Dealing with Reflection Invariance in Bayesian Factor Analysis.

This paper considers the reflection unidentifiability problem in confirmatory factor analysis (CFA) and the associated implications for Bayesian estimation. We note a direct analogy between the multimodality in CFA models that is due to all possible column sign changes in the matrix of loadings and the multimodality in finite mixture models that is due to all possible relabelings of the mixture components. Drawing on this analogy, we derive and present a simple approach for dealing with reflection in variance in Bayesian factor analysis. We recommend fitting Bayesian factor analysis models without rotational constraints on the loadings-allowing Markov chain Monte Carlo algorithms to explore the full posterior distribution-and then using a relabeling algorithm to pick a factor solution that corresponds to one mode. We demonstrate our approach on the case of a bifactor model; however, the relabeling algorithm is straightforward to generalize for handling multimodalities due to sign invariance in the likelihood in other factor analysis models.

Development and assessment of a composite score for memory in the Alzheimer's Disease Neuroimaging Initiative (ADNI).

We sought to develop and evaluate a composite memory score from the neuropsychological battery used in the Alzheimer's Disease (AD) Neuroimaging Initiative (ADNI). We used modern psychometric approaches to analyze longitudinal Rey Auditory Verbal Learning Test (RAVLT, 2 versions), AD Assessment Schedule - Cognition (ADAS-Cog, 3 versions), Mini-Mental State Examination (MMSE), and Logical Memory data to develop ADNI-Mem, a composite memory score. We compared RAVLT and ADAS-Cog versions, and compared ADNI-Mem to RAVLT recall sum scores, four ADAS-Cog-derived scores, the MMSE, and the Clinical Dementia Rating Sum of Boxes. We evaluated rates of decline in normal cognition, mild cognitive impairment (MCI), and AD, ability to predict conversion from MCI to AD, strength of association with selected imaging parameters, and ability to differentiate rates of decline between participants with and without AD cerebrospinal fluid (CSF) signatures. The second version of the RAVLT was harder than the first. The ADAS-Cog versions were of similar difficulty. ADNI-Mem was slightly better at detecting change than total RAVLT recall scores. It was as good as or better than all of the other scores at predicting conversion from MCI to AD. It was associated with all our selected imaging parameters for people with MCI and AD. Participants with MCI with an AD CSF signature had somewhat more rapid decline than did those without. This paper illustrates appropriate methods for addressing the different versions of word lists, and demonstrates the additional power to be gleaned with a psychometrically sound composite memory score.

A composite score for executive functioning, validated in Alzheimer's Disease Neuroimaging Initiative (ADNI) participants with baseline mild cognitive impairment.

The Alzheimer's Disease Neuroimaging Initiative (ADNI) measures abilities broadly related to executive function (EF), including WAIS-R Digit Symbol Substitution, Digit Span Backwards, Trails A and B, Category Fluency, and Clock Drawing. This study investigates whether a composite executive function measure based on these multiple indicators has better psychometric characteristics than the widely used individual components. We applied item response theory methods to 800 ADNI participants to derive an EF composite score (ADNI-EF) from the above measures. We then compared ADNI-EF with component measures in 390 longitudinally-followed participants with mild cognitive impairment (MCI) with respect to: (1) Ability to detect change over time; (2) Ability to predict conversion to dementia; (3) Strength of cross-sectional association with MRI-derived measures of structures involved in frontal systems, and (4) Strength of baseline association with cerebrospinal fluid (CSF) levels of amyloid ß1₋42, total tau, and phosphorylated tau(181P). ADNI-EF showed the greatest change over time, followed closely by Category Fluency. ADNI-EF needed a 40 % smaller sample size to detect change. ADNI-EF was the strongest predictor of AD conversion. ADNI-EF was the only measure significantly associated with all the MRI regions, though other measures were more strongly associated in a few of the regions. ADNI-EF was associated with all the CSF measures. ADNI-EF appears to be a useful composite measure of EF in MCI, as good as or better than any of its composite parts. This study demonstrates an approach to developing a psychometrically sophisticated composite score from commonly-used tests.

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.