NEWS.md

In MBNMAtime: Run Time-Course Model-Based Network Meta-Analysis (MBNMA) Models

MBNMAtime 0.2.4

Additions/changes

• Reference SDs can now be used when modelling using SMDs to avoid using study-specific SDs which can lead to imprecision and heterogeneity.
• rank.mbnma() can now only rank a single parameter (e.g. param argument must be length 1). This facilitates differentiation between treatment and class parameters.

Bug fixes

• Error in nma.run when using link="smd" - a log link function was previously used but has now been fixed
• Class models did not work properly with character class labels - now fixed

MBNMAtime 0.2.3

Additions/changes

• Truncated priors are used as the default priors for time-course parameters that can only take positive values (e.g. et50) for all functions
• texp() has been removed - titp() is a more stable parameterisation of this function

Bug fixes

Major

• Error fixed preventing natural splines from functioning, causing removal from CRAN

Minor

• Return values added to documentation for functions in which they were missing

MBNMAtime 0.2.2

Additions/changes

• Can now specify numeric values for time-course parameters in the method argument. Can be useful for discrete values that cannot be estimated (e.g. fractional polynomial powers, Hill parameter).
• Fractional polynomial powers in tfpoly() can only take numeric values from set defined in Jansen 2015.
• Integrated Two-Component Prediction (ITP) function (titp()) added
• get.relative() can be used to combine two MBNMA models to allow different time-course functions to be fitted to a different set of treatments (see examples in the vignette)
• New priors that restrict posterior to positive values where necessary can be easily incorporated.
• binplot() can be used to plot the results of NMAs conducted at multiple time bins. This can be particularly useful to explore which time-course functions might be appropriate, and to check the validity of MBNMA predictions.
• mb.nodesplit() can be performed at specific time-points, in addition to by time-course parameter
• corparam set to FALSE as default

Bug fixes

Minor

• Error with overlay.nma argument in plot.mb.predict() fixed

MBNMAtime 0.2.1

Additions/changes

• get.relative() function can be used to calculate relative effects/mean differences between treatments/classes
• cumrank() added for cumulative ranking plots. Also calculates SUCRA values for each treatment and time-course parameter at specified follow-up times (even those at which treatments have not been compared within any study)
• Studies reporting change from baseline or absolute means can now be specified in mb.network(), or will be automatically inferred from the data (studies with no time=0 are assumed to report change from baseline)
• Model intercept (response at time=0) is now conditional on change from baseline for each study
• texp() now implements 2-parameter exponential function (though the simpler 1-parameter model remains the default)

Bug fixes

Major

• Error with predict() not properly incorporating absolute time-course parameters fixed

Minor

• Error with model.file input length fixed for mb.run()

MBNMAtime 0.2.0

Additions/changes

• Added variance adjustment (covar="varadj") for correlation between time-points - this is now the default in mb.run()
• Added log linear time-course function (tloglin())
• Added spline functions (piecewise linear splines, B-splines, restricted cubic splines, natural splines)
• Added overlay.nma option to predict() to allow plotting of "lumped" NMA results over MBNMA predictions
• Modelling can now incorporate Standardised Mean Differences (link="smd") or Ratios of Means (link="log") to allow modelling of studies with different scales
• lower_better argument used instead of decreasing for rankings
• Time-course functions given to mb.run() are now given as class("timefun") and time-course parameters are specified within these functions
• Predictions from predict() can now be ranked
• Forest plots now also plot posterior densities using ggdist::stat_halfeye()
• Neater outputs when using print() or summary()
• Wishart prior used to model correlations between within-study baseline effects on different time-course parameters, in addition to relative effects on different time-course parameters.

Bug fixes

Major

• Corrected calculation for Bayesian p-value in mb.nodesplit()

MBNMAtime 0.1.3

Additions/changes

• Added citation file
• plot.mb.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())
• Objects returned from plot.mb.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.
• mb.network objects are now stored within lists of most other mb class objects for easy reference of data format

Bug fixes

Major

• Exponential function models were not working previously but the dose-response function has been rewritten so that it runs the model correctly.
• Ensured comparisons are cycled through correctly in mb.nodesplit
• Ensured timeplot raw responses can be plotted by either arm (plotby="arm") or relative (plotby="rel") effects.

MBNMAtime 0.1.2

First release of package

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

MBNMAtime documentation built on Oct. 14, 2023, 5:08 p.m.