MBNMAdose 0.5.0

Additions/changes

• SUCRA values generated by cumrank() now normalised to be between 0 and 1.
• Allowed spline functions with up to 6 dose-response parameters
• pd is now specified as pD and should be logical as either TRUE (estimates the effective number of parameters via the Kullback-Leibler divergence) or FALSE (using the pV approximation)

Bug fixes

• This version incorporates a fix in R2jags that was ignoring the warmup stage and effectively saving all the simulations performed for Monte-Carlo estimation.

MBNMAdose 0.4.3

Additions/changes

• netmeta added to list of suggested packages

MBNMAdose 0.4.2

Additions/changes

• Reference SDs can now be used when modelling using SMDs to avoid using study-specific SDs, which can be problematic.
• Network Meta-Regression: Effect modifiers can now be incorporated using regress.vars argument in mbnma.run(). Various sharing assumptions for effects can be specified in regress.effect.
• Predictions can be estimated for class effect models
• Fractional polynomial powers in dfpoly() can only take numeric values from set defined in Jansen 2015.
• Added 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.
• Plots of predictions look prettier
• Dose-response parameters that were previously modelled on an exponential scale (ed50, hill, onset) are now on the natural scale and are assigned truncated normal default priors
• Separate prior distributions can be specified for different indices of a parameter - allows for agent-specific prior distributions on dose-response parameters.

Bug fixes

• Fixed issue with duplication of studyIDs in getjagsdata()
• Fixed bug with studyID labels in fitplot() and devplot()
• Fixed issue and documentation with splines to highlight that knot limits should not be specified

MBNMAdose 0.4.1

Additions/changes

• Added get.relative() to allow estimation of relative effects between any doses of different agents.
• Adding ranking for "relative.array" objects generated by get.relative().
• Agent-specific dose-response functions run more smoothly. Can now incorporate splines with different knots
• Number of responders should now be included in dataset as n rather than N so that datasets can be consistent with those used in MBNMAtime
• Simplified names of datasets included in the package
• Prediction intervals can be calculated in addition to credible intervals (the default) for predict.mbnma() and get.relative()
• Added devdev() for comparing deviance contributions between models

Bug fixes

• Fixed issue with agent-specific dose-response functions that had different spline knot locations
• Fixed bug that cut out the wrong sections of JAGS output based on incorrect indexing of agent-specific dose-response functions

MBNMAdose 0.4.0

Additions/changes

• Dose-response functions provided to mbnma.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.
• Added log linear dose-response function (dloglin())
• Added spline function (dspline()) (piecewise linear splines, B-splines, restricted cubic splines, natural splines)
• Added fractional polynomial function (dfpoly())
• Agent-specific dose-response functions work with all other functions (e.g. ranking, nodesplit, prediction)
• Added link="smd" to allow for analysis using Standardised Mean Differences
• Uses calcom() to guess outcome measure scale for more careful specification of default priors for SD
• Study IDs added to "mbnma.network" object

Bug fixes

• Error with running random effects models in mbnma.nodesplit() fixed

MBNMAdose 0.3.1

Bug fixes

• Spline functions also return doses as well as spline basis matrices in jagsdata
• Added informative error for if params in plot.mbnma.rank() is not a subset of x

Additions/changes

• Updated and simplified package structure plot in README file
• overlay.split() uses full distribution of E0 rather than summary statistics
• mbnma.predict object now contains values assigned/estimated for E0 to be used in overlay.split()
• In plot.nodesplit(), plot.type="forest" plots a single forest plot with results for each node-split comparison, rather than presenting results in panels.

MBNMAdose 0.3.0

Bug fixes

• Ensured summary.mbnma.network() returns valid minimum doses per agent
• Ensured models run in parallel when parallel=TRUE and added a warning when pd is set to "pd.kl" or "popt" for these models.
• Ensured results are printed properly for each parameter when using summary() for multiple dose-response function models

Additions/changes

• Added restricted cubic spline dose-response function (fun="rcs") in mbnma.run()
• Unrelated mean effects (UME) model now added to 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 parameter
• autojags 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
• Data from getjagsdata() contains studyID and has been added to mbnma objects
• Added studyID column to output from devplot() 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.
• All nodesplit models now return object of class("nodesplit")
• mbnma.nodesplit() includes potential splits via dose-response curve and direct and indirect evidence contributions are calculated simultaneously in the same model.
• Corrected calculation for Bayesian p-value in mbnma.nodesplit() and nma.nodesplit()
• Added legend options to plot.mbnma.network()
• Added psoriasis and ssri datasets to package
• Used crayon package to neaten printed console outputs

MBNMAdose 0.2.7

Bug fixes

• Ensured stringsAsFactors = FALSE does not affect package in preparation for R 4.0.0.
• Edited tests to ensure that any checks for matrix objects account for matrix objects now having matrix and array classes
• Allowed number of responders for binomial data to be greater than or equal to zero (rather than greater than zero)

MBNMAdose 0.2.6

Additions/Changes

• Ensured non-parametric functions are properly monotonic by setting default initial values. Each agent now includes an index dose level of 1, which corresponds to the reference treatment effect (placebo)

MBNMAdose 0.2.5

Additions/Changes

• Added Lujin Li to list of authors as a reviewer thanks to his considerable help in identifying issues in the previous version.
• Each agent can be assigned a different dose-response function (by assigning a vector of functions to fun 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())

Bug fixes

• Changed if {class(x)=="matrix"} statements to if {is.matrix(x)} to address R development changes

Major

• Exponential function models were not working previously but the dose-response function has been rewritten so that it runs the model correctly.
• DIC reported correctly in output when using plugin (pd="plugin"), or Kullback-Leibler divergence (pd="pd.kl")
• Using the argument parallel=TRUE in mbnma.run() (or wrapper functions) now properly runs JAGS in parallel on multiple cores.

Minor

• Downstream mbnma-related objects now store mbnma.network in their output rather than just treatment and agent names.

MBNMAdose 0.2.4

Documentation changes

• Update to README to ensure package workflow image works correctly on CRAN.
• Added JAGS as a System Requirement (JAGS >= 4.3.0) to the DESCRIPTION

Bug fixes

Major

• Fixed incorrect ordering of treatment codes in mbnma.network, which also led to problems in subsequent commands/plots

Minor

• Fixed minor with numeric vs character coding of treatments in arguments for ranking functions
• Fixed issue with nma.nodesplit() that prevented the model running if disconnected treatments were included in the analysis (drop.discon=FALSE)

MBNMAdose 0.2.3

Documentation changes

• Made corrections to some arguments specified in documentation
• Fixed incorrect vignette reference
• Allowed more examples in the vignette to run so that plots can be created which better illustrates how MBNMA.run() functions are used.

MBNMAdose 0.2.2

First release of package

Welcome to MBNMAdose. Ready for release into the world. I hope it can be of service to you! For time-course MBNMA, also check out the sister package, MBNMAtime.