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Mission

R for Health Technology Assessment (HTA) is an academic consortium whose main objective is to explore the use of R for cost-effectiveness analysis (CEA) as an alternative to less efficient, generalisable and powerful software such as spreadsheets. R is a freely available language and environment for statistical computing and graphics which provides a wide variety of statistical and graphical techniques. We advocate the use of proper statistical software, notably R, to be used in the whole process of health economic evaluation.

General topics of interest include a wide range of technical aspects, e.g. the discussion of the many available R add-on packages, as well as ways to help users get the most out of R for CEA. Presentations and public discussions are used to address the computational and transparency advantages of R over Excel for CEA and for easing collaboration. Our members have diverse experience in government (including NICE in the UK), academia, and industry.

Events

Our events, including the annual workshop, short courses and hackathons

R for HTA annual workshop

Next edition: 9-12 October 2020. Check for updates!

Training events

Here’s a list of all the training events that are organised by members of our consortium

Hackathons

Come and play with R!

Online R resources

Relevant R packages and tools for statistical analysis and economic evaluation

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BCEA

Bayesian Cost-Effectiveness Analysis

SAVI

Sheffield Accelerated Value of Information

survHE

Survival analysis in health economic evaluation

flexsurv

Flexible Parametric Survival and Multi-State Models

hesim

Discrete and continuous time Markov/semi-Markov models

heemod

Discrete-time Markov modelling

MAIC

Facilitates performing matching-adjusted indirect comparison (MAIC) analysis for a disconnected treatment network

gemtcPlus

Convinience functions for perfoming Bayesian NMA using the gemtc package

rpsftmPlus

Convenience functions for working with the rpsftm package and general analysis of trials affected by treatment switching

msm

Continuous-time Markov models fit to panel data and hidden Markov models

mstate

Continuous-time Markov/semi-Markov models

missingHE

Health economic evaluations with missing data using a set of pre-defined Bayesian models

EVSI

Calculation and presentation of the Expected Value of Sample Information

Standalone R code

Various scripts/code

Members of the consortium

Co-director

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Gianluca Baio

Professor of Statistics and Health Economics

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Howard Thom

Lecturer in Health Economics

Scientific committee

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Anthony Hatswell

Director and Analyst

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Boby Mihaylova

Associate Professor in Health Economics

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Claire Williams

Senior Researcher in Health Economics

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Iryna Schlackow

Senior Researcher in Health Economics

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James O'Mahony

Research Assistant Professor in Public Health & Primary Care

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Nathan Green

Research Fellow

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Nicky Welton

Professor in Statistical and Health Economic Modelling

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Padraig Dixon

Research Fellow

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Pedro Saramago

Research Fellow

Relevant institutions

UCL
Bristol
HERC
CHE
Trinity College Dublin

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