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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.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.