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，对重要健康相关属性的受试者进行准确缩放，以及用于评估组间差异（如治疗与对照）和随时间变化的统计学合理程序。项目反应理论（IRT）提供了一个强大的建模框架，实现这些目标，通过测量的潜在属性，只能间接测量的可观察的数据。不幸的是，在日常研究应用中，大量缺乏用户友好的IRT软件，可以直接计算各种IRT模型。该项目的目的是开发灵活，用户友好的IRT软件，特别适合健康科学研究人员。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），经典的多分类模型（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，与健康相关的研究问题高度相关。广义模型能够分析纵向和多水平数据，以及检查治疗组的规模效应。多维模型克服了有时相当严格的假设，需要分析只有一个属性的时间。从技术的角度来看，该计划将提供众多的项目和人的参数，如MML，非参数MML，完全非参数模型，MCMC，贝叶斯EAP，加权似然等的统计估计方法，一旦参数估计，研究人员可以评估模型通过一个大的模型测试和拟合指数。大量的交互式高层次图将允许结果的可定制可视化，XML导出将确保表格和数字的出版质量。用户友好性方面的一个特别重点是使用基于JAVA的图形用户界面（GUI），该界面在各种计算平台上都是一致的。在整个IRT建模工作流程中，研究人员将得到上下文相关对话框的支持。经验丰富的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。该软件包将得到一个综合在线平台（Wiki）的支持，该平台包括技术解释、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.