R for Health Technology Assessment

Quarto

Academia

publication

health economics

statistics

Missing Data (book chapter)

Authors

Affiliations

Andrea Gabrio

Maastricht University

Alexina J Mason

London School of Hygiene and Tropical Medicine

Baptiste Leurent

University College London

Manuel Gomes

University College London

Published

October 2, 2026

Abstract

This chapter details methods for handling missing data in health economic evaluations, …

Keywords

Missing Data, Economic Evaluations

Abstract

This chapter details methods for handling missing data in health economic evaluations, focusing on cost-effectiveness analyses of randomised controlled trials. It explores different types of missingness (MCAR, MAR, MNAR), outlining the implications of each for analysis. The chapter then compares ad-hoc methods (case deletion, single imputation) with statistically principled methods (multiple imputation, fully Bayesian approaches). A case study using the Ten Top Tips trial illustrates multiple imputation and Bayesian techniques, including sensitivity analyses to assess the robustness of results under varying missing data assumptions. The Bayesian approach is explored extensively using JAGS within R.

Citation

BibTeX citation:

@online{gabrio2026,
  author = {Gabrio, Andrea and J Mason, Alexina and Leurent, Baptiste
    and Gomes, Manuel},
  title = {R for {Health} {Technology} {Assessment}},
  date = {2026-10-02},
  url = {https://www.taylorfrancis.com/books/edit/10.1201/9781003031819/health-technology-assessment-gianluca-baio-howard-thom-petros-pechlivanoglou},
  doi = {10.1201/9781003031819},
  langid = {en},
  abstract = {{[}This chapter details methods for handling missing data
    in health economic evaluations, ...{]}\{style=“font-size: 85\%”\}}
}

For attribution, please cite this work as:

Gabrio, Andrea, Alexina J Mason, Baptiste Leurent, and Manuel Gomes. 2026. “R for Health Technology Assessment.” Chapman and Hall/CRC, October 2. https://doi.org/10.1201/9781003031819.