Trial-Based Economic Evaluations in R
Jason Shafrin
Senior Managing Director, Center for Healthcare Economics & Policy at FTI Consulting; Adjunct Professor, University of Southern California
A recent paper by Ben et al. (2023) provides an R tutorial for implementing economic evaluations--often cost effectiveness analyses--using data from clinical trials and analyzed using R. The article starts by providing a summaries of key issues researchers face when conducting these economic evaluations:
Here is some sample code for implementing each of the 4 approaches.
Missing values. The relevant R package for MI is mice.
Addressing skewed data and correlated costs with bootstrapping and seemingly unrelated regressions (SUR) methodology. The authors use the boot function provided by the boot R Package. The boot function is used to resample the data and for each bootstrap sample a SUR model is fit using the systemfit function. [The authors note that rather than using SUR, a linear mixed model (LMM) could be fit instead using the lme4 or nlme R packages].
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Then one can extract relevant statistics of interest as follows:
Additional instructions are given on how to create a cost-effectiveness plane and cost-effectiveness acceptability curve. You can read the full article here.
Originally posted at Healthcare Economist.?
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