multinma 0.6.1
- Fix: Piecewise exponential hazard models no longer give errors during set-up. Calculation of RW1 prior weights needed to be handled as a special case.
multinma 0.6.0
Feature: Survival/time-to-event models are now supported
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.
- Left, right, and interval censoring as well as left truncation (delayed entry) are all supported.
- The available likelihoods are Exponential (PH and AFT forms), Weibull (PH and AFT forms), Gompertz, log-Normal, log-Logistic, Gamma, Generalised Gamma, flexible M-splines on the baseline hazard, and piecewise exponential hazards.
- Auxiliary parameters (e.g. shapes, spline coefficients) are always stratified by study to respect randomisation, and may be further stratified by treatment (e.g. to relax the proportional hazards assumption) and/or by additional factors using the
aux_by
argument to nma()
.
- A regression model may be defined for the auxiliary parameters using the
aux_regression
argument to nma()
, allowing non-proportionality to be modelled by treatment and/or covariate effects on the shapes or spline coefficients.
- The
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.
- The
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.
- A new vignette demonstrates ML-NMR survival analysis with an example of progression-free survival after autologous stem cell transplant for newly diagnosed multiple myeloma, with corresponding datasets
ndmm_ipd
, ndmm_agd
, and ndmm_agd_covs
.
Feature: Automatic checking of numerical integration for ML-NMR models
- The accuracy of numerical integration for ML-NMR models can now be checked automatically, and is by default. To do so, half of the chains are run with
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.
- This feature is controlled by a new
int_check
argument to nma()
, which is enabled (TRUE
) by default.
- Saving thinned cumulative integration points can now be disabled with
int_thin = 0
, and is now disabled by default. The previous default was int_thin = max(n_int %/% 10, 1)
.
- Because we can now check sufficient accuracy automatically, the default number of integration points
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.
- As a result, ML-NMR models are now much faster to run by default, both due to lower
n_int
and disabling saving cumulative integration points.
Other updates
- Feature:
dic()
now includes an option to use the pV penalty instead of pD.
- Feature: The
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.
- Improvement:
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).
- Improvement: Compatibility with future rstan versions (PR #25).
- Improvement: Added a
plot.mcmc_array()
method, as a shortcut for plot(summary(x), ...)
.
- Fix: In
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
.
- Fix: Documentation corrections (PR #24).
- Fix: Added missing
as.tibble.stan_nma()
and as_tibble.stan_nma()
methods, to complement the existing as.data.frame.stan_nma()
.
- Fix: Bug in ordered multinomial models where data in studies with missing categories could be assigned the wrong category (#28).
multinma 0.5.1
- Fix: Now compatible with latest StanHeaders v2.26.25 (fixes #23)
- Fix: Dealt with various tidyverse deprecations
- Fix: Updated TSD URLs again (thanks to @ndunnewind)
multinma 0.5.0
- Feature: Treatment labels in network plots can now be nudged away from the nodes when
weight_nodes = TRUE
, using the new nudge
argument to plot.nma_data()
(#15).
- Feature: The data frame returned by calling
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
column
- Feature: Added log t prior distribution
log_student_t()
, which can be used for positive-valued parameters (e.g. heterogeneity variance).
- Improvement:
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.
- Improvement: Updated plaque psoriasis ML-NMR vignette to include new analyses, including assessing the assumptions of population adjustment and synthesising multinomial outcomes.
- Improvement: Improved behaviour of the
.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.
- Fix: CRAN check note for manual HTML5 compatibility.
- Fix: Residual deviance and log likelihood parameters are now named correctly when only contrast-based aggregate data is present (PR #19).
multinma 0.4.2
- Fix: Error in
get_nodesplits()
when studies have multiple arms of the same treatment.
- Fix:
print.nma_data()
now prints the repeated arms when studies have multiple arms of the same treatment.
- Fix: CRAN warning regarding invalid img tag height attribute in documentation.
multinma 0.4.1
- Fix: tidyr v1.2.0 breaks ordered multinomial models when some studies do not report all categories (i.e. some multinomial category outcomes are
NA
in multi()
) (PR #11)
multinma 0.4.0
- Feature: Node-splitting models for assessing inconsistency are now available with
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()
.
- Feature: The correlation matrix for generating integration points with
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"
.
- Feature: For random effects models, the predictive distribution of relative/absolute effects in a new study can now be obtained in
relative_effects()
and predict.stan_nma()
respectively, using the new argument predictive_distribution = TRUE
.
- Feature: Added option to calculate SUCRA values when summarising the posterior treatment ranks with
posterior_ranks()
or posterior_rank_probs()
, when argument sucra = TRUE
.
- Improvement: Factor order is now respected when
trt
, study
, or trt_class
are factors, previously the order of levels was reset into natural sort order.
- Improvement: Update package website to Bootstrap 5 with release of pkgdown 2.0.0
- Fix: Model fitting is now robust to non-default settings of
options("contrasts")
.
- Fix:
plot.nma_data()
no longer gives a ggplot deprecation warning (PR #6).
- Fix: Bug in
predict.stan_nma()
with a single covariate when newdata
is a data.frame
(PR #7).
- Fix: Attempting to call
predict.stan_nma()
on a regression model with only contrast data and no newdata
or baseline
specified now throws a descriptive error message.
multinma 0.3.0
- Feature: Added
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.
- Feature: The
baseline
argument to predict.stan_nma()
can now accept a (named) list of baseline distributions if newdata
contains multiple studies.
- Improvement: Misspecified
newdata
arguments to functions like relative_effects()
and predict.stan_nma()
now give more informative error messages.
- Fix: Constructing models with contrast-based data previously gave errors in some scenarios (ML-NMR models, UME models, and in some cases AgD meta-regression models).
- Fix: Ensure CRAN additional checks with
--run-donttest
run correctly.
multinma 0.2.1
- Fix: Producing relative effect estimates for all contrasts using
relative_effects()
with all_contrasts = TRUE
no longer gives an error for regression models.
- Fix: Specifying the covariate correlation matrix
cor
in add_integration()
is not required when only one covariate is present.
- Improvement: Added more detailed documentation on the likelihoods and link functions available for each data type (
likelihood
and link
arguments in nma()
).
multinma 0.2.0
- Feature: The
set_*()
functions now accept dplyr::mutate()
style semantics, allowing inline variable transformations.
- Feature: Added ordered multinomial models, with helper function
multi()
for specifying the outcomes. Accompanied by a new data set hta_psoriasis
and vignette.
- Feature: Implicit flat priors can now be specified, on any parameter, using
flat()
.
- Improvement:
as.array.stan_nma()
is now much more efficient, meaning that many post-estimation functions are also now much more efficient.
- Improvement:
plot.nma_dic()
is now more efficient, particularly with large numbers of data points.
- Improvement: The layering of points when producing “dev-dev” plots using
plot.nma_dic()
with multiple data types has been reversed for improved clarity (now AgD over the top of IPD).
- Improvement: Aggregate-level predictions with
predict()
from ML-NMR / IPD regression models are now calculated in a much more memory-efficient manner.
- Improvement: Added an overview of examples given in the vignettes.
- Improvement: Network plots with
weight_edges = TRUE
no longer produce legends with non-integer values for the number of studies.
- Fix:
plot.nma_dic()
no longer gives an error when attempting to specify .width
argument when producing “dev-dev” plots.
multinma 0.1.3
- Format DESCRIPTION to CRAN requirements
multinma 0.1.2
- Wrapped long-running examples in
\donttest{}
instead of \dontrun{}
multinma 0.1.1
- Reduced size of vignettes
- Added methods paper reference to DESCRIPTION
- Added zenodo DOI
multinma 0.1.0
- Feature: Network plots, using a
plot()
method for nma_data
objects.
- Feature:
as.igraph()
, as_tbl_graph()
methods for nma_data
objects.
- Feature: Produce relative effect estimates with
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.
- Feature: Produce predictions of absolute effects with a
predict()
method for stan_nma
objects.
- Feature: Plots of relative effects, ranks, predictions, and parameter estimates via
plot.nma_summary()
.
- Feature: Optional
sample_size
argument for set_agd_*()
that:
- Enables centering of predictors (
center = TRUE
) in nma()
when a regression model is given, replacing the agd_sample_size
argument of nma()
- Enables production of study-specific relative effects, rank probabilities, etc. for studies in the network when a regression model is given
- Allows nodes in network plots to be weighted by sample size
- Feature: Plots of residual deviance contributions for a model and “dev-dev” plots comparing residual deviance contributions between two models, using a
plot()
method for nma_dic
objects produced by dic()
.
- Feature: Complementary log-log (cloglog) link function
link = "cloglog"
for binomial likelihoods.
- Feature: Option to specify priors for heterogeneity on the standard deviation, variance, or precision, with argument
prior_het_type
.
- Feature: Added log-Normal prior distribution.
- Feature: Plots of prior distributions vs. posterior distributions with
plot_prior_posterior()
.
- Feature: Pairs plot method
pairs()
.
- Feature: Added vignettes with example analyses from the NICE TSDs and more.
- Fix: Random effects models with even moderate numbers of studies could be very slow. These now run much more quickly, using a sparse representation of the RE correlation matrix which is automatically enabled for sparsity above 90% (roughly equivalent to 10 or more studies).
multinma 0.0.1