IRT Software for Health Outcomes and Behavioral Cancer Research

描述（由申请人提供）：健康和行为癌症研究人员需要高度可靠、客观和方法学上建立的测量工具，在重要的健康相关属性上对受试者进行精确的缩放，以及统计上合理的程序来评估组间差异（如治疗组与对照组）和时间变化。项目反应理论（IRT）提供了一个强大的建模框架，通过测量只能通过可观察数据间接测量的潜在属性来实现这些目标。不幸的是，目前大量缺乏用户友好的IRT软件，无法在日常研究应用中直接计算各种IRT模型。该项目的目的是开发灵活的、用户友好的IRT软件，特别适合健康科学的研究人员。该软件将在多个平台上运行，涵盖广泛的IRT模型，如经典的二元模型（Rasch, 1-PL, 2-PL, 3-PL），经典的多体模型（GRM， RSM， PCM， NRM），以及最新的方法，如协变量模型（混合效应模型）和多维模型，这些模型与健康相关的研究问题高度相关。广义模型可以对纵向和多层数据进行分析，并在一定规模上检查治疗组的效果。多维模型克服了有时要求一次只分析一个属性的限制性假设。从技术角度来看，该程序将为项目和人的参数提供许多统计估计方法，如MML，非参数MML，全非参数模型，MCMC，贝叶斯EAP，加权似然等。一旦估计了参数，研究人员就可以通过大量的模型检验和拟合指标来评估模型。许多交互式高级图表将允许对结果进行可定制的可视化，XML导出将确保表格和数字具有出版质量。在用户友好性方面，特别强调的是使用基于JAVA的图形用户界面（GUI），该界面将在各种计算平台上保持一致。在整个IRT建模工作流程中，研究人员将得到上下文敏感对话框的支持。有经验的IRT学者可以选择使用直观的IRT命令语言来完善他们的模型。该软件包将由一个综合的在线平台（Wiki）提供支持，包括技术说明、用户指南、模型和数据示例、新闻部分、讨论板、常见问题部分和其他功能。

DESCRIPTION (provided by applicant): Health and behavioral cancer researchers require highly reliable, objective, and methodologically founded measurement instruments, accurate scaling of subjects on important health-related attributes, and statistically sound procedures for evaluating differences among groups (such as treatment vs control) and change across time. Item response theory (IRT) provides a powerful modeling framework for achieving these goals via the measurement of latent attributes that are only indirectly measured by observable data. Unfortunately, there is a massive lack of user-friendly IRT software that allows for a straightforward computation of a variety of IRT models in daily research applications. The aim of this project is the development of flexible, user-friendly IRT software especially suited for researchers in the health sciences. This software will run on multiple platforms and cover a broad spectrum of IRT models such as classical binary models (Rasch, 1-PL, 2-PL, 3-PL), classical polytomous models (GRM, RSM, PCM, NRM), as well as up-to-date approaches such as models with covariates (mixed-effects models) and multidimensional models that are highly relevant for health related research questions. The generalized models enable analysis of longitudinal and multilevel data, as well as examination of treatment group effects on a scale. Multidimensional models overcome the sometimes rather restrictive assumptions that require analysis of only one attribute at a time. From a technical point of view, the program will offer numerous statistical estimation approaches for item and person parameters such as MML, nonparametric MML, fully nonparametric models, MCMC, Bayesian EAP, weighted likelihood, etc. Once the parameters are estimated, a researcher can evaluate the model by means of a large set of model tests and fit indices. Numerous interactive high- level plots will allow for a customizable visualization of the results, and an XML export will assure that tables and figures are publication quality. A special emphasis in terms of user-friendliness is the use of a JAVA based graphical user interface (GUI) that will be consistent across a variety of computing platforms. Throughout the IRT modeling workflow, a researcher will be supported by context-sensitive dialog boxes. Experienced IRT scholars will have the option to refine their models using an intuitive IRT command language. The software package will be supported by a comprehensive online platform (Wiki) including technical explanations, a user's guide, model and data examples, a news section, a discussion board, a FAQ section, and other features.