Session 1 | R for HTA (Basics) Workshop 2026
The global financial crisis had just hit. Governments were debating the biggest economic question of the decade:
Should we spend our way out of the crisis, or cut spending to reduce debt?
Trillions of dollars — and the livelihoods of millions — hung on the answer.
Carmen Reinhart and Kenneth Rogoff published “Growth in a Time of Debt”.
Their finding: countries with public debt above 90% of GDP experience negative economic growth (−0.1% average).
Cited 3,000+ times. A paper that shaped global policy.
Paul Ryan cited it in the US Budget Committee. The European Commission and UK Treasury used it to justify austerity. The IMF referenced it in country recommendations.
Governments across Europe and the US cut public spending — affecting:
Healthcare budgets — hospital funding reduced, waiting lists grew
Social services — disability support, unemployment benefits cut
Education funding — university fees tripled in England
Research grants — scientific funding stalled across Europe
Thomas Herndon, a 28-year-old PhD student at UMass Amherst, was given a class assignment:
Pick a famous economics paper and try to replicate its results.
He chose Reinhart-Rogoff. He couldn’t replicate the results.
He emailed the authors. They sent him the spreadsheet.
Error 1: Excel Row Exclusion
The AVERAGE formula covered rows 30–44 instead of 30–49. Five countries (Australia, Austria, Belgium, Canada, Denmark) were simply left out. A drag-select error — the kind Excel makes invisible.
. . .
Error 2: Unconventional Weighting
New Zealand’s single year of high-debt data (−7.6% growth) was given the same weight as the UK’s 19 years. This one outlier dragged the entire average into negative territory.
. . .
Error 3: Selective Data Exclusion
Post-war data from three countries was excluded without clear justification.
−0.1%
ORIGINAL (with errors)
+2.2%
CORRECTED (Herndon et al.)
Growth was POSITIVE, not negative. One spreadsheet error. Billions in policy impact.
Nobody could catch the error earlier — because nobody could audit the spreadsheet.
Your cost-effectiveness analysis informs drug pricing, reimbursement, and treatment guidelines.
Can a reviewer trace every calculation in your Excel model?
Can you be certain there is no hidden error in a nested cell reference?
What if there was a better way?
Let me show you what R makes possible — interactively.
We will explore:
In the Shiny app, everything was:
Reproducible — same seed, same results, every time, anywhere
Auditable — every simulation row visible, every formula transparent
Interactive — change a parameter, see the result immediately
Scalable — 10,000 PSA iterations in a fraction of a second
The entire PSA logic was 3 lines of R. The entire Markov model was 10 lines. Try auditing that in Excel.
PSA is painful in Excel — complex macros or VBA; spreadsheet becomes slow and fragile
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Reproducibility is difficult — tracing nested cell references across multiple sheets
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Version control is manual — Model_v3_final_FINAL_revised.xlsx
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Transparency for publication — journals and HTA agencies increasingly require open-source models
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Collaboration is messy — two people cannot work on the same Excel model simultaneously
R is not replacing your HTA expertise.
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It is giving that expertise a more powerful medium.
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The clinical judgement, model structure, parameter selection — that remains yours.
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R simply makes the execution more reproducible, transparent, and scalable.
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Let us get started.
R for HTA (Basics) — RRC-HTA, AIIMS Bhopal