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@dmphillippo dmphillippo released this 24 Jan 14:08
· 128 commits to develop since this release
v0.6.0
6be4758

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).
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