Bayesian Methods for a Longitudinal CAT

描述(由申请者提供)：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。具体地说，我对使用计算机化自适应测试(CAT)来改进对患者报告的健康结果的纵向测量感兴趣，这些结果对癌症预防和癌症控制非常重要，例如抑郁症状。为了准确衡量患者报告的健康结果，自我报告调查往往很长，并造成中等到高度的应答者负担。CAT是一种比大多数当前天平更有效的测量工具，在降低响应负担的同时提高了精度。我建议通过首先改进CAT方法来改进纵向评估，以获得更高的收益，然后开发CAT测量工具，用于纵向测量患者报告的健康结果。 这一职业发展奖将建立在我之前接受的统计科学培训的基础上，并让我在尖端贝叶斯统计方法和患者报告的健康结果研究方面获得进一步的培训。我组建了一个由两名统计学家、一名心理测量学家、一名生活质量研究人员和一名开发计算机化自适应测试(CAT)的领导者组成的指导团队，用于衡量患者报告的健康结果。 我的研究建议重点是开发对当前CAT算法的方法增强，并开发新的算法，以进一步减轻受访者在行为癌症预防和癌症控制研究中对患者报告的健康结果进行纵向评估的负担。我假设，与目前的方法相比，为纵向评估量身定做CAT算法将提高精度并减轻患者负担。我将使用模拟研究来1)比较开发CAT算法的三种不同方法，以及2)开发和比较为纵向评估量身定做的CAT算法。这个职业发展奖的研究和培训将使我成为研究项目和拨款的首席研究员，这些研究项目和拨款继续增强CAT的方法，用于衡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。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，甚至手持计算机。

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.