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，该方法无缝集成了专家和参与者数据，同时使用比经典方法更少的受试者，以实现对新仪器开发有效性证据的一致和经济估计。将研究证据转化为实践的延迟的一个原因是它所花费的时间 开发有效和可靠的心理测量工具，用于测量患者报告的结果。确定足够数量的参与者，特别是当人口较少或资源有限时，通常会延长仪器开发时间。例如，对于人口较少或可用资源有限的人口，制定文书可能需要四年而不是两年。 目前公认的仪器验证方法分别和连续地分析专家和参与者数据，并且需要大量的受试者。内容专家对项目与结构的理论定义相匹配的程度的评估首先用于估计内容有效性。在此之后，这些项目与参与者一起重新测试，以获得结构效度证据（例如，通过因子分析的内部结构）。来自专家数据（内容分析）的信息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，所提出的OBID方法使用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）与参与者的数据（后验分布）更新，以有效地实现统一的心理测量模型。 基于我们使用近似方法的初步研究，这项拟议研究的具体目标是：1）通过比较其性能（即，稳定性和开发时间差异）到经典仪器开发与精确估计程序，使用模拟数据; 2）在各种患者和家庭护理人员人群中测试OBID;和3）传播BID和OBID软件，供其他研究社区的研究人员评估。我们假设OBID和经典仪器开发对于大样本量同样有效，但是当只有少量参与者或有限的资源可用时，OBID将比经典仪器开发更有效。因此，OBID有望成为仪器开发的一种新的快速方法，增加我们目前的测量工具箱。

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.