Analytical approach:
This economic evaluation was based on a Markov model with a 10-year time horizon. The authors stated that the perspective of the UK National Health Service (NHS) was adopted.
Effectiveness data:
The clinical data came from a selection of known, relevant studies. The evidence for valsartan came from a multi-country, double-blind, randomised controlled trial; the Valsartan in Acute Myocardial Infarction (VALIANT) trial. This compared valsartan with captopril (an ACE inhibitor) in more than 14,000 patients, who had experience one MI and were followed-up for an average of 24.7 months. The data for placebo were obtained from a meta-analysis of three trials. Some assumptions were also needed as no head-to-head comparisons between valsartan and placebo were available. The key clinical endpoints were the event rates for subsequent MIs, strokes, or cardiovascular deaths.
Monetary benefit and utility valuations:
The utility valuations were derived from published studies. One of these studies used the time trade-off approach and another was a meta-analysis of pooled data on quality of life in stroke patients.
Measure of benefit:
Quality-adjusted life-years (QALYs) and life-years (LYs) were the summary benefit measures and were discounted at an annual rate of 3.5%.
Cost data:
The economic analysis included the following costs: valsartan, treatment of cardiovascular events, visits to health care professionals (general practitioners, cardiologists, and nurses), investigations (exercise tolerance test and angiography), and revascularisations (percutaneous coronary intervention and coronary artery bypass grafting). These costs were presented as macro-categories and were mainly calculated using NHS official sources and a published study. Some assumptions were made, based on expert opinions, to supplement the published data on resource consumption. All costs were in UK pounds sterling (£) for the price year 2008. Future costs were discounted at an annual rate of 3.5%.
Analysis of uncertainty:
A deterministic one-way sensitivity analysis was undertaken to identify the most influential model inputs by varying the base-case values by ±20% for most parameters. A probabilistic sensitivity analysis was carried out using probability distributions for the model inputs to generate confidence intervals (CIs) and cost-effectiveness acceptability curves.