This R package was developed as part of the Sheffield Tobacco and Alcohol Policy Modelling https://stapm.gitlab.io/ by the Sheffield Centre for Health and Related Research at the University of Sheffield.
The aim of the research programme is to identify and evaluate approaches to reducing the harm from tobacco and alcohol, with the aim of improving commissioning in a public health policy context, i.e. providing knowledge to support benefits achieved by policymakers.
The two objectives of the research programme are:
- To evaluate the health and economic effects of past trends, policy changes or interventions that have affected alcohol consumption and/or tobacco smoking
- To appraise the health and economic outcomes of potential future trends, changes to alcohol and/or tobacco policy or new interventions
The STAPM modelling is not linked to the tobacco or alcohol industry and is conducted without industry funding or influence.
The code has been made open source for the following two reasons:
- Transparency. Open science, allowing review and feedback to the project team on the code and methods used.
- Methodology sharing. For people to understand the code and methods used so they might use aspects of it in their own work, e.g., because they are doing something partially related that isn’t exactly the same job and might like to ‘dip into’ elements of this code for inspiration.
The code is actively being used in project work. It is being reviewed and developed all the time; more tests and checks are being added.
The repository is not intended to be maintained by an open source community wider than the development team.
The code on Github (https://github.com/STAPM/qalyr) is a mirror of the code in a private Gitlab repository where the actual development takes place (https://gitlab.com/stapm/r-packages/qalyr). The code in the Github repository is linked to a repository on the Open Science Framework, which provides the doi for the package citation (https://osf.io/8v5xj/).
Gillespie D, Webster L, Angus C, Brennan A ([YEAR]). qalyr: An R package for estimating health state utility values for conditions related to tobacco and alcohol. R package version [x.x.x]. University of Sheffield. https://stapm.gitlab.io/r-packages/qalyr/. doi: https://doi.org/10.17605/OSF.IO/8V5XJ
The motivation for qalyr was to standardised the way that health state
utility values for diseases related to tobacco and/or alcohol were
prepared for input to our decision-analytic models. Health state utility
values associated with tobacco- and alcohol-related disease diagnosis
codes are derived from the Health Outcomes Data Repository (HODaR) data
(Currie et al. 2005), and general population
utility values from the Health Survey for England (Ara and Brazier
2010). The suite of functions within qalyr wrangle the
data into the correct format and then calculate the health state utility
values, for which we use the eq5d R package (Morton and Nijjar
2020).
qalyr was created as part of a programme of work on the health
economics of tobacco and alcohol at the School of Health and Related
Research (ScHARR), The University of Sheffield. This programme is based
around the construction of the Sheffield Tobacco and Alcohol Policy
Model (STAPM), which aims to use comparable methodologies to evaluate
the impacts of tobacco and alcohol policies, and investigate the
consequences of clustering and interactions between tobacco and alcohol
consumption behaviours.
The following is a summary from Currie et al. (2005).
“HODaR supplements routine clinically coded data from the Cardiff and Vale NHS Hospitals Trust, UK, with survey data covering sociodemographic characteristics, QoL, utility, and resource use information. Data that constitutes HODaR were collated from a prospective survey of subjects treated as inpatients or outpatients. Details from the survey then need to be linked to existing routine hospital health data. All inpatients aged 18 years or older were surveyed. For inpatients, all subjects were surveyed 6 weeks postdischarge by postal survey with a ‘freepost’ return envelope. For outpatients, all patients attending a selected clinic were handed a survey pack by the clinic receptionist when they attend.”
We were provided with two datasets in the form of inpatient data and survey data. The inpatient data includes a pseudo-anonymised patient identification number and information on the type of admission including the dates of admission and diagnostic and operation codes. The inpatient data is provided at episode level and was filtered such that we were only provided with episodes that had an alcohol or tobacco-related ICD-10 code in one of the diagnostic positions remained.
The HODaR data are risk-bearing because they constitute patient-level hospital records and survey data. Our data storage and processing takes place on a restricted user-access University of Sheffield managed virtual machine. Data storage and processing follows SCHARR’s Information Governance Policy.
The qalyr package contains functions to calculate utilities using the
EQ-5D, for specific diseases and for the general population.
The inputs are the HODaR data and previously published estimates of general population utility values by Ara and Brazier (2010), who analysed the Health Survey for England.
The processes are performed by functions that:
- Read and link the HODaR survey and inpatient data
- Calculate and adjust the disease specific and general population utility values
The outputs are lookup tables of utility values ready for use in our modelling.
qalyr is publicly available via Github.
By default the user should install the latest tagged version of the package. Otherwise, if you want to reproduce project work and know the version of the package used, install that version.
If on a University of Sheffield managed computer, install the R, RStudio
and Rtools bundle from the Software Centre. Install Rtools - using the
installr
package can make this easier. Then install the latest or a specified
version of qalyr from Github with:
#install.packages("devtools")
devtools::install_git(
"https://github.com/stapm/qalyr.git",
ref = "x.x.x",
build_vignettes = FALSE)
# ref = "x.x.x" is the version to install - change to the version you want e.g. "1.2.3"Or clone the package repo locally and use the ‘install and restart’ button in the Build tab of RStudio. This option is more convenient when testing development versions.
Then load the package, and some other packages that are useful. Note
that the code within qalyr uses the data.table::data.table() syntax.
# Load the package
library(qalyr)
# Other useful packages
library(dplyr) # for data manipulation and summary
library(magrittr) # for pipes
library(ggplot2) # for plottingAra, Roberta, and John E Brazier. 2010. “Populating an Economic Model with Health State Utility Values: Moving Toward Better Practice.” Value in Health 13 (5): 509–18. https://doi.org/10.1111/j.1524-4733.2010.00700.x.
Currie, Craig J, Phil McEwan, John R Peters, Tunia C Patel, and Simon Dixon. 2005. “The Routine Collation of Health Outcomes Data from Hospital Treated Subjects in the Health Outcomes Data Repository (HODaR): Descriptive Analysis from the First 20,000 Subjects.” Value in Health 8 (5): 581–90. https://doi.org/10.1111/j.1524-4733.2005.00046.x.
Morton, Fraser, and Jagtar Singh Nijjar. 2020. eq5d: Methods for Calculating ’EQ-5D’ Utility Index Scores. https://CRAN.R-project.org/package=eq5d.