Posterior predictive checks to quantify lack-of-fit in admixture models of latent population structure.

Admixture models are a ubiquitous approach to capture latent population structure in genetic samples. Despite the widespread application of admixture models, little thought has been devoted to the quality of the model fit or the accuracy of the estimates of parameters of interest for a particular study. Here we develop methods for validating admixture models based on posterior predictive checks (PPCs), a Bayesian method for assessing the quality of fit of a statistical model to a specific dataset. We develop PPCs for five population-level statistics of interest: within-population genetic variation, background linkage disequilibrium, number of ancestral populations, between-population genetic variation, and the downstream use of admixture parameters to correct for population structure in association studies. Using PPCs, we evaluate the quality of the admixture model fit to four qualitatively different population genetic datasets: the population reference sample (POPRES) European individuals, the HapMap phase 3 individuals, continental Indians, and African American individuals. We found that the same model fitted to different genomic studies resulted in highly study-specific results when evaluated using PPCs, illustrating the utility of PPCs for model-based analyses in large genomic studies.

How We Do Things With Words: Analyzing Text as Social and Cultural Data.

In this article we describe our experiences with computational text analysis involving rich social and cultural concepts. We hope to achieve three primary goals. First, we aim to shed light on thorny issues not always at the forefront of discussions about computational text analysis methods. Second, we hope to provide a set of key questions that can guide work in this area. Our guidance is based on our own experiences and is therefore inherently imperfect. Still, given our diversity of disciplinary backgrounds and research practices, we hope to capture a range of ideas and identify commonalities that resonate for many. This leads to our final goal: to help promote interdisciplinary collaborations. Interdisciplinary insights and partnerships are essential for realizing the full potential of any computational text analysis involving social and cultural concepts, and the more we bridge these divides, the more fruitful we believe our work will be.

Imagined Examples of Painful Experiences Provided by Chronic Low Back Pain Patients and Attributed a Pain Numerical Rating Score.

OBJECTIVE: The pain numerical rating scale (NRS) is widely used in pain research and clinical settings to represent pain intensity. For an individual with chronic pain, NRS reporting requires representation of a complex subjective state as a numeral. To evaluate the process of NRS reporting, this study examined the relationship between reported pain NRS levels and imagined painful events reported by study subjects. DESIGN: A total of 149 subjects with chronic low back pain characterized by the NIH Research Task Force Recommended Minimal Dataset reported current pain NRS and provided imagined examples of painful experiences also attributing to these an NRS. We present a quantitative and qualitative analysis of the 797 pain examples provided by the study subjects. RESULTS: Study subjects tended to be able to imagine both highly painful 10/10 events and non-painful events with relative agreement across subjects. While NRS for the pain examples tended to increase with example severity, for many types of examples there was wide dispersion around the mean pain level. Examination of pain examples indicated unexpected relationships between current pain and the intensity and nature of the imagined painful events. CONCLUSIONS: Our results indicate that the pain NRS does not provide a reliably interpretable assessment of current physical pain intensity for an individual with chronic pain at a specific moment.