NicholasEtAl2019 {bayesmeta}

Multiple sclerosis disability progression example data

Description

Proportions of patients with disability progression in the placebo groups of 28 studies.

Usage

data("NicholasEtAl2019")

Format

The data frame contains the following columns:

study	character	publication identifier (first author and publication year)
year	numeric	publication year
patients	numeric	number of placebo patients
prog.percent	numeric	percentage of patients with disability progression

Details

A systematic literature review investigated the characteristics of randomized placebo-controlled trials in multiple sclerosis published between 1988 and 2018 (Nicholas et al., 2019). A number of trends were observed in the trial characteristics over the investigated period; one of these was a decline in the proportion of placebo patients experiencing disability progression within 24 months during the course of a study. The data set contains the placebo groups' sizes along with the percentages of progressing patients within that group for 28 studies. The data were originally extracted from tables or Kaplan-Meier curves.

Source

R.S. Nicholas, E. Han, J. Raffel, J. Chataway, T. Friede. Over three decades study populations in progressive multiple sclerosis have become older and more disabled, but have lower on-trial progression rates: A systematic review and meta-analysis of 43 randomised placebo-controlled trials. Multiple Sclerosis Journal, 25(11):1462-1471, 2019. doi:10.1177/1352458518794063.

References

C. Roever, T. Friede. Using the bayesmeta R package for Bayesian random-effects meta-regression. Computer Methods and Programs in Biomedicine, 299:107303, 2023. doi:10.1016/j.cmpb.2022.107303.

Examples

# load data:
data("NicholasEtAl2019")

# show data:
head(NicholasEtAl2019)

## Not run: 
# compute effect sizes (logarithmic odds) from count data
# (note: effect of potential drop-outs is ignored here):
es <- escalc(measure="PLO",
             xi=patients*(prog.percent/100), ni=patients,
             slab=study, data=NicholasEtAl2019)

# illustrate estimates (log-odds):
forestplot(es, zero=NA, xlab="log(odds)", title="Nicholas et al. (2019) data")

# set up regressor matrix
# (note: "year" variable is re-scaled so that the intercept
# corresponds to the log-odds at year=2000):
X <- cbind("intercept2000" = 1, "year" = (es$year-2000))

# perform analysis:
bmr01 <- bmr(es, X=X)

# show results:
print(bmr01)
plot(bmr01)

# illustrate the data and time trend;
# first derive predictions from the model
# and specify corresponding "regressor matrix":
newx <- cbind(1, (1989:2019)-2000)

# compute credible intervals for the mean:
pred <- cbind("median"=bmr01$qpred(0.5, x=newx),
              bmr01$pred.interval(x=newx))

# compute prediction intervals:
map <- cbind("median"=bmr01$qpred(0.5, x=newx, mean=FALSE),
              bmr01$pred.interval(x=newx, mean=FALSE))

# draw empty plot:
plot(range(newx[,2]), range(map), type="n",
     xlab="publication year - 2000", ylab="log(odds)")

# show the 26 studies' estimates (and 95 percent CIs):
matlines(rbind(es$year, es$year)-2000,
         rbind(es$yi-qnorm(0.975)*sqrt(es$vi), es$yi+qnorm(0.975)*sqrt(es$vi)),
         col=1, lty=1)
points(es$year-2000, es$yi)

# show trend lines (and 95 percent intervals):
matlines(newx[,2], map, col="blue", lty=c(1,2,2))
matlines(newx[,2], pred, col="red", lty=c(1,2,2))
legend("topright", pch=15, col=c("red","blue"), c("mean","prediction"))

## End(Not run)