Based on scientific literature and interviews with clinicians and patients, we developed a quality of life instrument for use with people with MS called the Functional Assessment of Multiple Sclerosis (FAMS).The initial item pool consisted of 88 questions: 28 from the general version of the Functional Assessment of Cancer Therapy quality of life instrument, plus 60 generated by patients, providers, and literature review. The validation samples comprised a mail survey cohort (N = 377) and a clinical cohort (N = 56). Both cohorts provided evidence for internal consistency of the derived subscales, test-retest reliability, content validity, concurrent validity, and construct validity. Principal components and Rasch measurement model analyses were applied sequentially to survey sample data, reducing test length to 44 questions, divided into six subscales: mobility, symptoms, emotional well-being (depression), general contentment, thinking/fatigue, and family/social well-being. Fifteen initially rejected questions were added back as miscellaneous (unscored) questions for their potential clinical and empirical value. The mobility subscale was strongly predictive of the Kurtzke Extended Disability Status Scale and the Scripps Neurologic Rating Scales. The other five subscales were not, indicating they measure aspects of patient quality of life not captured by the neurologic exam. The final 59-item English language instrument (FAMS version 2) is available for inclusion in clinical trials and clinical practice. NEUROLOGY 1996;47: 129-139

The accurate measurement of examinee test performance is critical to educational decision-making, and inaccurate measurement can lead to negative consequences for examinees. Person-fit statistics are important in a psychometric analysis for detecting examinees with aberrant response patterns that lead to inaccurate measurement. Unfortunately, although a large number of person-fit statistics is available, there is little consensus as to which ones are most useful. The purpose of this study was to compare 36 person-fit indices, under different testing conditions, to obtain a better consensus as to their relative merits. The results of these comparisons, and their implications, are discussed.

We are living in the big data era, as current technologies and networks allow for the easy and routine collection of data sets in different disciplines. Bayesian Statistics offers a flexible modeling approach which is attractive for describing the complexity of these datasets. These models often exhibit a likelihood function which is intractable due to the large sample size, high number of parameters, or functional complexity. Approximate Bayesian Computational (ABC) methods provides likelihood-free methods for performing statistical inferences with Bayesian models defined by intractable likelihood functions. The vastity of the literature on ABC methods created a need to review and relate all ABC approaches so that scientists can more readily understand and apply them for their own work. This article provides a unifying review, general representation, and classification of all ABC methods from the view of approximate likelihood theory. This clarifies how ABC methods can be characterized, related, combined, improved, and applied for future research. Possible future research in ABC is then outlined.