Embedded Multilevel Regression and Poststratification: Model-based Inference with Incomplete Auxiliary Information

Health disparity research often evaluates health outcomes across demographic subgroups. Multilevel regression and poststratification (MRP) is a popular approach for small subgroup estimation due to its ability to stabilize estimates by fitting multilevel models and to adjust for selection bias by poststratifying on auxiliary variables, which are population characteristics predictive of the analytic outcome. However, the granularity and quality of the estimates produced by MRP are limited by the availability of the auxiliary variables' joint distribution; data analysts often only have access to the marginal distributions. To overcome this limitation, we embed the estimation of population cell counts needed for poststratification into the MRP workflow: embedded MRP (EMRP). Under EMRP, we generate synthetic populations of the auxiliary variables before implementing MRP. All sources of estimation uncertainty are propagated with a fully Bayesian framework. Through simulation studies, we compare different methods and demonstrate EMRP's improvements over alternatives on the bias-variance tradeoff to yield valid subpopulation inferences of interest. As an illustration, we apply EMRP to the Longitudinal Survey of Wellbeing and estimate food insecurity prevalence among vulnerable groups in New York City. We find that all EMRP estimators can correct for the bias in classical MRP while maintaining lower standard errors and narrower confidence intervals than directly imputing with the WFPBB and design-based estimates. Performances from the EMRP estimators do not differ substantially from each other, though we would generally recommend the WFPBB-MRP for its consistently high coverage rates

Handling Attrition in Longitudinal Studies: The Case for Refreshment Samples

Panel studies typically suffer from attrition, which reduces sample size and can result in biased inferences. It is impossible to know whether or not the attrition causes bias from the observed panel data alone. Refreshment samples - new, randomly sampled respondents given the questionnaire at the same time as a subsequent wave of the panel - offer information that can be used to diagnose and adjust for bias due to attrition. We review and bolster the case for the use of refreshment samples in panel studies. We include examples of both a fully Bayesian approach for analyzing the concatenated panel and refreshment data, and a multiple imputation approach for analyzing only the original panel. For the latter, we document a positive bias in the usual multiple imputation variance estimator. We present models appropriate for three waves and two refreshment samples, including nonterminal attrition. We illustrate the three-wave analysis using the 2007-2008 Associated Press-Yahoo! News Election Poll.Comment: Published in at http://dx.doi.org/10.1214/13-STS414 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Fully Synthetic Data for Complex Surveys

When seeking to release public use files for confidential data, statistical agencies can generate fully synthetic data. We propose an approach for making fully synthetic data from surveys collected with complex sampling designs. Specifically, we generate pseudo-populations by applying the weighted finite population Bayesian bootstrap to account for survey weights, take simple random samples from those pseudo-populations, estimate synthesis models using these simple random samples, and release simulated data drawn from the models as the public use files. We use the framework of multiple imputation to enable variance estimation using two data generation strategies. In the first, we generate multiple data sets from each simple random sample, whereas in the second, we generate a single synthetic data set from each simple random sample. We present multiple imputation combining rules for each setting. We illustrate each approach and the repeated sampling properties of the combining rules using simulation studies

A Case Study of Nonresponse Bias Analysis

Nonresponse bias is a widely prevalent problem for data collections. We develop a ten-step exemplar to guide nonresponse bias analysis (NRBA) in cross-sectional studies, and apply these steps to the Early Childhood Longitudinal Study, Kindergarten Class of 2010-11. A key step is the construction of indices of nonresponse bias based on proxy pattern-mixture models for survey variables of interest. A novel feature is to characterize the strength of evidence about nonresponse bias contained in these indices, based on the strength of relationship of the characteristics in the nonresponse adjustment with the key survey variables. Our NRBA incorporates missing at random and missing not at random mechanisms, and all analyses can be done straightforwardly with standard statistical software

Statistical Graphics for Survey Weights

Survey weights are used for correcting known differences between the sample and the population due to sampling design, nonresponse, undercoverage, and other factors. However, practical considerations often result in weights that are not constructed in a systematic fashion. Graphical methods can be useful in understanding complex survey weights and their relations with other variables in the dataset, particularly when little to no information on the construction of the weights is available. Graphical tools can also assist in diagnostics, including detection of outliers and extreme weights. We apply our methods to the Fragile Families and Child Wellbeing Study, an ongoing longitudinal survey.Los pesos de muestreo se utilizan para corregir las diferencias conocidas entre la muestra y la población debido al diseño muestral, la falta de respuesta, subcobertura, y otros factores. Sin embargo, consideraciones prácticas a menudo resultan en pesos que no se han construido de una manera sistemática. Los métodos gráficos pueden ser útiles en la comprensión de ponderaciones complejas de la encuesta y sus relaciones con otras variables del conjunto de datos, sobre todo cuando se dispone de poca información sobre la construcción de los pesos. Las herramientas gráficas también pueden ayudar en el diagnóstico, incluyendo la detección de valores atípicos y pesos extremos. Aplicamos nuestros métodos en el estudio de Familias Frágiles y Bienestar Infantil, un estudio longitudinal en curso

Inhibitory Effect and Mechanism of Lactiplantibacillus plantarum HB13-2 on Candida albicans

This study explored the inhibitory effect and mechanism of the culture supernatant of Lactiplantibacillus plantarum HB13-2 on Candida albicans. The minimum inhibitory concentration was determined by the double dilution method. Then, the fluorescent dye calcofluor white (CFW) was used to stain the cell wall and observe it. The results showed that the supernatant enhanced the fluorescence intensity and damaged the cell wall. Flow cytometry and fluorescence microscopy showed that the supernatant changed the membrane permeability of C. albicans. The transmembrane potential was detected using the fluorescent probe DiSC3(5), and it was found that the fluorescence intensity was enhanced, indicating that the supernatant caused dissipation of the transmembrane potential. Through microstructural observation by scanning electron microscopy (SEM), it was found that the supernatant of Lactobacillus plantarum HB13-2 caused cellular deformation and leakage of intracellular contents. As detected by fluorescence staining with 2’,7’-dichlorodihydrofluorescin diacetate (DCFH-DA) and Rhodamine-123, the supernatant resulted in accumulation of a large amount of intracellular reactive oxygen species (ROS) and increased mitochondrial membrane potential. In conclusion, the supernatant of L. plantarum HB13-2 can deform cells by destroying the cell wall and membrane and lead to mitochondrial damage, thereby inhibiting C. albicans. This study will provide a scientific basis for the development of L. plantarum HB13-2 as an oral probiotic