Advancing PROMIS’s methodology: results of the Third Patient-Reported Outcomes Measurement Information System (PROMIS ® ) Psychometric Summit

In 2002, the NIH launched the ‘Roadmap for Medical Research’. The Patient-Reported Outcomes Measurement Information System (PROMIS®) is one of the Roadmap’s key aspects. To create the next generation of patient-reported outcome measures, PROMIS utilizes item response theory (IRT) and computerized adaptive testing. In 2009, the NIH funded the second wave of PROMIS studies (PROMIS II). PROMIS II studies continue PROMIS’s agenda, but also include new features, including longitudinal analyses and more sociodemographically diverse samples. PROMIS II also includes increased emphasis on pediatric populations and evaluation of PROMIS item banks for clinical research and population science. These aspects bring new psychometric challenges. To address this, investigators associated with PROMIS gathered at the Third Psychometric Summit in September 2010 to identify, describe and discuss pressing psychometric issues and new developments in the field, as well as make analytic recommendations for PROMIS. The summit addressed five general themes: linking, differential item functioning, dimensionality, IRT models for longitudinal applications and new IRT software. In this article, we review the discussions and presentations that occurred at the Third PROMIS Psychometric Summit

Bayesian Modeling For Multivariate Mixed Outcomes With Applications To Cognitive Testing Data

Thesis (Ph.D.)--University of Washington, 2012This dissertation studies parametric and semiparametric approaches to latent variable models, multivariate regression and model-based clustering for mixed outcomes. We use the term mixed outcomes to refer to binary, ordered categorical, count, continuous and other ordered outcomes in combination. Such data structures are common in social, behavioral, and medical sciences. We first review existing parametric approaches to mixed outcomes in latent variable models before developing extensions to accommodate outcome types specific to cognitive testing data. We subsequently develop two new regression approaches for mixed outcome data, the semiparametric Bayesian latent variable model and the semiparametric reduced rank multivariate regression model. In contrast to the existing parametric approaches, these models allow us to avoid specification of distributions for each outcome type. We apply the latent variable and multivariate regression models to investigate the association between cognitive outcomes and MRI-measured regional brain volumes using data from a study of dementia and compare results from the different models. Finally, we develop a new semiparametric correlated partial membership model for model-based clustering of mixed outcome data that also allows us to avoid specification of outcome distributions. We demonstrate our semiparametric approach to model-based clustering on NBA player data from the 2010-2011 season as well as on cognitive testing data from a study of dementia

Social Machines

Gefördert im Rahmen des Projekts DEA