- Fix: Predictions for non-proportional hazards IPD NMA or ML-NMR
survival models using
`aux_regression = ~.trt`

were incorrectly omitting the treatment effects on the auxiliary parameter(s) in some cases (#43). - Fix: Calling
`marginal_effects()`

for survival outcomes with a single target population previously gave an error. - Fix: Predictions from exponential models where
`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. - Fix: Avoid an error when trying to fit M-spline models combining IPD
and AgD in R versions prior to 4.1.0, due to integer coercion of factors
by
`c()`

.

- Fix: Producing survival/hazard/cumulative hazard predictions for
survival models with
`predict()`

outside of a`plot()`

call no longer gives an error (#40). - Fix: Increased StanHeaders version requirement to version 2.32.9 or later, to avoid CRAN sanitizer warnings (caused by stan-dev/rstan#1111).

- Feature: The new
`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. - Feature: Progress bars are now displayed when running interactively
for calculations with
`predict()`

or`marginal_effects()`

from ML-NMR models that may take longer to run. These can be controlled with the new`progress`

argument. - Deprecation: The
`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. - Fix: Fallback formatting used by print methods when the crayon package is not installed now works properly, rather than giving errors.
- Fix: Small bug caused
`predict()`

for AgD meta-regression models with new data and`baseline_type = "response"`

to fail with an error. - Fix: The number of studies on a contrast in a network plot
`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.

- 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.

`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`

.

- 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.

- 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).

- 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)

- 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).

- 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.

- 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)

- 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.

- 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.

- 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()`

).

- 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.

- Format DESCRIPTION to CRAN requirements

- Wrapped long-running examples in
`\donttest{}`

instead of`\dontrun{}`

- Reduced size of vignettes
- Added methods paper reference to DESCRIPTION
- Added zenodo DOI

- 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

- Enables centering of predictors (
- 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).

- Initial release.