma_b=c(-10 / qnorm(0.1),
                                      log(0.5) / qnorm(0.05)),
                            Zero=c(0,0), n=nrow(d))))
s
betas <- extract(s, pars='beta')$beta

Inference for Stan model: cd388c9aa01c1c78a612ddca57e2c5c6.
4 chains, each with iter=10000; warmup=5000; thin=1; 
post-warmup draws per chain=5000, total post-warmup draws=20000.

             mean se_mean     sd     2.5%    97.5% n_eff   Rhat
alpha[1]   1.0047  0.0002 0.0259   0.9539   1.0557 11616 1.0006
alpha[2]   0.0923  0.0001 0.0133   0.0666   0.1182 11596 1.0006
beta[1]   -3.1780  0.0027 0.3607  -3.8797  -2.4695 18285 1.0001
beta[2]   -0.2129  0.0012 0.1596  -0.5325   0.1026 16387 1.0001
mu[1]     -5.4464  0.0339 3.6436 -12.6012   1.6968 11581 1.0006
mu[2]    -15.2366  0.0177 1.9063 -18.9511 -11.5603 11583 1.0006

Samples were drawn using NUTS(diag_e) at Mon Dec 19 13:51:42 2016.
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).

Posterior densities obtained using kernel density estimators

Code

spar(mfrow=c(1,2), bty='l')
b1 <- betas[, 1]
b2 <- betas[, 2]
plot(density(b1), type='l', xlab='B-A SBP Difference', main='')
plot(density(exp(b2)), type='l', xlab='B:A OR for Death or Stroke', main='')

Posterior densities for treatment effects on two outcomes

Posterior means, medians, and 0.95 credible intervals are computed simply by using ordinary samples estimates on the posterior draws

Code

ci1 <- quantile(b1, c(0.025, 0.975))
ci2 <- quantile(b2, c(0.025, 0.975))
data.frame(Mean=c(mean(b1), mean(b2)), Median=c(median(b1), median(b2)),
           Lower=c(ci1[1], ci2[1]), Upper=c(ci2[1], ci2[2]), row.names=c('b1', 'b2'))

        Mean     Median      Lower      Upper
b1 -3.178025 -3.1782677 -3.8796797 -0.5324795
b2 -0.212865 -0.2117236 -0.5324795  0.1025766

The posterior mean and median log odds ratio is less impressive than the frequentist maximum likelihood estimate of -0.271 because of the skeptical prior
Variety of posterior probabilities easily calculated
Non-inferiority is defined by SBP increase less than 1mmHg and DS increased by an odds ratio less than 1.05
Similarity with respect to effect on DS is an odds ratio between 0.85 and 1/0.85
Final calculation is mean number of targets achieved when the targets are any SBP reduction and any reduction of odds of DS

Code

cat('Prob(SBP reduced at least 2 mmHg) = ', rmean(b1 < -2), '\n',
    'Prob(B:A OR for DS < 1)           = ', rmean(b2 < 0),  '\n',
    'Prob(SBP reduced ≥ 2 and OR < 1)  = ', rmean(b1 < -2 & b2 < 0), '\n',
    'Prob(SBP reduced ≥ 2 or  OR < 1)  = ', rmean(b1 < -2 | b2 < 0), '\n',
    'Prob(Non-inferiority)             = ', rmean(b1 < 1 & b2 < log(1.05)),'\n',
    'Prob(DS similar)                  = ', rmean(exp(b2) > 0.85 &
                                                  exp(b2) < 1 / 0.85), '\n',
    'E(# targets achieved)             = ', rmean((b1 < 0) + (b2 < 0)), '\n',
    sep='')

Prob(SBP reduced at least 2 mmHg) = 0.999
Prob(B:A OR for DS < 1)           = 0.908
Prob(SBP reduced ≥ 2 and OR < 1)  = 0.908
Prob(SBP reduced ≥ 2 or  OR < 1)  = 1.000
Prob(Non-inferiority)             = 0.948
Prob(DS similar)                  = 0.363
E(# targets achieved)             = 1.908

Correlation between the posterior draws is 0.024
6 probabilities above are forward probabilities directly addressing clinical questions
Frequentist solutions for these questions are highly indirect or unavailable