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: 7361836
• 负责人: DAVID TODEM
• 金额: $ 13.06万
• 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/7361836
• 关键词: Behavior; Biology; Biometry; Cancer Patient; Characteristics; Clinical; Clinical Trials; Collaborations; Complex; Data; Data Adjustments; Data Analyses; Development; Dropout; Effectiveness of Interventions; End Point; Family; Foundations; Goals; Grant; Health; Health Professional; Joints; Knowledge; Malignant Neoplasms; Measurement; Measures; Mental Health; Mentored Research Scientist Development Award; Mentors; Methodology; Methods; Modality; Modeling; Nature; Observational Study; Outcome; Patient Self-Report; Patients; Performance; Population; Process; Psyche structure; Public Health; Randomized; Range; Research; Research Personnel; Sampling; Solid; Techniques; Testing; Time; Training; anticancer research; base; cancer epidemiology; design; improved; simulation

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此类终点的重要性，近年来，与身体和心理健康状况相关的结果的测量已经有所增加。这些终点是与常规临床结果一起测量的，并且在很大程度上取决于患者的自我报告。关键问题是缺少数据，因为患者可能死亡或可能太病了，无法完成研究。信息丢失还代表了除效率的丧失外，还可能对研究结果的有效性构成了很大的威胁。有强有力的证据表明，此类数据并非随机缺失，并且如果不引入偏见，就不能忽略。关于如何处理不完整数据的两种极端观点是（1）完全删除信息不完整的信息，以及（2）构建复杂的关节模型，以进行测量和丢失过程。这些极端的观点被问题所包围，这主要是由于人们对丢失机制必须做出的假设的不可测试的性质。然后需要一种更通用的方法，该方法将不完整数据的处理嵌入灵敏度分析中。开发这种方法学需要广泛了解癌症的生物学和流行病学。 K01机制将通过在开发新的统计方法来处理丢失数据的新统计方法中建立坚实的基础，从而有助于将癌症研究中的指导和正式基础培训与先前的生物统计和人群健康培训相结合。该研究计划将培训和指导整合到研究中，例如基线和时间依赖的特征如何影响癌症患者的功能和精神状态，而数据调整缺失。我们的方法是开发一个具有敏感性参数的非忽视模型家庭，可以由主题专家解释。将在估算和假设测试的背景下使用经典的频繁方法和贝叶斯后后预测检查原理开发和实施对所提出的不可忽视模型的全球灵敏度分析。最后，将使用分析技术和蒙特卡洛模拟研究有关提出方法的中心理论问题。该K01赠款机制的一个关键目标是通过协作，设计复杂的临床试验和癌症研究中的观察性研究来提高我们的帮助，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发现。