https://anupama-singh01.r-universe.devRecent package updates in anupama-singh01R-universehttps://github.com/anupama-singh01.pnghttps://anupama-singh01.r-universe.devTue, 24 Mar 2026 18:30:29 GMTanupama.singh@pharmacoevidence.com (Anupama Singh)Implements Doubly Robust Matching-Adjusted Indirect Comparison (DR-MAIC) for population-adjusted indirect treatment comparisons in health technology appraisal (HTA). The package provides: (1) standard MAIC via entropy balancing / exponential tilting; (2) augmented/doubly robust MAIC combining inverse probability weighting with outcome regression; (3) comprehensive covariate balance diagnostics including standardised mean differences, Love plots, and effective sample size; (4) sensitivity analyses including E-values, weight trimming, and variable exclusion analyses; (5) bootstrap confidence intervals; and (6) submission-ready outputs aligned with NICE Decision Support Unit Technical Support Document 18, Cochrane Handbook guidance on indirect comparisons, and ISPOR best practice guidelines. Supports binary (risk difference, risk ratio, odds ratio) and time-to-event (hazard ratio) outcomes.https://github.com/r-universe/heorlytics/actions/runs/36847856492Tue, 24 Mar 2026 18:30:29 GMTdrMAIC0.1.0successhttps://heorlytics.r-universe.devhttps://github.com/heorlytics/drmaicdrMAIC_intro.RmddrMAIC_intro.htmlDoubly Robust MAIC for HTA: A Complete Worked Example in Advanced NSCLC2026-03-23 04:50:112026-03-24 18:30:29