compute_weights(): Entropy balancing / exponential tilting for MAIC weight estimation,
with support for mean-only and mean+SD matching. Returns ESS, convergence diagnostics,
and pre/post SMDs.
dr_maic(): Doubly robust MAIC estimator combining IPW (standard MAIC) with
outcome regression (STC / g-computation). Supports binary, continuous, and
time-to-event outcomes. Effect measures: RD, RR, OR, HR, MD.
maic_diagnostics(): Love plot, weight distribution plot, covariate balance table,
and ESS summary. All plots are ggplot2 objects.
check_assumptions(): Structured assumption checklist aligned with NICE DSU TSD 18
and Cochrane Handbook Chapter 23. Checks ESS adequacy, covariate balance,
optimiser convergence, and DR augmentation term.
bootstrap_ci(): Non-parametric bootstrap confidence intervals (BCa, percentile,
normal) for all three estimators (MAIC, STC, DR-MAIC). Full bootstrap distribution
plot included.
sensitivity_analysis(): E-value computation (VanderWeele & Ding, 2017),
weight trimming sensitivity analysis, and leave-one-variable-out (LOVO) analysis.
All results include ggplot2 visualisations.
nice_report(): Structured submission-ready report covering population
characteristics, weight estimation, covariate balance (NICE TSD 18 format),
treatment effect estimates, uncertainty quantification, sensitivity analysis,
assumptions/limitations, and a citable methods paragraph.
nsclc_ipd: Simulated individual patient data (n = 200) from a hypothetical
single-arm immunotherapy trial in advanced NSCLC.nsclc_agd: Simulated aggregate data from a hypothetical comparator trial.