[statisticshealtheconomics] outstandR 2.0.0

n.green@ucl.ac.uk (Nathan Green) — Tue, 23 Jun 2026 14:14:26 GMT

For the problem of indirect treatment comparison with limited subject-level data, this package provides tools for model-based standardisation with several different computation approaches. See Remiro‐Azócar A, Heath A, Baio G (2022) ``Parametric G‐computation for compatible indirect treatment comparisons with limited individual patient data'', Res. Synth. Methods, 1–31. ISSN 1759-2879, .

[statisticshealtheconomics] blendR 1.0.0

blendr-pkg@proton.me (Zhaojing Che) — Thu, 16 Oct 2025 07:41:13 GMT

Create a blended curve from two survival curves, which is particularly useful for survival extrapolation in health technology assessment. The main idea is to mix a flexible model that fits the observed data well with a parametric model that encodes assumptions about long-term survival. The two curves are blended into a single survival curve that is identical to the first model over the range of observed times and gradually approaches the parametric model over the extrapolation period based on a given weight function. This approach allows for the inclusion of external information, such as data from registries or expert opinion, to guide long-term extrapolations, especially when dealing with immature trial data. See Che et al. (2022) .

statisticshealtheconomics.r-universe.dev

[statisticshealtheconomics] outstandR 2.0.0

[statisticshealtheconomics] blendR 1.0.0