drMAIC 0.1.0 (2024)

Initial release

New features

• 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.

Example data

• 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.

Guideline alignment

• NICE DSU Technical Support Document 18 (Phillippo et al., 2016; 2020)
• Cochrane Handbook Chapter 23 (Dias et al.)
• ISPOR Task Force on Indirect Treatment Comparisons
• Remiro-Azócar et al. (2022) Statistics in Medicine