Improved two-step testing of genome-wide gene–environment interactions

Two-step tests for gene–environment ($G\times E$) interactions exploit marginal single-nucleotide polymorphism (SNP) effects to improve the power of a genome-wide interaction scan. They combine a screening step based on marginal effects used to “bin” SNPs for weighted hypothesis testing in the second step to deliver greater power over single-step tests while preserving the genome-wide Type I error. However, the presence of many SNPs with detectable marginal effects on the trait of interest can reduce power by “displacing” true interactions with weaker marginal effects and by adding to the number of tests that need to be corrected for multiple testing. We introduce a new significance-based allocation into bins for Step-2 $G\times E$ testing that overcomes the displacement issue and propose a computationally efficient approach to account for multiple testing within bins. Simulation results demonstrate that these simple improvements can provide substantially greater power than current methods under several scenarios. An application to a multistudy collaboration for understanding colorectal cancer reveals a G × Sex interaction located near the SMAD7 gene.     

An improved closed procedure for testing multiple hypotheses

Clinical trials routinely involve multiple hypothesis testing. The closed testing procedure (CTP) is a fundamental principle in testing multiple hypotheses. This article presents an improved CTP in which intersection hypotheses can be tested at a level greater than $\alpha$ such that the control of the familywise error rate at level $\alpha$ remains. Consequently, our method uniformly improves the power of discovering false hypotheses over the original CTP. We illustrate that an improvement by our method exists for many commonly used tests. An empirical study on the effectiveness of a glucose-lowering drug is provided.     

Two-sample test with g-modeling and its applications

Many real data analyses involve two-sample comparisons in location or in distribution. Most existing methods focus on problems where observations are independently and identically distributed in each group. However, in some applications the observed data are not identically distributed but associated with some unobserved parameters which are identically distributed. To address this challenge, we propose a novel two-sample testing procedure as a combination of the $g$-modeling density estimation introduced by Efron and the two-sample Kolmogorov-Smirnov test. We also propose efficient bootstrap algorithms to estimate the statistical significance for such tests. We demonstrate the utility of the proposed approach with two biostatistical applications: the analysis of surgical nodes data with binomial model and differential expression analysis of single-cell RNA sequencing data with zero-inflated Poisson model.     

Group sequential designs for clinical trials with bivariate endpoints

Although all clinical trials are designed and monitored using more than one endpoint, methods are needed to assure that decision criteria are chosen to reflect the clinically relevant tradeoffs that assure the trial's scientific integrity. This article presents a framework for the design and monitoring clinical trials in a bivariate outcome space. The framework uses a rectangular hyperbola to define a bivariate null curve that divides outcome space into regions of benefit and lack of benefit. The curve is shown to be a flexible mapping of bivariate space that allows a continuous tradeoff between the two endpoints in a manner that captures many previous bivariate designs. The curve is extended to a distance function in bivariate space that allows different decisions in each of the four quadrants that comprise bivariate space. The distance function forms a statistic ($\delta$); the distribution of its estimate is derived and used as a basis for trial design and group sequential monitoring plans in bivariate space. A recursive form of the bivariate group sequential density is used to evaluate and control operating characteristics for the proposed design. The bivariate designs are shown to meet or exceed the usual standards for size and power. The proposed design is illustrated in the ongoing NHLBI-sponsored Kids-DOTT multinational randomized controlled trial comparing shortened versus conventional anticoagulation for the treatment of venous thromboembolism in patients less than 21 years of age. The proposed methods are broadly applicable to a wide range of clinical settings and trial designs.     

Properties of the full random-effect modeling approach with missing covariate data

During drug development, a key step is the identification of relevant covariates predicting between-subject variations in drug response. The full random effects model (FREM) is one of the full-covariate approaches used to identify relevant covariates in nonlinear mixed effects models. Here we explore the ability of FREM to handle missing (both missing completely at random (MCAR) and missing at random (MAR)) covariate data and compare it to the full fixed-effects model (FFEM) approach, applied either with complete case analysis or mean imputation. A global health dataset (20 421 children) was used to develop a FREM describing the changes of height for age Z-score (HAZ) over time. Simulated datasets (n = 1000) were generated with variable rates of missing (MCAR) covariate data (0%-90%) and different proportions of missing (MAR) data condition on either observed covariates or predicted HAZ. The three methods were used to re-estimate model and compared in terms of bias and precision which showed that FREM had only minor increases in bias and minor loss of precision at increasing percentages of missing (MCAR) covariate data and performed similarly in the MAR scenarios. Conversely, the FFEM approaches either collapsed at $$70% of missing (MCAR) covariate data (FFEM complete case analysis) or had large bias increases and loss of precision (FFEM with mean imputation). Our results suggest that FREM is an appropriate approach to covariate modeling for datasets with missing (MCAR and MAR) covariate data, such as in global health studies.     

Marine omega-3 fatty acid supplementation in non-alcoholic fatty liver disease: Plasma proteomics in the randomized WELCOME* trial

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Can the interRAI home care instrument be applied to the definition criteria of the Global Leadership Initiative on Malnutrition (GLIM)? A longitudinal study

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Plant-derived natural agents as dietary supplements for the regulation of glycosylated hemoglobin: A review of clinical trials

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Knowledge and attitudes on pediatric vaccinations and intention to vaccinate in a sample of pregnant women from the City of Rome

Background

In recent years, pediatric immunization rates in Italy have decreased well below the recommended thresholds, largely due to an increase in scepticism about the efficacy and safety of vaccines. We aimed to identify the degree of such scepticism, and the factors driving it, among a sample of pregnant women in the City of Rome.

Exploring the Role of Grandparents in the Feeding of Grandchildren Aged 1–5 Years

Objective

Early childhood is a crucial time for the development of eating behaviors and food preferences. With increased labor force participation by Australian mothers of young children, grandparents are acting as the main informal carers of grandchildren. Therefore, grandparents have the capacity to influence the feeding of young children and thus their eating behaviors.

Resveratrol attenuates high-fat diet-induced non-alcoholic steatohepatitis by maintaining gut barrier integrity and inhibiting gut inflammation through regulation of the endocannabinoid system

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Leprosy in the british mandated territory of Togoland

     

20.

Differences in human milk peptide release along the gastrointestinal tract between preterm and term infants     

Robert L. Beverly  Robert K. Huston  Andi M. Markell  Elizabeth A. McCulley  Rachel L. Martin  David C. Dallas 《Clinical nutrition (Edinburgh, Scotland)》2021,40(3):1214-1223

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Admissions	97
Discharged on parole	8
Out patients	26
Died	1 (Phthisis)
Left against advice	6

Differences in human milk peptide release along the gastrointestinal tract between preterm and term infants

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