The road not taken: transferability issues in multinational trials

National regulatory agencies often have to use cost-effectiveness (CE) data from multinational randomized controlled trials (RCTs) for national decision making on reimbursement of new drugs. We need to make the best use of these patient-level data to obtain estimates of country-specific CE. Several methods, ranging from simple to statistically complex, have existed for years. We investigated which of these methods are used to estimate CE ratios in economic evaluations performed alongside recent, multinational RCTs that enrolled at least 500 patients.

In this systematic literature review, studies were classified based on whether resource use, unit costs, health outcomes and utility value sets were obtained from all countries, a subset of countries or one country. We recorded if the study presented trial-wide and country-specific CE results and reported the statistical analyses that were used to estimate them.

We included 21 studies, of which the majority used measurements of health care utilization and health outcomes from all countries to estimate CE. Thirteen studies used a one-country valuation of health care utilization; six used a multi-country valuation. Despite the availability of country-specific utility value sets, none of the studies that presented quality-adjusted life-years (QALYs) used multi-country valuation. Valuation of health care utilization and health outcomes was not always consistent within a study: three studies combined a multi-country valuation of health care utilization, with a one-country valuation of health outcomes. Most studies calculated trial-wide CE estimates, while 11 studies calculated country- or region-specific estimates. Thirteen studies used relatively simple methods, which do not take the possible interaction between the country and treatment effect on health care utilization and health outcomes into account. Eight studies used more advanced statistical methods. Three of them used a fixed-effects modeling approach. Five studies explicitly took the hierarchical structure of the data into account, which leads to more appropriate estimates of population average results and associated standard errors. In this way, they help improve transferability of the published results.

Based on this systematic review, we concluded that the uptake of more advanced statistical methods has been relatively slow, while simpler naïve methods are still routinely employed.