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dceasimR - Distributional Cost-Effectiveness Analysis for Health Technology Assessment

Implements distributional cost-effectiveness analysis (DCEA) as described in Cookson et al. (2020, ISBN:9780198838197) and the methods endorsed by NICE (2025) for health technology evaluation. Provides functions for both aggregate and full-form DCEA, inequality measurement (Atkinson index, Gini coefficient, slope index of inequality, relative index of inequality), social welfare function evaluation, equity-efficiency impact plane visualisation, and sensitivity analysis over inequality aversion parameters. Includes baseline health distributions for England (by IMD quintile), Canada (income quintile), and global WHO regions. Suitable for academic research, health technology assessment submissions, and public health policy analysis.

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5.15 score 2 stars 9 scripts 438 downloads

metaSurvival - Meta-analysis of a single survival curve using the multivariate methodology of DerSimonian and Laird

This package contains functions to assess a summary survival curve from survival probabilities and number of at-risk patients collected at various points in time in various studies, and to test the between-strata heterogeneity.

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4.65 score 9 stars 4 scripts 241 downloads

htaBIM - Budget Impact Modelling for Health Technology Assessment

Implements a structured, reproducible framework for budget impact modelling (BIM) in health technology assessment (HTA), following the ISPOR Task Force guidelines (Sullivan et al. (2014) <doi:10.1016/j.jval.2013.08.2291> and Mauskopf et al. (2007) <doi:10.1111/j.1524-4733.2007.00187.x>). Provides functions for epidemiology-driven population estimation, market share modelling with flexible uptake dynamics, per-patient cost calculation across multiple cost categories, multi-year budget projections, payer perspective analysis, deterministic sensitivity analysis (DSA), and probabilistic sensitivity analysis (PSA) via Monte Carlo simulation. Produces submission-quality outputs including ISPOR-aligned summary tables, scenario comparison tables, per-patient cost breakdowns, tornado diagrams, PSA histograms, and text and HTML reports compatible with NICE, CADTH, and EU-HTA dossier formats. Ships with an interactive Shiny dashboard built on 'bslib' for point-and-click model building and exploration.

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4.00 score 474 downloads

closeloop - Integrate Single-Arm Observational Data in Network Meta Analysis

This package Calculates distance between single-arm observational studies by using co-variates information to remove heterogeneity in Network Meta-Analysis in Randomized clinical trials. This package will facilitate the inclusion of observational data in Network Meta ANalysis, thereby enhancing the comprehensiveness and robustness of comparative effectiveness research.The proposed R package will provide researchers with a powerful tool to systematically adjust for heterogeneity in single-arm observational studies, enabling their integration into NMAs. This will improve the accuracy and reliability of comparative effectiveness research, ultimately informing better healthcare decisions. Schmitz S et. al. The use of single armed observational data to closing the gap in otherwise disconnected evidence networks: a network meta-analysis in multiple myeloma. BMC Med Res Methodol. 2018 Jun 28;18(1):66. doi: 10.1186/s12874-018-0509-7. PMID: 29954322; PMCID: PMC6022299.

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3.00 score 172 downloads