Baio, G;
Heath, A;
(2016)
When simple becomes complicated: why Excel should lose its place at the top table.
Global and regional health technology assessment
, 4
(1)
e3-e6.
10.5301/grhta.5000247.
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Abstract
Traditionally, the majority of health economic modelling has been performed in spreadsheet calculators such as Microsoft Excel as it is perceived to be more transparent and easy to use. However, as the modelling requirements become more realistic and therefore complex, spreadsheets become increasingly cumbersome and difficult to manage. We argue that specialist statistical packages such as R should be used when the models become suitably complex. We acknowledge the difficulties associated with script-based statistical software, but argue that user-written packages designed for health-technology assessments simplify the analysis when compared to spreadsheet calculators. Additionally, we argue that the production of web-applications based on R will allow the statistical capabilities of specialist software to be available for all. All that is needed is a dialogue between the modellers and the academic to make the software available for all.
Type: | Article |
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Title: | When simple becomes complicated: why Excel should lose its place at the top table |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.5301/grhta.5000247 |
Publisher version: | http://dx.doi.org/10.5301/grhta.5000247 |
Language: | English |
Additional information: | © 2017. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC-ND 4.0) license. |
UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10033873 |
1. | United States | 4 |
2. | China | 3 |
3. | Russian Federation | 2 |
4. | Indonesia | 1 |
5. | France | 1 |
6. | Lithuania | 1 |
7. | United Kingdom | 1 |
8. | Germany | 1 |
9. | Brazil | 1 |
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