BaetenEtAl2013 {bayesmeta} | R Documentation |

## Ankylosing spondylitis example data

### Description

Numbers of cases (patients) and events (responders) in the placebo control groups of eight studies.

### Usage

`data("BaetenEtAl2013")`

### Format

The data frame contains the following columns:

study | `character` | study label |

year | `numeric` | publication year |

events | `numeric` | number of responders |

total | `numeric` | total number of patients |

### Details

A study was conducted in order to investigate a novel treatment
in ankylosing spondylitis (Baeten et al., 2013). The primary endpoint
related to *treatment response*.
In order to formulate an informative prior distribution for the
response rate to be expected in the control group of the new study, a
systematic review of previous studies was consulted (McLeod et al.,
2007), and, after a meta-analysis of the estimated response
probabilities, the predictive distribution for the new study's
response probability was derived. The predictive distribution here
constitutes the *meta-analytic-predictive (MAP) prior*
distribution (Schmidli et al., 2014). The data set contains the
relevant data from the eight “historical” studies' placebo
groups.

Note that the original analysis (Baeten et al., 2013) involved a binomial model, and the resulting posterior predictive distribution was eventually approximated by a mixture of beta distributions.

### Source

D. Baeten et al.
Anti-interleukin-17A monoclonal antibody secukinumab in treatment of
ankylosing spondylitis: a randomised, double-blind, placebo-controlled
trial.
*The Lancet*, **382**(9906):1705-1713, 2013.
doi:10.1016/S0140-6736(13)61134-4.

### References

C. McLeod et al.
Adalimumab, etanercept, and infliximab for the treatment of ankylosing
spondylitis: a systematic review and economic evaluation.
*Health Technology Assessment*, **11**(28), 2007.
doi:10.3310/hta11280.

H. Schmidli, S. Gsteiger, S. Roychoudhury, A. O'Hagan,
D. Spiegelhalter, B. Neuenschwander.
Robust meta-analytic-predictive priors in clinical trials with
historical control information.
*Biometrics*, **70**(4):1023-1032, 2014.
doi:10.1111/biom.12242.

### See Also

### Examples

```
# load data:
data("BaetenEtAl2013")
# show data:
BaetenEtAl2013
## Not run:
# compute effect sizes (logarithmic odds) from the count data:
as <- escalc(xi=events, ni=total, slab=study,
measure="PLO", data=BaetenEtAl2013)
# compute the unit information standard deviation (UISD):
uisd(as)
# perform meta-analysis
# (using uniform priors for effect and heterogeneity):
bm <- bayesmeta(as)
# show results (log-odds):
forestplot(bm, xlab="log-odds", zero=NA)
# show results (odds):
forestplot(bm, exponentiate=TRUE, xlab="odds", zero=NA)
# show posterior predictive distribution --
# in terms of log-odds:
bm$summary[,"theta"]
logodds <- bm$summary[c(2,5,6), "theta"]
logodds
# in terms of odds:
exp(logodds)
# in terms of probabilities:
(exp(logodds) / (exp(logodds) + 1))
# illustrate MAP prior density:
x <- seq(-3, 1, by=0.01)
plot(x, bm$dposterior(theta=x, predict=TRUE), type="l",
xlab="log-odds (response)", ylab="posterior predictive density")
abline(h=0, v=0, col="grey")
## End(Not run)
```

*bayesmeta*version 3.4 Index]