outstandR 2.0.0

New features

• Explicit Model Specifications: The package architecture has been fundamentally updated to explicitly separate outcome prediction from population weighting. The formula argument across all strategy_*() functions now accepts a list containing outcome_model and balance_model. This conceptual split makes the statistical intent of methods like MAIC much clearer (i.e., defining exactly which covariates to balance vs. which to regress on) and future-proofs the package for upcoming doubly-robust methods that require both models simultaneously.

  # New recommended approach 
  strategy_maic(
    formula = list(
      outcome_model = y ~ trt, 
      balance_model = ~ age + sex
    )
  )
  
  # Legacy approach (still supported) 
  strategy_maic(formula = y ~ trt + age + sex)

• To support this new architecture, strategy_maic() introduces intelligent defaults for missing formula components:

  • If a traditional single formula is passed (legacy support), it automatically infers the balance_model by stripping the response and treatment variables.

  • If balance_model is omitted from a list, it acts as a delayed function defaulting to a linear sum of all covariates found in the aggregate-level data (ALD).

  • If outcome_model is omitted, it gracefully defaults to y ~ trt.

• outstandR() gains a new verbose argument (defaulting to TRUE), allowing users to silence console output during simulations or repetitive tasks. It also introduces proactive warnings for computationally expensive operations (e.g., very high n_boot for MAIC, or large \(N \times R\) for ML G-computation) to prevent users from thinking the session has hung.

• MAIC weight estimation can now balance covariate variances and covariances, reducing residual confounding from mismatched distributions.

  • Added the moments argument to strategy definition functions and calc_maic(). Setting moments = 2 automatically expands the balancing formula to include squared terms.

  • Added the int argument to strategy definition functions and calc_maic(). Setting int = TRUE automatically expands the balancing formula to include all two-way interactions between covariates.

  • Automatic variance target calculation when moments = 2, the maic.boot() function now automatically calculates the required aggregate target for squared terms (\(E[X^2]\)) internally using the base variable’s mean and sd provided in the ALD.

Minor improvements and fixes

• Fixed a critical bug in strategy_mim() and strategy_gcomp_bayes() where covariate uncertainty was not being fully propagated due to incorrect looping over predicted samples.

• Significantly improved the performance of the MAIC method by refactoring maic.boot() to pre-calculate balance and outcome matrices, reducing redundant computations during bootstrap iterations.

• guess_treatment_name() (used under the hood by all strategies when trt_var is NULL) is now smarter. It correctly identifies the treatment variable by checking for duplicated variables in interaction terms, or by falling back to the last main effect term.

• check_balance_formula() has been added to provide strict validation for balance models. It ensures the formula is one-sided (no response variable) and throws an informative warning if the treatment variable is mistakenly included.

• check_formula() now explicitly intercepts Surv objects in outcome models. It throws a clear, informative error stating that survival data support is officially scheduled for a later version.

• Improved reproducibility and side-effect management by switching to withr::local_seed() for local random seed handling.

• Updated the print.outstandR() method to handle delayed balance model functions, providing clearer output for auto-generated models.

• Standardized strategy naming: strategy_gcomp_stan() is now strategy_gcomp_bayes() to more accurately reflect the underlying statistical approach.

Deprecated and defunct

• strategy_stc() is now deprecated and will be removed in a future release. Simulated Treatment Comparison (STC) is being phased out in favor of G-computation approaches (e.g., strategy_gcomp_ml()), which offer better statistical properties for population adjustment.

outstandR 1.0.0

outstandR 1.0.0 marks a major milestone, transforming the original code from the Remiro-Azocar study into a generalized, user-friendly R package.

Breaking changes & major updates

• The aggregate-level data (ALD) input has been redesigned to use a standard long format, replacing the legacy format used in the original study.

• Treatment labels and covariate names are no longer hard-coded. outstandR now dynamically extracts treatment arms (e.g., rather than “A”, “B”, “C”) and general covariates directly from your input data (5119596).

• Covariate generation functions have been separated and moved into their own standalone package, simcovariates.

Expanded modelling capabilities

• The family argument in outstandR() has been expanded to support binary (binomial), continuous (gaussian), and count data (poisson).

• Input data can now contain any combination of binary and continuous covariates, specifically upgraded for MAIC (4fb1e21).

• simulate_ALD_pseudo_pop() now accepts user-provided marginal arguments for a target distribution. These can be defined optionally via marginal_distns and marginal_params in strategy_gcomp_ml() and strategy_gcomp_bayes().

• Users can now explicitly select the outcome scale (e.g., log-odds, risk difference, relative risk) using the new scale argument in outstandR().

• Added support for optional correlation structures in ALD simulations (d48d0ab).

Output & documentation improvements

• The main outstandR() function now returns absolute values in addition to treatment contrasts (2f1fbd7).

• Added a dedicated print() method for outstandR objects to cleanly display analysis results (983cb2f).

• Comprehensive, separate vignettes demonstrating workflows for binary, continuous, and count outcome data (fc10a68).

• A complete package documentation website has been built using pkgdown and is now available via GitHub Pages (4df16f2).