Prioritizing Future Research on Allopurinol and Febuxostat for the Management of Gout: Value of Information Analysis

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Jutkowitz E, Alarid-Escudero F, Choi HK, Kuntz KM, Jalal H. Prioritizing Future Research on Allopurinol and Febuxostat for the Management of Gout: Value of Information Analysis. PharmacoEconomics, 2017;35(10):1073-1085

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BibTex

@article{jutkowitz2017prioritizing,
  title={Prioritizing future research on allopurinol and febuxostat for the management of gout: value of information analysis},
  author={Jutkowitz, Eric and Alarid-Escudero, Fernando and Choi, Hyon K and Kuntz, Karen M and Jalal, Hawre},
  journal={Pharmacoeconomics},
  volume={35},
  pages={1073--1085},
  year={2017},
  publisher={Springer}
}

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EndNote

%0 Journal Article
%T Prioritizing future research on allopurinol and febuxostat for the management of gout: value of information analysis
%A Jutkowitz, Eric
%A Alarid-Escudero, Fernando
%A Choi, Hyon K
%A Kuntz, Karen M
%A Jalal, Hawre
%J Pharmacoeconomics
%V 35
%P 1073-1085
%@ 1170-7690
%D 2017
%I Springer

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RefMan

TY  - JOUR
T1  - Prioritizing future research on allopurinol and febuxostat for the management of gout: value of information analysis
A1  - Jutkowitz, Eric
A1  - Alarid-Escudero, Fernando
A1  - Choi, Hyon K
A1  - Kuntz, Karen M
A1  - Jalal, Hawre
JO  - Pharmacoeconomics
VL  - 35
SP  - 1073
EP  - 1085
SN  - 1170-7690
Y1  - 2017
PB  - Springer
ER  - 

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Abstract

 

Objective

Microsimulation models are becoming increasingly common in the field of decision modeling for health.

 

Methods

We used a previously developed Markov model that evaluated the cost effectiveness of nine urate-lowering strategies: no treatment, allopurinol-only fixed dose (300 mg), allopurinol-only dose escalation (up to 800 mg), febuxostat-only fixed dose (80 mg), febuxostat-only dose escalation (up to 120 mg), allopurinol–febuxostat sequential therapy fixed dose, allopurinol–febuxostat sequential therapy dose escalation, febuxostat–allopurinol sequential therapy fixed dose, and febuxostat–allopurinol sequential therapy dose escalation. Each strategy was evaluated over the lifetime of a hypothetical gout patient. We calculated population expected value of perfect information (EVPI). We used a linear regression meta-modeling approach to calculate population expected value of partial perfect information (EVPPI), and a Gaussian approximation to calculate the population expected value of sample information for parameters (EVSI) and the expected net benefit of sampling (ENBS) for four potential study designs: (1) an allopurinol efficacy trial; (2) a febuxostat efficacy trial; (3) a prospective observational study evaluating health utilities; and (4) a comprehensive study evaluating the efficacy of allopurinol and febuxostat and health utilities. A 5-year decision time horizon was used in the base-case analysis.

 

Results

EVPI varied by a decision maker’s willingness-to-pay (WTP) per quality-adjusted life-year (QALY) and was $US900 million for WTP of $US60,000 per QALY. Population EVPPI was highest across all WTP values for study design #4. For study design #4 and a WTP of $US60,000 per QALY, the optimal sample size was 735 patients per study arm.

 

Conclusion

Future studies are needed to evaluate the effectiveness of allopurinol and febuxostat dose escalation.