A Novel Method for Expediting the Development of Patient Reported Outcome Measures

描述(由申请人提供)：我们建议测试和传播一种创新的有序贝叶斯仪器开发(OBID)方法，该方法无缝地整合专家和参与者的数据，同时使用比经典方法更少的对象，以实现对新仪器开发的有效性证据的一致和经济的估计。延迟将研究证据转化为实践的一个原因是它所需的时间 开发有效和可靠的心理测量工具来测量患者报告的结果。确定足够数量的参与者，特别是在人口较少或资源有限的情况下，往往会延长仪器开发时间。例如，对于人数较少或可用资源有限的人口，开发工具可能需要四年时间，而不是两年。目前公认的仪器验证方法对专家和参与者数据的分析是分开的和连续的，需要大量的受试者。内容专家对条目与结构理论定义匹配程度的评估首先被用来评估内容的有效性。然后，对这些项目进行测试，以获得结构效度证据(例如，通过因素分析获得内部结构)。来自专家数据的信息(内容分析)不用于参与者数据的因素分析(或项目反应理论模型)。相比之下，建议的Obid方法使用了一个基于长期和经验验证的贝叶斯分析的框架，其中专家的数据(先验分布)用参与者的数据(后验分布)进行更新，以有效地实现统一的心理测量模型。在我们使用近似方法的初步研究的基础上，这项拟议研究的具体目标是：1)通过使用模拟数据将有序贝叶斯仪器开发(OBID)的性能(即稳定性和开发时间差)与使用精确估计程序的经典仪器开发进行比较来测试有序贝叶斯仪器开发(OBID)；2)在各种患者和家庭护理人员群体中测试OBID；以及3)传播BID&OBID软件，供其他研究社区的研究人员评估。我们假设OBID和经典仪器开发在大样本量下将同样有效，但当参与者数量较少或资源有限时，OBID将比经典仪器开发更有效。因此，OBID有望成为一种新的、快速的仪器开发方法，为我们目前的测量工具箱添加内容。

DESCRIPTION (provided by applicant): We propose to test and disseminate an innovative Ordinal Bayesian Instrument Development (OBID) method that seamlessly integrates expert and participant data, while using fewer subjects than classical approaches, to achieve a coherent and economical estimate of validity evidence for new instrument development. One contributor to delays in translating research evidence into practice is the amount of time it takes to develop valid and reliable psychometric instruments for measuring patient reported outcomes. Identifying sufficient numbers of participants, particularly when there are small populations or limited resources, often extends instrument development time. For example, for populations where there are small numbers or limited resources available, the development of instruments can take four years instead of two. Current accepted instrument validation methods analyze expert and participant data separately and consecutively, and require a large number of subjects. Content experts' evaluations of the extent to which items match the theoretical definition of the construct are first used to estimate content validity. Following that, the items re tested with participants to garner construct validity evidence (e.g., internal structure through factor analysis). Information from the expert data (content analysis) is not used in a factor analysis (or item response theory model) of the participants' data. In contrast, the proposed OBID method uses a framework grounded in long-standing and empirically verified Bayesian analyses where experts' data (prior distributions) are updated with participants' data (posterior distributions) to efficiently achieve a unified psychometric model. Building on our preliminary studies that used an approximation approach, the specific aims for this proposed study are to: 1) Test Ordinal Bayesian Instrument Development (OBID) by comparing its performance (i.e., stability and development time differences) to classical instrument development with exact estimation procedures, using simulation data; 2) Test OBID across a variety of patient and family caregiver populations; and 3) Disseminate BID & OBID software for evaluation by investigators in other research communities. We hypothesize OBID and classical instrument development will be equally efficient for large sample sizes, but that OBID will be more efficient than classical instrument development when only small numbers of participants or limited resources are available. Thus, OBID promises to be a new and expeditious method for instrument development, adding to our current measurement toolbox.