MBNMAtime (version 0.2.4)

MBNMAtime-package: MBNMAtime for Model-Based Network Meta-Analysis of longitudinal (time-course) data

Description

MBNMAtime provides a collection of useful commands that allow users to run time-course Model-Based Network Meta-Analysis (MBNMA).

Introduction

MBNMAtime allows meta-analysis of studies with multiple follow-up measurements that can account for time-course for a single or multiple treatment comparisons.

Including all available follow-up measurements within a study makes use of all the available evidence in a way that maintains connectivity between treatments, and it does so in a way that explains time-course, thus explaining heterogeneity and inconsistency that may be present in a standard Network Meta-Analysis (NMA). All models and analyses are implemented in a Bayesian framework, following an extension of the standard NMA methodology presented by lu2004MBNMAtime and are run in JAGS jagsMBNMAtime. Correlation between time-points can be accounted for in the modelling framework. For full details of time-course MBNMA methodology see pedder2019;textualMBNMAtime.

Workflow

Functions within MBNMAtime follow a clear pattern of use:

1. Load your data into the correct format using mb.network

2. Specify a suitable time-course function and analyse your data using mb.run

3. Test for consistency using mb.nodesplit or by fitting Unrelated Mean Effects models

4. Examine model results using forest plots and treatment rankings

5. Use your model to predict responses or estimate treatment effects at specific time-points using predict.mbnma

At each of these stages there are a number of informative plots that can be generated to help make sense of your data and the models that you are fitting.

Author

Maintainer: Hugo Pedder (ORCID)

References

See Also

Useful links:

• https://hugaped.github.io/MBNMAtime/

Examples

# \donttest{
# Generate an "mb.network" object that stores data in the correct format
network <- mb.network(osteopain)

# Generate a network plot
plot(network, label.distance=3)

# Analyse data using mb.run()
result <- mb.run(network, fun=tloglin())

# Time-course parameters can be explicitly specified
# Correlation between time-points can be accounted for
result <- mb.run(network,
  fun=temax(pool.emax="rel", method.emax="common",
    pool.et50="rel", method.et50="common"),
  rho="dunif(0,1)")

# Explore model fit statistics - plot residual deviances
devplot(result, n.iter=500)

# Generate a forest plot for model results
plot(result)

decision.treats <- c("Pl_0", "Ce_100", "Lu_400", "Ro_125",
  "Na_1000", "Na_1500", "Et_10")

# Predict responses for selected treatments
pred <- predict(result, time=c(0:10), E0=8,
  treats=decision.treats,
  ref.resp=subset(osteopain, treatment=="Pl_0"))

# Plot predicted response
plot(pred, disp.obs=TRUE)

# Rank by Area Under the time-course Curve
ranks <- rank(result, param="auc", lower_better=TRUE, n.iter=500,
  treats=decision.treats)

plot(ranks) # Plot histogram of rankings
cumrank(ranks) # Plot cumulative rankograms
# }