Developing appropriate methods for handling missing data in health economic evaluation.

世界各地的决策者使用卫生经济学评估来帮助决定提供哪些卫生保健干预措施。经济评价研究面临的一个重要问题是可能会有数据缺失。然而，大多数已发表的研究忽略了这一点，或使用不适当的方法来解决这个问题。如果缺失的数据没有得到适当的处理，这可能会导致误导性的成本效益结果和稀缺的卫生保健资源被错误分配。在卫生经济学评价中，需要仔细的分析方法来解决不同情况下的数据缺失问题。 我建议进行一项全面的研究计划，以解决这一知识差距，同时使用模拟工作和数据，从临床领域的高政策相关性。该研究将开发和测试解决缺失数据的替代方法，并显示更好的方法在评估卫生干预措施和卫生保健提供者方面的影响。将向应用研究人员和决策者全面传播各种方法，以改进目前的做法。这项研究有助于提高用于为决策提供信息的成本效益研究的质量，从而有助于确保以最佳方式分配稀缺资源，改善人口健康。

Policy makers worldwide use health economic evaluation to help decide which health care interventions to provide. An important concern faced by economic evaluation studies is that there may be missing data. However, most published studies ignore this or use inappropriate methods to address the problem. If missing data are not addressed appropriately this can lead to misleading cost-effectiveness results and scarce health care resources being misallocated. Careful analytical methods are required to address missing data across different circumstances in health economic evaluation. I propose to conduct a comprehensive programme of research to address this gap in knowledge, using both simulation work and data from clinical areas of high policy relevance. The research will develop and test alternative methods for addressing missing data and show the impact that better methods can have in the evaluation of health interventions and health care providers. Methods will be thoroughly disseminated to applied researchers and policy makers to improve current practice. By helping improve the quality of cost-effectiveness studies which are used to inform policy making, this research will help ensure that scarce resources are allocated in the best ways for improving the population's health.