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

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果测量越来越多。这些终点是与常规临床结果一起测量的，并且大部分依赖于患者的自我报告。一个关键的问题是缺少数据，因为患者可能会死亡或病情太重而无法完成研究。这种信息的损失，除了效率的损失外，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，这些数据不是随机丢失的，不能在不引入偏见的情况下被忽视。关于如何处理不完整数据，有两种极端的观点：(1)将信息不完整的案例全部删除；(2)为测量过程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，很大程度上是由于人们必须对缺失机制做出的假设的不可测试性。因此需要一种更通用的方法，将不完整数据的处理嵌入到敏感性分析中。发展这样一种方法需要广泛的生物学和癌症流行病学知识。K01机制将在开发处理缺失数据的新统计方法的坚实基础上，帮助将癌症研究方面的指导和正式基础培训与生物统计学和人口健康方面的先前培训结合起来。该研究计划将培训和指导相结合，研究基线和时间依赖特征在缺失数据调整的情况下如何跨时间影响癌症患者的功能和精神状态。我们的方法是开发一系列不可忽略的模型，这些模型具有可由主题专家解释的灵敏度参数。本文将利用经典的频率方法和贝叶斯后验预测检验原理，在估计和假设检验的背景下，对所提出的不可忽略模型进行全局敏感性分析。最后，关于所提出的方法的核心理论问题将使用分析技术和蒙特卡罗模拟进行研究。这个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的复杂临床试验和观察性研究，分析生成的数据，同时调整退出和缺失的数据，并向公共卫生专业人员和公众解释研究结果。

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.