CooccurrenceAffinity: An R package for computing a novel metric of affinity in co-occurrence data that corrects for pervasive errors in traditional indices.

1. Analysis of co-occurrence data with traditional indices has led to many problems such as sensitivity of the indices to prevalence and the same value representing either a strong positive or strong negative association across different datasets. In our recent study (Mainali et al 2022), we revealed the source of the problems that make the traditional indices fundamentally flawed and unreliable-namely that the indices in common use have no target of estimation quantifying degree of association in the non-null case-and we further developed a novel parameter of association, alpha, with complete formulation of the null distribution for estimating the mechanism of affinity.

Adaptive Gaussian Markov random fields for child mortality estimation.

The under-5 mortality rate (U5MR), a critical health indicator, is typically estimated from household surveys in lower and middle income countries. Spatio-temporal disaggregation of household survey data can lead to highly variable estimates of U5MR, necessitating the usage of smoothing models which borrow information across space and time. The assumptions of common smoothing models may be unrealistic when certain time periods or regions are expected to have shocks in mortality relative to their neighbors, which can lead to oversmoothing of U5MR estimates.

Differential richness inference for 16S rRNA marker gene surveys.

Background: Individual and environmental health outcomes are frequently linked to changes in the diversity of associated microbial communities. Thus, deriving health indicators based on microbiome diversity measures is essential. While microbiome data generated using high-throughput 16S rRNA marker gene surveys are appealing for this purpose, 16S surveys also generate a plethora of spurious microbial taxa.

Variable targeting and reduction in large vector autoregressions with applications to workforce indicators.

We develop statistical tools for time series analysis of large multivariate datasets, when a few core series are of principal interest and there are many potential auxiliary predictive variables. The methodology, based on Vector Autoregressions (VAR), handles the case where unrestricted fitting is precluded by a large number of series and a huge parameter space. In particular, we adopt a forecast error criterion and use Granger-causality tests in a sequential manner to build a VAR model that targets the main variables.

A better index for analysis of co-occurrence and similarity.

Scientists often need to know whether pairs of entities tend to occur together or independently. Standard approaches to this issue use co-occurrence indices such as Jaccard, Sørensen-Dice, and Simpson. We show that these indices are sensitive to the prevalences of the entities they describe and that this invalidates their interpretability.

Small-Area Estimation of Smoke-Free Workplace Policies and Home Rules in US Counties.

Introduction: The workplace and home are sources of exposure to secondhand smoke, a serious health hazard for nonsmoking adults and children. Smoke-free workplace policies and home rules protect nonsmoking individuals from secondhand smoke and help individuals who smoke to quit smoking. However, estimated population coverages of smoke-free workplace policies and home rules are not typically available at small geographic levels such as counties.

Post-randomization for controlling identification risk in releasing microdata from general surveys.

Before releasing survey data, statistical agencies usually perturb the original data to keep each survey unit's information confidential. One significant concern in releasing survey microdata is identity disclosure, which occurs when an intruder correctly identifies the records of a survey unit by matching the values of some key (or pseudo-identifying) variables. We examine a recently developed post-randomization method for a strict control of identification risks in releasing survey microdata.

Comparative Study of Confidence Intervals for Proportions in Complex Sample Surveys.

The most widespread method of computing confidence intervals (CIs) in complex surveys is to add and subtract the margin of error (MOE) from the point estimate, where the MOE is the estimated standard error multiplied by the suitable Gaussian quantile. This Wald-type interval is used by the American Community Survey (ACS), the largest US household sample survey. For inferences on small proportions with moderate sample sizes, this method often results in marked under-coverage and lower CI endpoint less than 0.

Establishing a common instantaneous center of rotation for the metatarso-phalangeal and metatarso-sesamoid joints: a theoretical geometric model based on specific morphometrics.

Background: Previous research has identified separate sagittal plane instantaneous centers of rotation for the metatarso-phalangeal and metatarso-sesamoid joints, but surprisingly, it does not appear that any have integrated the distinctive morphological characteristics of all three joints and their respective axes into a model that collectively unifies their functional motions. Since all joint motion is defined by its centers of rotation, establishing this in a complicated multi-dimensional structure such as the metatarso-phalangeal-sesamoid joint complex is fundamental to understanding its functionality and subsequent structural failures such as hallux abducto valgus and hallux rigidus.

Methods: Based on a hypothesis that it is possible to develop an instantaneous center of rotation common to all four osseous structures, specific morphometrics were selected from a sequential series of 0.

Analysis and correction of compositional bias in sparse sequencing count data.

Background: Count data derived from high-throughput deoxy-ribonucliec acid (DNA) sequencing is frequently used in quantitative molecular assays. Due to properties inherent to the sequencing process, unnormalized count data is compositional, measuring relative and not absolute abundances of the assayed features. This compositional bias confounds inference of absolute abundances.

A pooling strategy to effectively use genotype data in quantitative traits genome-wide association studies.

The goal of quantitative traits genome-wide association studies is to identify associations between a phenotypic variable, such as a vitamin level and genetic variants, often single-nucleotide polymorphisms. When funding limits the number of assays that can be performed to measure the level of the phenotypic variable, a subgroup of subjects is often randomly selected from the genotype database and the level of the phenotypic variable is then measured for each subject. Because only a proportion of the genotype data can be used, such a simple random sampling method may suffer from substantial loss of efficiency, especially when the number of assays is relative small and the frequency of the less common variant (minor allele frequency) is low.

Checking distributional assumptions for pharmacokinetic summary statistics based on simulations with compartmental models.

Bioequivalence (BE) studies are an essential part of the evaluation of generic drugs. The most common in vivo BE study design is the two-period two-treatment crossover design. AUC (area under the concentration-time curve) and Cmax (maximum concentration) are obtained from the observed concentration-time profiles for each subject from each treatment under each sequence.

Statistical inferences from formaldehyde DNA-protein cross-link data: improving methods for characterization of uncertainty.

Physiologically based pharmacokinetic (PBPK) modeling has reached considerable sophistication in its application to pharmacological and environmental health problems. Yet, mature methodologies for making statistical inferences have not been routinely incorporated in these applications except in a few data-rich cases. This paper demonstrates how improved statistical inference on estimated model parameters from both frequentist and Bayesian points of view can be routinely carried out.