Adjusting for Non-ignorable Missing Data in Population-Based Cancer Research

调整基于人群的癌症研究中不可忽略的缺失数据

• 批准号: 7884285
• 负责人: DAVID TODEM
• 金额: $ 12.61万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-07 至 2013-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7884285
• 关键词: Affect; Age; Behavior; Biology; Biometry; Cancer Patient; Characteristics; Chronic Disease; Clinical; Clinical Trials; Clinical Trials Design; Cognitive Therapy; Collaborations; Complex; Data; Data Adjustments; Data Analyses; Development; Dimensions; Dropout; Family; Fatigue; Foundations; Funding; Goals; Grant; Health Professional; Intervention; Intervention Trial; Joints; Knowledge; Location; Longitudinal Studies; Malignant Neoplasms; Measurement; Measures; Mental Health; Mentored Research Scientist Development Award; Mentors; Methodology; Methods; Michigan; Modality; Modeling; Nature; Newly Diagnosed; Observational Study; Outcome; Pain; Patient Self-Report; Patients; Performance; Process; Property; Public Health; Quality of life; Randomized; Research; Research Personnel; Sampling; Severities; Site; Solid; Symptoms; Techniques; Testing; Time; Training; Universities; anticancer research; cancer epidemiology; chemotherapy; cohort; design; improved; intervention effect; mental state; population based; population health; randomized trial; response; simulation; statistics

DESCRIPTION (provided by applicant): Measurement of outcomes related to the quality of physical and mental health states in population-based cancer studies has increased in recent years as more and more researchers realize the importance of such endpoints. These endpoints are measured alongside conventional clinical outcomes and for the most part rely on patient self- report. A key problem has been missing data as patients may die or may be too sick to complete the study. This loss of information represents, besides the loss of efficiency, a potentially large threat to validity of the study results. There is strong evidence that such data are not missing at random, and cannot be ignored without introducing bias. Two extreme views on how to deal with incomplete data are (1) to delete cases with incomplete information altogether and (2) to construct complicated joint models for the measurement and missingness processes. These extreme views are surrounded with problems, largely due to the untestable nature of the assumptions one has to make regarding the missingness mechanism. A more versatile methodology that embeds the treatment of incomplete data within a sensitivity analysis is then required. Developing such a methodology necessitates extensive knowledge of biology and epidemiology of cancer. The K01 mechanism will help integrate mentoring and formal basic training in cancer research with prior training in Biostatistics and Population Health by building on a solid foundation in the development of new statistical methodologies for handling missing data. The research plan integrates training and mentoring to study, for example, how baseline and time dependent characteristics impact cancer patients' functional and mental states across time with missing data adjustment. Our approach is to develop a family of non ignorable models with sensitivity parameters that can be interpretable by subject matter experts. A global sensitivity analysis for the proposed non-ignorable models will be developed and implemented in the context of estimation and hypothesis testing using the classical frequentist approach and the Bayesian posterior predictive check principle. And finally, central theoretical questions about the proposed methods will be investigated using both analytic techniques and Monte Carlo simulations. A key goal of this K01 grant mechanism is to improve our ability to help, through collaborations, design complex clinical trials and observational studies in cancer research, analyze the generated data while adjusting for dropouts and missing data, and interpret the findings to public health professionals and the public.

描述（由申请人提供）：近年来，随着越来越多的研究人员意识到这些终点的重要性，基于人群的癌症研究中与身心健康状态质量相关的结局测量有所增加。这些终点与常规临床结局一起测量，并且在很大程度上依赖于患者自我报告。一个关键的问题是数据缺失，因为患者可能会死亡或病情过重而无法完成研究。这种信息的损失除了效率的损失之外，还对研究结果的有效性构成了潜在的巨大威胁。有强有力的证据表明，这些数据不是随机缺失的，不能忽视而不引入偏见。关于如何处理不完全数据的两种极端观点是（1）完全删除具有不完全信息的情况和（2）为测量和缺失过程构建复杂的联合模型。这些极端的观点被问题所包围，主要是由于人们必须对缺失机制做出的假设的不可检验性。然后需要一种更通用的方法，将不完整数据的处理嵌入敏感性分析中。开发这样的方法需要广泛的癌症生物学和流行病学知识。K01机制将有助于将癌症研究方面的指导和正式基本培训与先前在生物统计和人口健康方面的培训结合起来，为开发处理缺失数据的新统计方法奠定坚实的基础。该研究计划整合了培训和指导，以研究例如基线和时间依赖性特征如何在缺失数据调整的情况下影响癌症患者的功能和精神状态。我们的方法是开发一个家庭的非线性模型的敏感性参数，可以解释的主题专家。将使用经典频率论方法和贝叶斯后验预测检查原则，在估计和假设检验的背景下，对拟议的不可验证模型进行全球敏感性分析。最后，中心的理论问题，所提出的方法将使用分析技术和蒙特卡洛模拟。K01资助机制的一个关键目标是提高我们的能力，通过合作帮助设计癌症研究中复杂的临床试验和观察性研究，分析生成的数据，同时调整辍学和缺失数据，并向公共卫生专业人员和公众解释研究结果。