# MBNMAtime for Model-Based Network Meta-Analysis of longitudinal (time-course) data MBNMAtime provides a collection of useful commands that allow users to run time-course Model-Based Network Meta-Analysis (MBNMA). package ## 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 if(!exists(".Rdpack.currefs")) .Rdpack.currefs \<-new.env();Rdpack::insert_citeOnly(keys="lu2004",package="MBNMAtime",cached_env=.Rdpack.currefs) and are run in JAGS if(!exists(".Rdpack.currefs")) .Rdpack.currefs \<-new.env();Rdpack::insert_citeOnly(keys="jags",package="MBNMAtime",cached_env=.Rdpack.currefs) . Correlation between time-points can be accounted for in the modelling framework. For full details of time-course MBNMA methodology see if(!exists(".Rdpack.currefs")) .Rdpack.currefs \<-new.env();Rdpack::insert_citeOnly(keys="pedder2019;textual",package="MBNMAtime",cached_env=.Rdpack.currefs) . ## 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. ## Examples ```r # 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 ``` ## References if(!exists(".Rdpack.currefs")) .Rdpack.currefs \<-new.env();Rdpack::insert_all_ref(.Rdpack.currefs) ## See Also Useful links: * [https://hugaped.github.io/MBNMAtime/](https://hugaped.github.io/MBNMAtime/) ## Author(s) Maintainer : Hugo Pedder hugopedder@gmail.com ([ORCID](https://orcid.org/0000-0002-7813-3749))

MBNMAtime-package