test/demos/demo_c212_1a.r

In rcarragh/c212: Methods for Detecting Safety Signals in Clinical Trials Using Body-Systems (System Organ Classes)

library(c212)

set.seed(1)

data(c212.trial.data)

print(head(c212.trial.data))

# 1. Fit the model:

mod.1a <- c212.1a(c212.trial.data)

# 2. Assess convergence
conv <- c212.convergence.diag(mod.1a)

# Printing a convergence summary will indicate if there are any obvious issues
# Any reported statistics that are greater than about 1.1 may indicate an issue.
c212.print.convergence.summary(conv)

print(max(conv$theta.conv.diag$stat))
# [1] 1.000765

# 3. If required calculate summary statistics (mean/median/hpi)
summ <- c212.summary.stats(mod.1a)
c212.print.summary.stats(summ)

# These may be accessed directly for model parameters:
print(head(summ$theta))
print(summ$theta[1,]$mean)
# [1] 0.3246897
print(summ$theta[1,]$median)
# [1] 0.3230751
hpi <- c(summ$theta[1,]$hpi_lower, summ$theta[1,]$hpi_upper)
print(hpi)
# [1] -0.2249620  0.8854614

# 4. Assuming the model have converged assess which AEs may be associated with treatment.
# The model paramter theta is used for this purpose.
theta.post.prob <- c212.ptheta(mod.1a)

# A large (posterior) probability that theta is > 0 is an indication that an adverse event is associated with treamtment.

print(theta.post.prob[ theta.post.prob$ptheta > 0.95,])

#           B         AE    ptheta
#2  Bdy-sys_2   Adv-Ev_2 0.9976889
#3  Bdy-sys_2   Adv-Ev_3 0.9951889
#6  Bdy-sys_3   Adv-Ev_6 1.0000000
#7  Bdy-sys_3   Adv-Ev_7 1.0000000
#8  Bdy-sys_3   Adv-Ev_8 1.0000000
#9  Bdy-sys_3   Adv-Ev_9 0.9996222
#10 Bdy-sys_3 Adv-Ev_910 1.0000000
#11 Bdy-sys_3 Adv-Ev_911 1.0000000
#12 Bdy-sys_3 Adv-Ev_912 0.9998667