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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()
.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.