# 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
