Multiple Imputation Methods for Treatment Noncompliance and Nonresponse in Randomized Clinical Trials

Summary: Randomized clinical trials are a powerful tool for investigating causal treatment effects, but in human trials there are oftentimes problems of noncompliance which standard analyses, such as the intention-to-treat or as-treated analysis, either ignore or incorporate in such a way that the resulting estimand is no longer a causal effect. One alternative to these analyses is the complier average causal effect (CACE) which estimates the average causal treatment effect among a subpopulation that would comply under any treatment assigned. We focus on the setting of a randomized clinical trial with crossover treatment noncompliance (e.g., control subjects could receive the intervention and intervention subjects could receive the control) and outcome nonresponse. In this article, we develop estimators for the CACE using multiple imputation methods, which have been successfully applied to a wide variety of missing data problems, but have not yet been applied to the potential outcomes setting of causal inference. Using simulated data we investigate the finite sample properties of these estimators as well as of competing procedures in a simple setting. Finally we illustrate our methods using a real randomized encouragement design study on the effectiveness of the influenza vaccine

Assessing the Adequacy of Variance Function in Heteroscedastic Regression Models

Heteroscedastic data arise in many applications. In a heteroscedastic regression model, the variance is often taken as a parametric function of the covariate or the regression mean. This paper presents a kernel-smoothing based nonparametric test for checking the adequacy of such a postulated variance structure. The test does not need to specify a parametric distribution for the random errors. It has an asymptotical normal distribution under the null hypothesis and is powerful against a large class of alternatives. Numerical simulations and an illustrative example are provided

ROC Surfaces in the Presence of Verification Bias

In diagnostic medicine, the Receiver Operating Characteristic (ROC) surface is one of the established tools for assessing the accuracy of a diagnostic test in discriminating three disease states, and the volume under the ROC surface has served as a summary index for diagnostic accuracy. In practice, the selection for definitive disease examination may be based on initial test measurements, and induces verification bias in the assessment. We propose here a nonparametric likelihood-based approach to construct the empirical ROC surface in the presence of differential verification, and to estimate the volume under the ROC surface. Estimators of the standard deviation are derived by both the Fisher\u27s Information and Jack-knife method, and their relative accuracy is evaluated in an extensive simulation study. The methodology is further extended to incorporate discrete baseline covariates in the selection process, and to compare the accuracy of a pair of diagnostic tests. We apply the proposed method to compare the diagnostic accuracy between Mini-Mental State Examination and clinical evaluation of dementia, in discriminating among three disease states of Alzheimer\u27s disease

A local outbreak of dengue caused by an imported case in Dongguan China

<p>Abstract</p> <p>Background</p> <p>Dengue, a mosquito-borne febrile viral disease, is found in tropical and sub-tropical regions around the world. Since the first occurrence of dengue was confirmed in Guangdong, China in 1978, dengue outbreaks have been reported sequentially in different provinces in South China transmitted by^.peridomestic <it>Ae. albopictus </it>mosquitoes, diplaying <it>Ae. aegypti</it>, a fully domestic vector that transmits dengue worldwide. Rapid and uncontrolled urbanization is a characteristic change in developing countries, which impacts greatly on vector habitat, human lifestyle and transmission dynamics on dengue epidemics. In September 2010, an outbreak of dengue was detected in Dongguan, a city in Guangdong province characterized by its fast urbanization. An investigation was initiated to identify the cause, to describe the epidemical characteristics of the outbreak, and to implement control measures to stop the outbreak. This is the first report of dengue outbreak in Dongguan, even though dengue cases were documented before in this city.</p> <p>Methods</p> <p>Epidemiological data were obtained from local Center of Disease Control and prevention (CDC). Laboratory tests such as real-time Reverse Transcription Polymerase Chain Reaction (RT-PCR), the virus cDNA sequencing, and Enzyme-Linked immunosorbent assay (ELISA) were employed to identify the virus infection and molecular phylogenetic analysis was performed with MEGA5. The febrile cases were reported every day by the fever surveillance system. Vector control measures including insecticidal fogging and elimination of habitats of <it>Ae. albopictus </it>were used to control the dengue outbreak.</p> <p>Results</p> <p>The epidemiological studies results showed that this dengue outbreak was initiated by an imported case from Southeast Asia. The outbreak was characterized by 31 cases reported with an attack rate of 50.63 out of a population of 100,000. <it>Ae. albopictus </it>was the only vector species responsible for the outbreak. The virus cDNA sequencing analysis showed that the virus responsible for the outbreak was Dengue Virus serotype-1 (DENV-1).</p> <p>Conclusions</p> <p>Several characterized points of urbanization contributed to this outbreak of dengue in Dongguan: the residents are highly concentrated; the residents' life habits helped to form the habitats of <it>Ae. albopictus </it>and contributed to the high Breteau Index; the self-constructed houses lacks of mosquito prevention facilities. This report has reaffirmed the importance of a surveillance system for infectious diseases control and aroused the awareness of an imported case causing the epidemic of an infectious disease in urbanized region.</p

Global burden of metabolic diseases, 1990-2021

BACKGROUND: Common metabolic diseases, such as type 2 diabetes mellitus (T2DM), hypertension, obesity, hypercholesterolemia, and metabolic dysfunction-associated steatotic liver disease (MASLD), have become a global health burden in the last three decades. The Global Burden of Disease, Injuries, and Risk Factors Study (GBD) data enables the first insights into the trends and burdens of these metabolic diseases from 1990 to 2021, highlighting regional, temporal and differences by sex.METHODS: Global estimates of disability-adjusted life years (DALYs) and deaths from GBD 2021 were analyzed for common metabolic diseases (T2DM, hypertension, obesity, hypercholesterolemia, and MASLD). Age-standardized DALYs (mortality) per 100,000 population and annual percentage change (APC) between 1990 and 2021 were estimated for trend analyses. Estimates are reported with uncertainty intervals (UI).RESULTS: In 2021, among five common metabolic diseases, hypertension had the greatest burden (226 million [95 % UI: 190-259] DALYs), whilst T2DM (75 million [95 % UI: 63-90] DALYs) conferred much greater disability than MASLD (3.67 million [95 % UI: 2.90-4.61]). The highest absolute burden continues to be found in the most populous countries of the world, particularly India, China, and the United States, whilst the highest relative burden was mostly concentrated in Oceania Island states. The burden of these metabolic diseases has continued to increase over the past three decades but has varied in the rate of increase (1.6-fold to 3-fold increase). The burden of T2DM (0.42 % [95 % UI: 0.34-0.51]) and obesity (0.26 % [95 % UI: 0.17-0.34]) has increased at an accelerated rate, while the rate of increase for the burden of hypertension (-0.30 % [95 % UI: -0.34 to -0.25]) and hypercholesterolemia (-0.33 % [95 % UI: -0.37 to -0.30]) is slowing. There is no significant change in MASLD over time (0.05 % [95 % UI: -0.06 to 0.17]).CONCLUSION: In the 21st century, common metabolic diseases are presenting a significant global health challenge. There is a concerning surge in DALYs and mortality associated with these conditions, underscoring the necessity for a coordinated global health initiative to stem the tide of these debilitating diseases and improve population health outcomes worldwide.</p

Genetic effects on gene expression across human tissues

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of diseas

Open X-Embodiment:Robotic learning datasets and RT-X models

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Publisher Copyright: © 2022, The Author(s).Background: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.Peer reviewe

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Abstract Background Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk