Bayesian Methods for a Longitudinal CAT

描述（由申请人提供）：我的目标是成为一名专注于行为科学应用的定量方法学家。具体来说，我感兴趣的是使用计算机自适应测试（CAT）来改善对癌症预防和癌症控制（如抑郁症状）重要的患者报告健康结果的纵向测量。为了精确测量患者报告的健康结果，自我报告调查往往很长，并造成中度到高度的受访者负担。CAT是一种比目前大多数秤更有效的测量工具，在提高精度的同时减少了响应负担。我建议改善纵向评估，首先改善CAT方法，以实现更高的收益，然后开发CAT测量工具，用于纵向测量患者报告的健康结果。 这个职业发展奖将建立在我以前在统计科学的培训，并给我进一步的培训，在尖端贝叶斯统计方法和病人报告的健康结果研究。我组建了一个由两名统计学家、一名心理测量师、一名生活质量研究员和一名开发计算机化适应性测试（CAT）的领导者组成的指导团队，该测试用于测量患者报告的健康结果。 我的研究建议侧重于开发方法增强目前的CAT算法，并开发新的算法，以进一步减少在行为癌症预防和癌症控制研究中患者报告的健康结果的纵向评估应答负担。我假设，定制CAT算法的纵向评估将提高精度，并减少患者的负担相对于目前的方法。我将使用模拟研究1）比较开发CAT算法的三种不同方法，2）开发和比较适合纵向评估的CAT算法。这项职业发展奖的研究和培训将使我成为研究项目和赠款的主要研究者，这些项目和赠款继续在CAT中进行方法改进，用于测量行为癌症预防和控制研究中患者报告的健康结果，并将此工具带入临床用于研究目的。在这项研究中开发的方法将适用于广泛的计算机硬件设备，如台式机，笔记本电脑，甚至掌上电脑。

DESCRIPTION (provided by applicant): My goal is to be a quantitative methodologist focusing in behavioral science applications. Specifically I am interested in using computerized adaptive testing (CAT) to improve longitudinal measurement of patient reported health outcomes important in cancer prevention and cancer control, such as depressive symptoms. In order to get precise measurement of patient-reported health outcomes, self-report surveys tend to be long and create a moderate-to-high level of respondent burden. CAT is a more efficient measurement tool than most current scales and reduces response burden while simultaneously increasing precision. I propose to improve longitudinal assessment by first improving CAT methodology to achieve even higher gains, and then to develop CAT measurement instruments for longitudinal measurement of patient-reported health outcomes. This career development award will build upon my previous training in statistical science and give me further training in cutting edge Bayesian statistical methods and patient reported health outcomes research. I have assembled a mentoring team of two statisticians, a psychometrician, a quality of life researcher, and one of the leaders in developing computerized adaptive tests (CAT) for measuring patient reported health outcomes. My research proposal focuses on developing methodological enhancements to current CAT algorithms, and developing new algorithms to further reduce respondent burden in longitudinal assessments of patient reported health outcomes in behavioral cancer prevention and cancer control studies. I hypothesize that tailoring CAT algorithms to longitudinal assessment will improve precision and reduce patient burden relative to current methods. I will use simulation studies to 1) compare three different methods for developing a CAT algorithm, and 2) to develop and compare CAT algorithms tailored to longitudinal assessment. The research and training in this career development award will prepare me to be a principal investigator on research projects and grants that continue methodological enhancements in CAT used to measure patient reported health outcomes in behavioral cancer prevention and control studies, and to bring this tool into the clinic for research purposes. The methodology developed in this study will be applicable to a wide range of computer hardware devices, such as desktops, laptops, and even handheld computers.