class_effects argument in nma() allows
models with independent, exchangeable, or common class effects to be
fitted. Class standard deviations can also be shared between classes or
subsets of classes, controlled by the class_sd argument.
These features are demonstrated in a new vignette, analysing a network
of interventions for social anxiety.plot.nma_dic(), with the option
type = "leverage".combine_network(), where previously these were discarded.
One potential use case is to specify different types of marginal
distributions or correlation structures for different AgD studies in the
network, by setting these up separately with
add_integration() before combining with
combine_network().as.data.frame.nma_dic() and
as_tibble.nma_dic() methods that return data frames of the
pointwise contributions to the DIC, and as.matrix.nma_dic()
and as.array.nma_dic() methods that return posterior
samples of the residual deviances as a matrix or 3D array.softmax() and inv_softmax()
transforms are now exported.r = n on all arms) no longer give NaN
residual deviance.aux_regression = ~.trt were
incorrectly omitting the treatment effects on the auxiliary parameter(s)
in some cases (#43).marginal_effects() for survival outcomes
with a single target population previously gave an error.aux_regression had been specified were giving an error
(#44). aux_regression and aux_by have no
effect for exponential models since there are no auxiliary (shape)
parameters and are ignored, now with a warning.c().predict() outside of a
plot() call no longer gives an error (#40).marginal_effects() function produces
marginal treatment effects, as a wrapper around absolute predictions
from predict(). For example, for an analysis with a binary
outcome marginal odds ratios, risk ratios, or risk differences may be
produced. For survival outcomes, marginal effects may be based on the
full range of predictions produced by predict(), such as
marginal differences in restricted mean survival times, or time-varying
marginal hazard ratios.predict() or
marginal_effects() from ML-NMR models that may take longer
to run. These can be controlled with the new progress
argument.trt_ref argument to
predict() has been renamed to baseline_ref;
using trt_ref is now soft-deprecated. Renaming this
argument baseline_ref follows the naming convention for the
other arguments (baseline_type,
baseline_level) that specify the details of a provided
baseline distribution. This also makes way for the new
marginal_effects() functionality.predict() for AgD meta-regression
models with new data and baseline_type = "response" to fail
with an error.plot.nma_data() with weight_edges = TRUE was
incorrect when a study had multiple arms of the same treatment. This now
correctly counts the number of studies making a comparison, rather than
the number of arms.set_ipd() now has a Surv argument for
specifying survival outcomes using survival::Surv(), and a
new function set_agd_surv() sets up aggregate data in the
form of event/censoring times (e.g. from digitized Kaplan-Meier curves)
and overall covariate summaries.aux_by
argument to nma().aux_regression argument to nma(), allowing
non-proportionality to be modelled by treatment and/or covariate effects
on the shapes or spline coefficients.predict() method produces estimates of survival
probabilities, hazards, cumulative hazards, mean survival times,
restricted mean survival times, quantiles of the survival time
distribution, and median survival times. All of these predictions can be
plotted using the plot() method.geom_km() function assists in plotting Kaplan-Meier
curves from a network object, for example to overlay these on estimated
survival curves. The transform argument can be used to
produce log-log plots for assessing the proportional hazards assumption,
along with cumulative hazards or log survival curves.ndmm_ipd, ndmm_agd, and
ndmm_agd_covs.n_int and half with n_int/2
integration points. Any Rhat or effective sample size warnings can then
be ascribed to either: non-convergence of the MCMC chains, requiring
increased number of iterations iter in nma(),
or; insufficient accuracy of numerical integration, requiring increased
number of integration points n_int in
add_integration(). Descriptive warning messages indicate
which is the case.int_check argument
to nma(), which is enabled (TRUE) by
default.int_thin = 0, and is now disabled by default. The
previous default was int_thin = max(n_int %/% 10, 1).n_int in
add_integration() has been lowered to 64. This is still a
conservative choice, and will be sufficient in many cases; the previous
default of 1000 was excessive.n_int and disabling saving cumulative
integration points.dic() now includes an option to use the pV
penalty instead of pD.baseline and aux arguments to
predict() can now be specified as the name of a study in
the network, to use the parameter estimates from that study for
prediction.predict() will now produce aggregate-level
predictions over a sample of individuals in newdata for
ML-NMR models (previously newdata had to include
integration points).plot.mcmc_array() method, as a
shortcut for plot(summary(x), ...).plot.nma_data(), using a custom
layout that is not a string (e.g. a data frame of layout
coordinates) now works as expected when nudge > 0.as.tibble.stan_nma() and
as_tibble.stan_nma() methods, to complement the existing
as.data.frame.stan_nma().weight_nodes = TRUE, using the new
nudge argument to plot.nma_data() (#15).as_tibble()
or as.data.frame() on an nma_summary object
(such as relative effects or predictions) now includes columns for the
corresponding treatment (.trt) or contrast
(.trta and .trtb), and a
.category column may be included for multinomial models.
Previously these details were only present as part of the
parameter columnlog_student_t(), which can be used for positive-valued
parameters (e.g. heterogeneity variance).set_agd_contrast() now produces an
informative error message when the covariance matrix implied by the
se column is not positive definite. Previously this was
only checked by Stan after calling the nma() function..trtclass
special in regression formulas, now main effects of
.trtclass are always removed since these are collinear with
.trt. This allows expansion of interactions with
* to work properly, e.g. ~variable*.trtclass,
whereas previously this resulted in an over-parametrised model.get_nodesplits() when studies have
multiple arms of the same treatment.print.nma_data() now prints the repeated arms when
studies have multiple arms of the same treatment.NA in multi()) (PR #11)consistency = "nodesplit" in
nma(). Comparisons to split can be chosen using the
nodesplit argument, by default all possibly inconsistent
comparisons are chosen using get_nodesplits().
Node-splitting results can be summarised with
summary.nma_nodesplit() and plotted with
plot.nodesplit_summary().add_integration() for ML-NMR models is now adjusted to
the underlying Gaussian copula, so that the output correlations of the
integration points better match the requested input correlations. A new
argument cor_adjust controls this behaviour, with options
"spearman", "pearson", or "none".
Although these correlations typically have little impact on the results,
for strict reproducibility the old behaviour from version 0.3.0 and
below is available with cor_adjust = "legacy".relative_effects() and predict.stan_nma()
respectively, using the new argument
predictive_distribution = TRUE.posterior_ranks() or
posterior_rank_probs(), when argument
sucra = TRUE.trt,
study, or trt_class are factors, previously
the order of levels was reset into natural sort order.options("contrasts").plot.nma_data() no longer gives a ggplot
deprecation warning (PR #6).predict.stan_nma() with a single covariate
when newdata is a data.frame (PR #7).predict.stan_nma() on a
regression model with only contrast data and no newdata or
baseline specified now throws a descriptive error
message.baseline_type and
baseline_level arguments to
predict.stan_nma(), which allow baseline distributions to
be specified on the response or linear predictor scale, and at the
individual or aggregate level.baseline argument to
predict.stan_nma() can now accept a (named) list of
baseline distributions if newdata contains multiple
studies.newdata arguments to
functions like relative_effects() and
predict.stan_nma() now give more informative error
messages.--run-donttest
run correctly.relative_effects() with all_contrasts = TRUE
no longer gives an error for regression models.cor in
add_integration() is not required when only one covariate
is present.likelihood
and link arguments in nma()).set_*() functions now accept
dplyr::mutate() style semantics, allowing inline variable
transformations.multi() for specifying the outcomes. Accompanied by a new
data set hta_psoriasis and vignette.flat().as.array.stan_nma() is now much more
efficient, meaning that many post-estimation functions are also now much
more efficient.plot.nma_dic() is now more efficient,
particularly with large numbers of data points.plot.nma_dic() with multiple data types has been
reversed for improved clarity (now AgD over the top of IPD).predict()
from ML-NMR / IPD regression models are now calculated in a much more
memory-efficient manner.weight_edges = TRUE no
longer produce legends with non-integer values for the number of
studies.plot.nma_dic() no longer gives an error when
attempting to specify .width argument when producing
“dev-dev” plots.\donttest{} instead of
\dontrun{}plot() method for
nma_data objects.as.igraph(), as_tbl_graph()
methods for nma_data objects.relative_effects(), posterior ranks with
posterior_ranks(), and posterior rank probabilities with
posterior_rank_probs(). These will be study-specific when a
regression model is given.predict() method for stan_nma objects.plot.nma_summary().sample_size argument for
set_agd_*() that:
center = TRUE) in
nma() when a regression model is given, replacing the
agd_sample_size argument of nma()plot() method for nma_dic
objects produced by dic().link = "cloglog" for binomial likelihoods.prior_het_type.plot_prior_posterior().pairs().
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