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: 7933397
• 负责人: DAVID TODEM
• 金额: $ 10万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2012-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7933397
• 关键词: Behavior; Biology; Biometry; Cancer Patient; Characteristics; Clinical; Clinical Trials; Collaborations; Complex; Data; Data Adjustments; Data Analyses; Development; Dropout; Family; Foundations; Goals; Grant; 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; Process; Public Health; Randomized; Research; Research Personnel; Sampling; Solid; Techniques; Testing; Time; Training; anticancer research; cancer epidemiology; design; improved; intervention effect; mental state; population based; population health; 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这些终点的重要性，在基于人群的癌症研究中，对与身体和精神健康状态质量相关的结果的衡量有所增加。这些终点是与传统的临床结果一起衡量的，并且在很大程度上依赖于患者的自我报告。一个关键问题是缺少数据，因为患者可能会死亡或病情太重，无法完成这项研究。这种信息的丢失，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，这样的数据不是随机缺失的，不可能在不引入偏见的情况下被忽视。关于如何处理不完全数据的两种极端观点是(1)完全删除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。这些极端的观点充满了问题，很大程度上是因为人们不得不对缺失机制做出的假设具有不可检验的性质。然后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。发展这样的方法论需要广泛的生物学和癌症流行病学知识。K01机制将通过在开发处理缺失数据的新统计方法方面建立坚实的基础，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相结合。该研究计划整合了培训和指导，以研究基线和时间依赖特征如何在缺失数据调整的情况下随着时间的推移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，这些模型具有主题专家可以解释的灵敏度参数。在估计和假设检验的背景下，将使用经典的频域方法和贝叶斯后验预测检验原理来开发和实施对所提出的不可忽略模型的全局灵敏度分析。最后，将使用解析技术和蒙特卡罗模拟来研究与所提出的方法有关的核心理论问题。K01资助机制的一个关键目标是提高我们的能力，通过合作，在癌症研究中设计复杂的临床试验和观察性研究，分析产生的数据，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。