Strategies for handling missing data in the development, validation and implementation of clinical risk prediction tools

临床风险预测工具的开发、验证和实施中处理缺失数据的策略

• 批准号: nhmrc : 546059
• 负责人: Prof Andrew Forbes
• 金额: $ 30.12万
• 依托单位: Monash University
• 依托单位国家: 澳大利亚
• 项目类别: NHMRC Project Grants
• 财政年份: 2009
• 资助国家: 澳大利亚
• 起止时间: 2009-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://researchdata.edu.au/strategies-handling-missing-prediction-tools/76871
• 关键词: Strategies; handling; missing; data; development

Tools that predict the future outcome of disease are common. Missing data is a problem in studies that develop and validate such tools and affects their validity because simple approaches to dealing with missing data are biased. We will develop statistical methodology in this area and compare the performance of this and other methodologies. Alongside this methodological work we will re-assess existing prediction tools and develop new tools in the areas of cardiac surgery and kidney disease.

预测疾病未来结果的工具很常见。在开发和验证这些工具并影响其有效性的研究中，数据缺失是一个问题，因为处理缺失数据的简单方法是有偏见的。我们将开发这方面的统计方法，并比较这种方法和其他方法的性能。除了这项方法学工作，我们将重新评估现有的预测工具，并在心脏外科和肾脏疾病领域开发新的工具。