Changelog
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.
MBNMAtime 0.2.3
CRAN release: 2023-03-17
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
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 toFALSE
as default
Bug fixes
Minor
- Error with
overlay.nma
argument inplot.mb.predict()
fixed
MBNMAtime 0.2.1
CRAN release: 2021-09-13
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)
MBNMAtime 0.2.0
CRAN release: 2021-04-26
Additions/changes
- Added variance adjustment (
covar="varadj"
) for correlation between time-points - this is now the default inmb.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 topredict()
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 ofdecreasing
for rankings - Time-course functions given to
mb.run()
are now given asclass("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()
orsummary()
- 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
CRAN release: 2020-03-04
Additions/changes
- Added citation file
-
plot.mb.network()
now uses alayout
argument that takes an igraph layout function instead oflayout_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.