cumrank() now normalised to be between 0 and 1.regress.vars argument in mbnma.run(). Various sharing assumptions for effects can be specified in regress.effect.dfpoly() can only take numeric values from set defined in Jansen 2015.calc.edx() to allow easy estimation of different ED values (e.g. ED90 = the dose at which 90% of the maximum response (Emax) is reached)get.relative() now allows simultaneous comparison of two models in a single league table - can be used to compare MBNMA models with different dose-response functions, or MBNMA and NMA models, or NMA models that assume consistency versus those that use Unrelated Mean Effects.ed50, hill, onset) are now on the natural scale and are assigned truncated normal default priorsgetjagsdata()
fitplot() and devplot()
get.relative() to allow estimation of relative effects between any doses of different agents."relative.array" objects generated by get.relative().n rather than N so that datasets can be consistent with those used in MBNMAtime
predict.mbnma() and get.relative()
devdev() for comparing deviance contributions between modelsmbnma.run() are now given as class("dosefun") and dose-response parameters are specified within these functions. NOTE: Previous syntax of specifying a function name as a character (e.g. fun="linear") along with beta parameters (e.g. mbnma.run(beta.1="rel")) will be removed in subsequent versions, along with wrapper functions.dloglin())dspline()) (piecewise linear splines, B-splines, restricted cubic splines, natural splines)dfpoly())link="smd" to allow for analysis using Standardised Mean Differencescalcom() to guess outcome measure scale for more careful specification of default priors for SD"mbnma.network" objectmbnma.nodesplit() fixedparams in plot.mbnma.rank() is not a subset of x
overlay.split() uses full distribution of E0 rather than summary statisticsmbnma.predict object now contains values assigned/estimated for E0 to be used in overlay.split()
plot.nodesplit(), plot.type="forest" plots a single forest plot with results for each node-split comparison, rather than presenting results in panels.summary.mbnma.network() returns valid minimum doses per agentparallel=TRUE and added a warning when pd is set to "pd.kl" or "popt" for these models.summary() for multiple dose-response function modelsfun="rcs") in mbnma.run()
mbnma.run() to allow relaxing of the consistency assumption. This can be used to test its validity.cumrank() added for cumulative ranking plots. Also calculates SUCRA values for each agent and dose-response parameterautojags options added for mbnma.run() to allow users to run models until they converge (convergence defined by Rhat)rank.mbnma() also calculates cumulative ranking probabilities and stores them in cum.matrix
getjagsdata() contains studyID and has been added to mbnma objectsdevplot() and fitplot()
plot.nodesplit() scales y-axis if density is >50 times larger in panel with highest density than in panel with lowest density. This improves legibility of the graph.class("nodesplit")
mbnma.nodesplit() includes potential splits via dose-response curve and direct and indirect evidence contributions are calculated simultaneously in the same model.mbnma.nodesplit() and nma.nodesplit()
plot.mbnma.network()
psoriasis and ssri datasets to packagecrayon package to neaten printed console outputsfun in mbnma.run()) so that multiple functions can be modelled simultaneously. Some downstream package functions still may not yet work with these models though.mbnma.network objects returned from plot.mbnma.network now have specific igraph attributes assigned to them, which can be easily changed by the user.user.fun now takes a formula as an argument (for example ~ (beta.1 * dose) + (beta.2 * dose^2)) rather than a string.plot.mbnma.network() now uses a layout argument that takes an igraph layout function instead of layout_in_circle (which was a logical argument). This allows any igraph layout to be plotted rather than just a circle (e.g. igraph::as_star())if {class(x)=="matrix"} statements to if {is.matrix(x)} to address R development changespd="plugin"), or Kullback-Leibler divergence (pd="pd.kl")parallel=TRUE in mbnma.run() (or wrapper functions) now properly runs JAGS in parallel on multiple cores.nma.nodesplit() that prevented the model running if disconnected treatments were included in the analysis (drop.discon=FALSE)