ABSTRACT

The original cluster-randomised controlled trial took place in two provinces of Northern Ecuador. The trial compared cash, food or voucher assistance and found them to be effective. The results of the original paper are completely replicable and the findings of the original analysis are robust; however, we additionally find that treatments are more effective in the poorer province. We then extend the cost-effectiveness analysis by taking into account the parameter uncertainty in a probabilistic sensitivity analysis (PSA). Moreover, we provide information about the usefulness of additional research to reduce uncertainty concerning the cost-effectiveness analysis by calculating the expected value of perfect information (EVPI). Our extended cost-effectiveness analysis reveals that for some outcomes decision makers with a relatively small willingness-to-pay maybe a risk to choose a less cost-effective intervention. Furthermore, our findings indicate that further research into the comparative cost-effectiveness of these interventions could be worthwhile. This is in particular true for low-resource settings where decision makers face stricter budget constraints to finance interventions. In particular, policy makers with a small budget have a high risk of choosing a comparably less cost-effective treatment. Hence, further research to reduce uncertainty of effect estimates could aid decision-making.

摘要

最初的集群随机对照试验在北厄瓜多尔的两个省进行。该试验对现金，食品或代金券援助进行了比较，发现它们是有效的。原始论文的结果是完全可复制的，原始分析的结果是可靠的；但是，我们还发现在较贫穷的省份治疗更有效。然后，我们通过在概率敏感性分析（PSA）中考虑参数不确定性来扩展成本效益分析。此外，我们提供了有关其他研究的有用信息，以通过计算理想信息（EVPI）的期望值来减少成本效益分析的不确定性。我们扩展的成本效益分析表明，对于某些结果，付款意愿较小的决策者可能会选择成本效益较低的干预措施。此外，我们的发现表明，进一步研究这些干预措施的相对成本效益可能是值得的。对于资源匮乏的环境尤其如此，决策者面临着更严格的预算约束来资助干预措施。特别是，预算少的决策者有较高的风险选择相对较不经济的治疗方法。因此，为减少效果估计的不确定性而进行的进一步研究可能有助于决策。我们的发现表明，进一步研究这些干预措施的相对成本效益可能是值得的。对于资源匮乏的环境尤其如此，决策者面临着更严格的预算约束来资助干预措施。特别是，预算少的政策制定者有较高的风险选择相对较不划算的治疗方法。因此，为减少效果估计的不确定性而进行的进一步研究可能有助于决策。我们的发现表明，进一步研究这些干预措施的相对成本效益可能是值得的。对于资源匮乏的环境尤其如此，决策者面临着更严格的预算约束来资助干预措施。特别是，预算少的政策制定者有较高的风险选择相对较不划算的治疗方法。因此，为减少效果估计的不确定性而进行的进一步研究可能有助于决策。