Missing data

Joint longitudinal models for dealing with missing at random data in trial-based economic evaluations

Objectives: In trial-based economic evaluation, some individuals are typically associated with missing data at some time point, so that their corresponding aggregated outcomes (eg, quality-adjusted life-years) cannot be evaluated. Restricting the …

Linear mixed models to handle missing at random data in trial-based economic evaluations

Contributed presentation

Joint longitudinal models for dealing with missing at random data in trial-based economic evaluations

Health economic evaluations based on patient-level data collected alongside clinical trials (e.g. health related quality of life and resource use measures) are an important component of the process which informs resource allocation decisions. Almost …

Choosing the Missing Data Method in Trial-Based Economic Evaluations. How to Make the Right Choice?

Invited presentation

A Bayesian Parametric Approach to Handle Missing Longitudinal Outcome Data in Trial-Based Health Economic Evaluations

Contibuted poster

A Bayesian Parametric Approach to Handle Missing Longitudinal Outcome Data in Trial-Based Health Economic Evaluations

Contibuted presentation

MissingHE 1.2.1

I have finally found some time to update the version for my R package missingHE, for which version 1.2.1 is now available on CRAN. I included two main features to the previous version of the package.

Adjusting for partially-observed utilities and costs in trial-based cost-effectiveness analysis: a comparison of different methods and their performance

Contributed presentation

MissingHE

missingHE is a R package aimed at providing some useful tools to analysts in order to handle missing outcome data under a Full Bayesian framework in economic evaluations. The package relies on the R package R2jags to implement Bayesian methods via the statistical software JAGS.

Bayesian methods for addressing missing data in health economic evaluations

Pre-conference workshop