A Bayesian semi-parametric scalar-on-function regression with measurement error using instrumental variables.

Wearable devices such as the ActiGraph are now commonly used in research to monitor or track physical activity. This trend corresponds with the growing need to assess the relationships between physical activity and health outcomes, such as obesity, accurately. Device-based physical activity measures are best treated as functions when assessing their associations with scalar-valued outcomes such as body mass index. Scalar-on-function regression (SoFR) is a suitable regression model in this setting. Most estimation approaches in SoFR assume that the measurement error in functional covariates is white noise. Violating this assumption can lead to underestimating model parameters. There are limited approaches to correcting measurement errors for frequentist methods and none for Bayesian methods in this area. We present a non-parametric Bayesian measurement error-corrected SoFR model that relaxes all the constraining assumptions often involved with these models. Our estimation relies on an instrumental variable allowing a time-varying biasing factor, a significant departure from the current generalized method of moment (GMM) approach. Our proposed method also permits model-based grouping of the functional covariate following measurement error correction. This grouping of the measurement error-corrected functional covariate allows additional ease of interpretation of how the different groups differ. Our method is easy to implement, and we demonstrate its finite sample properties in extensive simulations. Finally, we applied our method to data from the National Health and Examination Survey to assess the relationship between wearable device-based measures of physical activity and body mass index in adults in the United States.

Estimation of sparse functional quantile regression with measurement error: a SIMEX approach.

Quantile regression is a semiparametric method for modeling associations between variables. It is most helpful when the covariates have complex relationships with the location, scale, and shape of the outcome distribution. Despite the method's robustness to distributional assumptions and outliers in the outcome, regression quantiles may be biased in the presence of measurement error in the covariates. The impact of function-valued covariates contaminated with heteroscedastic error has not yet been examined previously; although, studies have investigated the case of scalar-valued covariates. We present a two-stage strategy to consistently fit linear quantile regression models with a function-valued covariate that may be measured with error. In the first stage, an instrumental variable is used to estimate the covariance matrix associated with the measurement error. In the second stage, simulation extrapolation (SIMEX) is used to correct for measurement error in the function-valued covariate. Point-wise standard errors are estimated by means of nonparametric bootstrap. We present simulation studies to assess the robustness of the measurement error corrected for functional quantile regression. Our methods are applied to National Health and Examination Survey data to assess the relationship between physical activity and body mass index among adults in the United States.

Toward more rigorous and informative nutritional epidemiology: The rational space between dismissal and defense of the status quo.

To date, nutritional epidemiology has relied heavily on relatively weak methods including simple observational designs and substandard measurements. Despite low internal validity and other sources of bias, claims of causality are made commonly in this literature. Nutritional epidemiology investigations can be improved through greater scientific rigor and adherence to scientific reporting commensurate with research methods used. Some commentators advocate jettisoning nutritional epidemiology entirely, perhaps believing improvements are impossible. Still others support only normative refinements. But neither abolition nor minor tweaks are appropriate. Nutritional epidemiology, in its present state, offers utility, yet also needs marked, reformational renovation. Changing the status quo will require ongoing, unflinching scrutiny of research questions, practices, and reporting-and a willingness to admit that "good enough" is no longer good enough. As such, a workshop entitled "Toward more rigorous and informative nutritional epidemiology: the rational space between dismissal and defense of the status quo" was held from July 15 to August 14, 2020. This virtual symposium focused on: (1) Stronger Designs, (2) Stronger Measurement, (3) Stronger Analyses, and (4) Stronger Execution and Reporting. Participants from several leading academic institutions explored existing, evolving, and new better practices, tools, and techniques to collaboratively advance specific recommendations for strengthening nutritional epidemiology.

PARTIALLY FUNCTIONAL LINEAR QUANTILE REGRESSION WITH MEASUREMENT ERRORS.

Ignoring measurement errors in conventional regression analyses can lead to biased estimation and inference results. Reducing such bias is challenging when the error-prone covariate is a functional curve. In this paper, we propose a new corrected loss function for a partially functional linear quantile model with function-valued measurement errors. We establish the asymptotic properties of both the functional coefficient and the parametric coefficient estimators. We also demonstrate the finite-sample performance of the proposed method using simulation studies, and illustrate its advantages by applying it to data from a children obesity study.

A function-based approach to model the measurement error in wearable devices.

Physical activity (PA) is an important risk factor for many health outcomes. Wearable-devices such as accelerometers are increasingly used in biomedical studies to understand the associations between PA and health outcomes. Statistical analyses involving accelerometer data are challenging due to the following three characteristics: (i) high-dimensionality, (ii) temporal dependence, and (iii) measurement error. To address these challenges we treat accelerometer-based measures of PA as a single function-valued covariate prone to measurement error. Specifically, in order to determine the relationship between PA and a health outcome of interest, we propose a regression model with a functional covariate that accounts for measurement error. Using regression calibration, we develop a two-step estimation method for the model parameters and establish their consistency. A test is also proposed to test the significance of the estimated model parameters. Simulation studies are conducted to compare the proposed methods with existing alternative approaches under varying scenarios. Finally, the developed methods are used to assess the relationship between PA intensity and BMI obtained from the National Health and Nutrition Examination Survey data.

Errors in the implementation, analysis, and reporting of randomization within obesity and nutrition research: a guide to their avoidance.

Randomization is an important tool used to establish causal inferences in studies designed to further our understanding of questions related to obesity and nutrition. To take advantage of the inferences afforded by randomization, scientific standards must be upheld during the planning, execution, analysis, and reporting of such studies. We discuss ten errors in randomized experiments from real-world examples from the literature and outline best practices for their avoidance. These ten errors include: representing nonrandom allocation as random, failing to adequately conceal allocation, not accounting for changing allocation ratios, replacing subjects in nonrandom ways, failing to account for non-independence, drawing inferences by comparing statistical significance from within-group comparisons instead of between-groups, pooling data and breaking the randomized design, failing to account for missing data, failing to report sufficient information to understand study methods, and failing to frame the causal question as testing the randomized assignment per se. We hope that these examples will aid researchers, reviewers, journal editors, and other readers to endeavor to a high standard of scientific rigor in randomized experiments within obesity and nutrition research.

Chlamydia trachomatis Is Associated With Medically Indicated Preterm Birth and Preeclampsia in Young Pregnant Women.

BACKGROUND: Studies on Chlamydia trachomatis-associated pregnancy outcomes are largely conflicting, ignoring the heterogeneous natures of pregnancy complications and potential effect modification by maternal age. This study determined if prenatal C. trachomatis infection is associated with preterm birth (PTB) and preeclampsia subtypes. METHODS: A retrospective cohort study was conducted using 22,772 singleton pregnancies with a prenatal C. trachomatis diagnostic test. Spontaneous and medically indicated PTBs, and term and preterm preeclampsia were outcomes. Modified Poisson regression calculated relative risk (RR) and 95% confidence intervals (CI) with propensity score adjustments stratified by maternal ages <25 and ≥25 years. RESULTS: Overall, C. trachomatis was significantly associated with term preeclampsia (adjusted RR [RRadj], 1.88; 95% CI, 1.38-2.57). Among young women (age <25 years), C. trachomatis was significantly associated with medically indicated PTB (RRadj, 2.29; 95% CI, 1.38-3.78) and term preeclampsia (RRadj, 1.57; 95% CI, 1.05-2.36) in propensity-adjusted models. No significant associations in older women were detected. CONCLUSION: C. trachomatis was associated with medically indicated PTB and term preeclampsia in young women. Associations between chlamydia and perinatal outcomes may depend on the subtype of PTB and preeclampsia, which should be investigated through mechanistic studies.

Instrumental variable approach to estimating the scalar-on-function regression model with measurement error with application to energy expenditure assessment in childhood obesity.

Wearable device technology allows continuous monitoring of biological markers and thereby enables study of time-dependent relationships. For example, in this paper, we are interested in the impact of daily energy expenditure over a period of time on subsequent progression toward obesity among children. Data from these devices appear as either sparsely or densely observed functional data and methods of functional regression are often used for their statistical analyses. We study the scalar-on-function regression model with imprecisely measured values of the predictor function. In this setting, we have a scalar-valued response and a function-valued covariate that are both collected at a single time period. We propose a generalized method of moments-based approach for estimation, while an instrumental variable belonging in the same time space as the imprecisely measured covariate is used for model identification. Additionally, no distributional assumptions regarding the measurement errors are assumed, while complex covariance structures are allowed for the measurement errors in the implementation of our proposed methods. We demonstrate that our proposed estimator is L2 consistent and enjoys the optimal rate of convergence for univariate nonparametric functions. In a simulation study, we illustrate that ignoring measurement error leads to biased estimations of the functional coefficient. The simulation studies also confirm our ability to consistently estimate the function-valued coefficient when compared to approaches that ignore potential measurement errors. Our proposed methods are applied to our motivating example to assess the impact of baseline levels of energy expenditure on body mass index among elementary school-aged children.

Safety of dietary supplementation with arginine in adult humans.

Previous studies with animals and humans have shown beneficial effects of dietary supplementation with L-arginine (Arg) on reducing white fat and improving health. At present, a long-term safe level of Arg administration to adult humans is unknown. The objective of this study was to conduct a randomized, placebo-controlled, clinical trial to evaluate the safety and tolerability of oral Arg in overweight or obese but otherwise healthy adults with a body mass index of ≥ 25 kg/m2. A total of 142 subjects completed a 7-day wash-in period using a 12 g Arg/day dose. All the remaining eligible 101 subjects who tolerated the wash-in dose (45 men and 56 women) were assigned randomly to ingest 0, 15 or 30 g Arg (as pharmaceutical-grade Arg-HCl) per day for 90 days. Arg was taken daily in at least two divided doses by mixing with a flavored beverage. At Days 0 and 90, blood pressures of study subjects were recorded, their physical examinations were performed, and their blood and 24-h urine samples were obtained to measure: (1) serum concentrations of amino acids, glucose, fatty acids, and related metabolites; and (2) renal, hepatic, endocrine and metabolic parameters. Our results indicate that the serum concentration of Arg in men or women increased (P < 0.05) progressively with increasing oral Arg doses from 0 to 30 g/day. Dietary supplementation with 30 g Arg/day reduced (P < 0.05) systolic blood pressure and serum glucose concentration in females, as well as serum concentrations of free fatty acids in both males and females. Based on physiological and biochemical variables, study subjects tolerated oral administration of 15 and 30 g Arg/day without adverse events. We conclude that a long-term safe level of dietary Arg supplementation is at least 30 g/day in adult humans.

Functional multiple indicators, multiple causes measurement error models.

Objective measures of oxygen consumption and carbon dioxide production by mammals are used to predict their energy expenditure. Since energy expenditure is not directly observable, it can be viewed as a latent construct with multiple physical indirect measures such as respiratory quotient, volumetric oxygen consumption, and volumetric carbon dioxide production. Metabolic rate is defined as the rate at which metabolism occurs in the body. Metabolic rate is also not directly observable. However, heat is produced as a result of metabolic processes within the body. Therefore, metabolic rate can be approximated by heat production plus some errors. While energy expenditure and metabolic rates are correlated, they are not equivalent. Energy expenditure results from physical function, while metabolism can occur within the body without the occurrence of physical activities. In this manuscript, we present a novel approach for studying the relationship between metabolic rate and indicators of energy expenditure. We do so by extending our previous work on MIMIC ME models to allow responses that are sparsely observed functional data, defining the sparse functional multiple indicators, multiple cause measurement error (FMIMIC ME) models. The mean curves in our proposed methodology are modeled using basis splines. A novel approach for estimating the variance of the classical measurement error based on functional principal components is presented. The model parameters are estimated using the EM algorithm and a discussion of the model's identifiability is provided. We show that the defined model is not a trivial extension of longitudinal or functional data methods, due to the presence of the latent construct. Results from its application to data collected on Zucker diabetic fatty rats are provided. Simulation results investigating the properties of our approach are also presented.

Urban air pollution, poverty, violence and health--Neurological and immunological aspects as mediating factors.

Rapid rural-urban migration has created overcrowded areas characterized by concentrated poverty and increases in indoor and outdoor air pollutants. These "hotspots" constitute an increased risk of violence and disease outbreaks. We hypothesize that the effects of poverty and associated air pollution-related stress on impaired cognitive skills are mediated by inflammatory cytokines. A research framework is proposed, encompassing (i) an epidemiological investigation of associations between poverty, high concentrations of air pollutants, violence and health, (ii) a longitudinal follow-up of working memory capacities and inflammatory markers, and (iii) intervention programs aiming to strengthen employability and decreased exposures to toxic air pollutants.

Multiple indicators, multiple causes measurement error models.

Multiple indicators, multiple causes (MIMIC) models are often employed by researchers studying the effects of an unobservable latent variable on a set of outcomes, when causes of the latent variable are observed. There are times, however, when the causes of the latent variable are not observed because measurements of the causal variable are contaminated by measurement error. The objectives of this paper are as follows: (i) to develop a novel model by extending the classical linear MIMIC model to allow both Berkson and classical measurement errors, defining the MIMIC measurement error (MIMIC ME) model; (ii) to develop likelihood-based estimation methods for the MIMIC ME model; and (iii) to apply the newly defined MIMIC ME model to atomic bomb survivor data to study the impact of dyslipidemia and radiation dose on the physical manifestations of dyslipidemia. As a by-product of our work, we also obtain a data-driven estimate of the variance of the classical measurement error associated with an estimate of the amount of radiation dose received by atomic bomb survivors at the time of their exposure.

Food insecurity and mental health among US adults during the COVID-19 pandemic: Results from National Health Interview Survey, 2020-2021.

OBJECTIVE: To examine socio-demographic disparities in food insecurity during the COVID-19 pandemic and the association between food insecurity and mental health among US adults overall and communities vulnerable to food insecurity. METHODS: A cross-sectional study was conducted using the 2020-2021 National Health Interview Survey of 57,456 US adults. Weighted multivariable logistic regression models were used to estimate the association between food insecurity and anxiety or depression symptoms in overall US adults and subgroups including young adults (18-34 years), females, Hispanic people, non-Hispanic Black people, individuals with prior COVID-19 infection, the unemployed, low-income participants, participants with children, and Supplemental Nutrition Assistance Program (SNAP) participants. RESULTS: Young or middle age, female sex, Hispanic/non-Hispanic Black/other race/ethnicity, lower education level, unmarried/other marital status, unemployment, being below the federal poverty level, and greater number of persons in the household were associated with food insecurity (AOR ranged from 1.35 to 2.70, all p < 0.05). Food insecurity was independently associated with anxiety (AOR = 2.67, 99 % CI: 2.33, 3.06) or depression (AOR = 3.04, 99 % CI: 2.60, 3.55) symptoms in the overall adults. Significant associations between food insecurity and anxiety or depression symptoms were also observed in all subgroups (AOR ranged from 1.95 to 3.28, all p < 0.0001). Compared with overall adults, the magnitude of the association was greater for participants with children, females (for depression only), and non-Hispanic Black people (for depression only). LIMITATIONS: The cross-sectional design prevents inference of causality. CONCLUSIONS: Comprehensive policies are needed to ensure accessible and affordable food resources to reduce disparities in food insecurity and improve mental health, especially for those socioeconomically disadvantaged communities.

A Cross-Sectional Study on the Association of Walnut Consumption with Obesity and Relative Fat Mass among United States Adolescents and Young Adults in NHANES (2003-2020).

Background: Walnuts contain nutrients and phytochemicals that can promote metabolic health. However, the high energy content of walnuts along with other nuts raises the concern that consuming nuts promotes obesity. Objectives: We sought to investigate the associations between consumption of walnuts as well as other nuts and measures of obesity in adolescents and young adults. Methods: This study included 8874 adolescents (12-19 y) and 10,323 young adults (20-39 y) from 8 waves of National Health and Nutrition Examination Survey data (2003-2020). The associations of consumption of 1) walnuts only (WO); 2) walnuts with other nuts (WON); 3) other nuts (ON); and 4) no nuts (NN) with obesity status and relative fat mass (RFM) were assessed using logistic and linear regressions stratified by age group and sex. Sample weights were used in all statistical analyses. Results: The mean daily intake of walnuts was not different between the 2 walnut consumption groups within each age group (adolescents: 2.18 [standard error (SE) 0.14] g; P = 0.917; young adults: 4.23 [0.37] g; P = 0.682). The WON group had the lowest prevalence of obesity (adolescents: 8.3%; young adults: 21.1%) while the NN group had the highest prevalence (adolescents: 24.1%; young adults: 35.4%). The models indicated lower odds of obesity in adolescent girls (odds ratio [OR]: 0.27; P < 0.05) and young adult women (OR: 0.58; P < 0.05) who consumed WON than in those who consumed NN. In both young women and girls, RFM was significantly lower in the WON and ON groups than the NN group (P < 0.001). In young men, WON consumption was also associated with a lower RFM (OR: -1.24; 95% confidence interval: -2.21, -0.28) compared with NN consumption. Conclusions: For adolescents girls and young women, dietary intake of walnuts combined with other nuts has the strongest inverse association with measures of obesity.

Recommendations on statistical approaches to account for dose uncertainties in radiation epidemiologic risk models.

PURPOSE: Epidemiological studies of stochastic radiation health effects such as cancer, meant to estimate risks of the adverse effects as a function of radiation dose, depend largely on estimates of the radiation doses received by the exposed group under study. Those estimates are based on dosimetry that always has uncertainty, which often can be quite substantial. Studies that do not incorporate statistical methods to correct for dosimetric uncertainty may produce biased estimates of risk and incorrect confidence bounds on those estimates. This paper reviews commonly used statistical methods to correct radiation risk regressions for dosimetric uncertainty, with emphasis on some newer methods. We begin by describing the types of dose uncertainty that may occur, including those in which an uncertain value is shared by part or all of a cohort, and then demonstrate how these sources of uncertainty arise in radiation dosimetry. We briefly describe the effects of different types of dosimetric uncertainty on risk estimates, followed by a description of each method of adjusting for the uncertainty. CONCLUSIONS: Each of the method has strengths and weaknesses, and some methods have limited applicability. We describe the types of uncertainty to which each method can be applied and its pros and cons. Finally, we provide summary recommendations and touch briefly on suggestions for further research.

Misstatements, misperceptions, and mistakes in controlling for covariates in observational research.

We discuss 12 misperceptions, misstatements, or mistakes concerning the use of covariates in observational or nonrandomized research. Additionally, we offer advice to help investigators, editors, reviewers, and readers make more informed decisions about conducting and interpreting research where the influence of covariates may be at issue. We primarily address misperceptions in the context of statistical management of the covariates through various forms of modeling, although we also emphasize design and model or variable selection. Other approaches to addressing the effects of covariates, including matching, have logical extensions from what we discuss here but are not dwelled upon heavily. The misperceptions, misstatements, or mistakes we discuss include accurate representation of covariates, effects of measurement error, overreliance on covariate categorization, underestimation of power loss when controlling for covariates, misinterpretation of significance in statistical models, and misconceptions about confounding variables, selecting on a collider, and p value interpretations in covariate-inclusive analyses. This condensed overview serves to correct common errors and improve research quality in general and in nutrition research specifically.

Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data.

MOTIVATION: Protein abundance in quantitative proteomics is often based on observed spectral features derived from liquid chromatography mass spectrometry (LC-MS) or LC-MS/MS experiments. Peak intensities are largely non-normal in distribution. Furthermore, LC-MS-based proteomics data frequently have large proportions of missing peak intensities due to censoring mechanisms on low-abundance spectral features. Recognizing that the observed peak intensities detected with the LC-MS method are all positive, skewed and often left-censored, we propose using survival methodology to carry out differential expression analysis of proteins. Various standard statistical techniques including non-parametric tests such as the Kolmogorov-Smirnov and Wilcoxon-Mann-Whitney rank sum tests, and the parametric survival model and accelerated failure time-model with log-normal, log-logistic and Weibull distributions were used to detect any differentially expressed proteins. The statistical operating characteristics of each method are explored using both real and simulated datasets. RESULTS: Survival methods generally have greater statistical power than standard differential expression methods when the proportion of missing protein level data is 5% or more. In particular, the AFT models we consider consistently achieve greater statistical power than standard testing procedures, with the discrepancy widening with increasing missingness in the proportions. AVAILABILITY: The testing procedures discussed in this article can all be performed using readily available software such as R. The R codes are provided as supplemental materials. CONTACT: ctekwe@stat.tamu.edu.

Dietary supplementation with monosodium glutamate is safe and improves growth performance in postweaning pigs.

Dietary intake of glutamate by postweaning pigs is markedly reduced due to low feed consumption. This study was conducted to determine the safety and efficacy of dietary supplementation with monosodium glutamate (MSG) in postweaning pigs. Piglets were weaned at 21 days of age to a corn and soybean meal-based diet supplemented with 0, 0.5, 1, 2, and 4 % MSG (n = 25/group). MSG was added to the basal diet at the expense of cornstarch. At 42 days of age (21 days after weaning), blood samples (10 mL) were obtained from the jugular vein of 25 pigs/group at 1 and 4 h after feeding for hematological and clinical chemistry tests; thereafter, pigs (n = 6/group) were euthanized to obtain tissues for histopathological examinations. Feed intake was not affected by dietary supplementation with 0-2 % MSG and was 15 % lower in pigs supplemented with 4 % MSG compared with the 0 % MSG group. Compared with the control, dietary supplementation with 1, 2 and 4 % MSG dose-dependently increased plasma concentrations of glutamate, glutamine, and other amino acids (including lysine, methionine, phenylalanine and leucine), daily weight gain, and feed efficiency in postweaning pigs. At day 7 postweaning, dietary supplementation with 1-4 % MSG also increased jejunal villus height, DNA content, and antioxidative capacity. The MSG supplementation dose-dependently reduced the incidence of diarrhea during the first week after weaning. All variables in standard hematology and clinical chemistry tests, as well as gross and microscopic structures, did not differ among the five groups of pigs. These results indicate that dietary supplementation with up to 4 % MSG is safe and improves growth performance in postweaning pigs.

Parametric and Semiparametric Approaches to Analyzing Device-Based Measures of Energy Expenditure in Zucker Diabetic Fatty Rats.

BACKGROUND: Obesity results from a chronic imbalance between energy intake and energy expenditure. Total energy expenditure for all physiological functions combined can be measured approximately by calorimeters. These devices assess energy expenditure frequently (e.g., in 60-second epochs), resulting in massive complex data that are nonlinear functions of time. To reduce the prevalence of obesity, researchers often design targeted therapeutic interventions to increase daily energy expenditure. METHODS: We analyzed previously collected data on the effects of oral interferon tau supplementation on energy expenditure, as assessed with indirect calorimeters, in an animal model for obesity and type 2 diabetes (Zucker diabetic fatty rats). In our statistical analyses, we compared parametric polynomial mixed effects models and more flexible semiparametric models involving spline regression. RESULTS: We found no effect of interferon tau dose (0 vs. 4 µg/kg body weight/day) on energy expenditure. The B-spline semiparametric model of untransformed energy expenditure with a quadratic term for time performed best in terms of the Akaike information criterion value. CONCLUSIONS: To analyze the effects of interventions on energy expenditure assessed with devices that collect data at frequent intervals, we recommend first summarizing the high dimensional data into epochs of 30 to 60 minutes to reduce noise. We also recommend flexible modeling approaches to account for the nonlinear patterns in such high dimensional functional data. We provide freely available R codes in GitHub.