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.

Creatinine clearance is maintained in a range of wet-bulb globe temperatures and work-rest ratios during simulated occupational heat stress.

We tested the hypothesis that compliance with the National Institute for Occupational Safety and Health (NIOSH) heat stress recommendations will prevent reductions in glomerular filtration rate (GFR) across a range of wet bulb globe temperatures (WBGTs) and work-rest ratios at a fixed work intensity. We also tested the hypothesis that non-compliance would result in a reduction in GFR compared to a work-rest matched compliant trial. Twelve healthy adults completed five trials (four NIOSH compliant, one non-compliant) that consisted of four hours of exposure to a range of WBGTs. Subjects walked on a treadmill (Hprod: ~430 W) and work-rest ratios (work per hour: 60, 45, 30, 15 min) were prescribed as a function of WBGT (24°C, 26.5°C, 28.5°C, 30°C, 36°C), and subjects drank a sport drink ad libitum. Peak core temperature (TC) and percentage change in body weight (%DBW) were measured. Creatinine clearance measured pre- and postexposure provided a primary marker of GFR. Peak TC did not differ among NIOSH compliant trials (p=0.065) but differed between compliant vs. non-compliant trials (p<0.001). %DBW did not differ among NIOSH compliant trials (p=0.131) or between compliant vs. non-compliant trials (p=0.185). Creatinine clearance did not change or differ among compliant trials (p³0.079). Creatinine clearance did not change or differ between compliant vs. non-compliant trials (p³0.661). Compliance with the NIOSH recommendations maintained GFR. Surprisingly, despite a greater heat strain in a non-compliant trial, GFR was maintained highlighting the potential relative importance of hydration.

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.

Design, analysis, and interpretation of treatment response heterogeneity in personalized nutrition and obesity treatment research.

It is increasingly assumed that there is no one-size-fits-all approach to dietary recommendations for the management and treatment of chronic diseases such as obesity. This phenomenon that not all individuals respond uniformly to a given treatment has become an area of research interest given the rise of personalized and precision medicine. To conduct, interpret, and disseminate this research rigorously and with scientific accuracy, however, requires an understanding of treatment response heterogeneity. Here, we define treatment response heterogeneity as it relates to clinical trials, provide statistical guidance for measuring treatment response heterogeneity, and highlight study designs that can quantify treatment response heterogeneity in nutrition and obesity research. Our goal is to educate nutrition and obesity researchers in how to correctly identify and consider treatment response heterogeneity when analyzing data and interpreting results, leading to rigorous and accurate advancements in the field of personalized medicine.

Causal models and causal modelling in obesity: foundations, methods and evidence.

Discussing causes in science, if we are to do so in a way that is sensible, begins at the root. All too often, we jump to discussing specific postulated causes but do not first consider what we mean by, for example, causes of obesity or how we discern whether something is a cause. In this paper, we address what we mean by a cause, discuss what might and might not constitute a reasonable causal model in the abstract, speculate about what the causal structure of obesity might be like overall and the types of things we should be looking for, and finally, delve into methods for evaluating postulated causes and estimating causal effects. We offer the view that different meanings of the concept of causal factors in obesity research are regularly being conflated, leading to confusion, unclear thinking and sometimes nonsense. We emphasize the idea of different kinds of studies for evaluating various aspects of causal effects and discuss experimental methods, assumptions and evaluations. We use analogies from other areas of research to express the plausibility that only inelegant solutions will be truly informative. Finally, we offer comments on some specific postulated causal factors. This article is part of a discussion meeting issue 'Causes of obesity: theories, conjectures and evidence (Part II)'.

Correcting calculation and data errors reveals that the original conclusions were incorrect in "The best drug supplement for obesity treatment: a systematic review and network meta-analysis".

The goal of this study was to reproduce and evaluate the reliability of the network meta-analysis performed in the article "The best drug supplement for obesity treatment: A systematic review and network meta-analysis" by Salari et al. In recent years, it has become more common to employ network meta-analysis to assess the relative efficacy of treatments often used in clinical practice. To duplicate Salari et al.'s research, we pulled data directly from the original trials and used Cohen's D to determine the effect size for each treatment. We reanalyzed the data since we discovered significant differences between the data we retrieved and the data given by Salari et al. We present new effect size estimates for each therapy and conclude that the prior findings were somewhat erroneous. Our findings highlight the importance of ensuring the accuracy of network meta-analyses to determine the quality and strength of existing evidence.

Factors associated with choice of behavioural weight loss program by adults with obesity.

We assessed the preference for two behavioural weight loss programs, Diabetes Prevention Program (DPP) and Healthy Weight for Living (HWL) in adults with obesity. A cross-sectional survey was fielded on the Amazon Mechanical Turk. Eligibility criteria included reporting BMI ≥30 and at least two chronic health conditions. Participants read about the programs, selected their preferred program, and answered follow-up questions. The estimated probability of choosing either program was not significantly different from .5 (N = 1005, 50.8% DPP and 49.2% HWL, p = .61). Participants' expectations about adherence, weight loss magnitude, and dropout likelihood were associated with their choice (p < .0001). Non-White participants (p = .040) and those with monthly income greater than $4999 (p = .002) were less likely to choose DPP. Participants who had postgraduate education (p = .007), did not report high serum cholesterol (p = .028), and reported not having tried losing weight before (p = .025) were more likely to choose DPP. Those who chose HWL were marginally more likely to report that being offered two different programs rather than one would likely affect their decision to enrol in one of the two (p = .052). The enrolment into DPP and HWL was balanced, but race, educational attainment, income, previous attempt to lose weight, and serum cholesterol levels had significant associations with the choice of weight loss program.

Bias in nutrition-health associations is not eliminated by excluding extreme reporters in empirical or simulation studies.

Self-reported nutrition intake (NI) data are prone to reporting bias that may induce bias in estimands in nutrition studies; however, they are used anyway due to high feasibility. We examined whether applying Goldberg cutoffs to remove 'implausible' self-reported NI could reliably reduce bias compared to biomarkers for energy, sodium, potassium, and protein. Using the Interactive Diet and Activity Tracking in the American Association of Retired Persons (IDATA) data, significant bias in mean NI was removed with Goldberg cutoffs (120 among 303 participants excluded). Associations between NI and health outcomes (weight, waist circumference, heart rate, systolic/diastolic blood pressure, and VO2 max) were estimated, but sample size was insufficient to evaluate bias reductions. We therefore simulated data based on IDATA. Significant bias in simulated associations using self-reported NI was reduced but not completely eliminated by Goldberg cutoffs in 14 of 24 nutrition-outcome pairs; bias was not reduced for the remaining 10 cases. Also, 95% coverage probabilities were improved by applying Goldberg cutoffs in most cases but underperformed compared with biomarker data. Although Goldberg cutoffs may achieve bias elimination in estimating mean NI, bias in estimates of associations between NI and outcomes will not necessarily be reduced or eliminated after application of Goldberg cutoffs. Whether one uses Goldberg cutoffs should therefore be decided based on research purposes and not general rules.

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.

Service Utilization among Persons Who Inject Drugs Attending a Syringe Exchange Program in New York State.

BACKGROUND: Epidemiologic studies commonly recommend the integration of harm reduction programs with health and social services to improve the well-being of persons who inject drugs (PWIDs). This study identified service utilization clusters for PWIDs attending a syringe exchange program (SEP) in 2017 to better understand in-house service usage. METHODS: We applied Multiple Correspondence Analysis and Hierarchical Clustering on Principal Components to classify 475 PWIDs into clusters using anonymized, SEP records data from New York. Multinomial logistic regression was used to identify sociodemographic and program engagement correlates of cluster membership. RESULTS: Only 22% of participants utilized at least one service. We identified three clusters of service utilization defined by 1) Nonuse; 2) Support, Primary Care, & Maintenance service use; and 3) HIV/STD, Support, Primary Care, & Maintenance service use. Cluster 2 members were less likely to be living alone compared to Cluster 1 (AOR = 0.08, 95% CI: 0.04, 0.17) while Cluster 3 members were less likely to be White (AOR = 0.19, 95% CI: 0.07, 0.50) or living alone (AOR = 0.16, 95% CI: 0.06, 0.44) and more likely to be Medicaid recipients (AOR = 2.89, 95% CI: 1.01, 8.36) compared to Cluster 1. Greater than one SEP interaction, lower syringe return ratios, and being a long-term client increased the odds of service utilization. DISCUSSION: Overall, PWID clients had a low prevalence of in-house service use particularly those who live alone. However, higher service utilization was observed among more vulnerable populations (i.e., non-White and LGBT). Future research is needed to profile services used outside of the SEP.

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.

Evaluation of the type I error rate when using parametric bootstrap analysis of a cluster randomized controlled trial with binary outcomes and a small number of clusters.

BACKGROUND: Cluster randomized controlled trials (cRCTs) are increasingly used but must be analyzed carefully. We conducted a simulation study to evaluate the validity of a parametric bootstrap (PB) approach with respect to the empirical type I error rate for a cRCT with binary outcomes and a small number of clusters. METHODS: We simulated a case study with a binary (0/1) outcome, four clusters, and 100 subjects per cluster. To compare the validity of the test with respect to error rate, we simulated the same experiment with K=10, 20, and 30 clusters, each with 2,000 simulated datasets. To test the null hypothesis, we used a generalized linear mixed model including a random intercept for clusters and obtained p-values based on likelihood ratio tests (LRTs) using the parametric bootstrap method as implemented in the R package "pbkrtest". RESULTS: The PB test produced error rates of 9.1%, 5.5%, 4.9%, and 5.0% on average across all ICC values for K=4, K=10, K=20, and K=30, respectively. The error rates were higher, ranging from 9.1% to 36.5% for K=4, in the models with singular fits (i.e., ignoring clustering) because the ICC was estimated to be zero. CONCLUSION: Using the parametric bootstrap for cRCTs with a small number of clusters results in inflated error rates and is not valid.

Randomization, design and analysis for interdependency in aging research: no person or mouse is an island.

Investigators traditionally use randomized designs and corresponding analysis procedures to make causal inferences about the effects of interventions, assuming independence between an individual's outcome and treatment assignment and the outcomes of other individuals in the study. Often, such independence may not hold. We provide examples of interdependency in model organism studies and human trials and group effects in aging research and then discuss methodologic issues and solutions. We group methodologic issues as they pertain to (1) single-stage individually randomized trials; (2) cluster-randomized controlled trials; (3) pseudo-cluster-randomized trials; (4) individually randomized group treatment; and (5) two-stage randomized designs. Although we present possible strategies for design and analysis to improve the rigor, accuracy and reproducibility of the science, we also acknowledge real-world constraints. Consequences of nonadherence, differential attrition or missing data, unintended exposure to multiple treatments and other practical realities can be reduced with careful planning, proper study designs and best practices.

Time variation in the probability of failing to detect a case of polymerase chain reaction testing for SARS-CoV-2 as estimated from a viral dynamics model.

Viral tests including polymerase chain reaction (PCR) tests are recommended to diagnose COVID-19 infection during the acute phase of infection. A test should have high sensitivity; however, the sensitivity of the PCR test is highly influenced by viral load, which changes over time. Because it is difficult to collect data before the onset of symptoms, the current literature on the sensitivity of the PCR test before symptom onset is limited. In this study, we used a viral dynamics model to track the probability of failing to detect a case of PCR testing over time, including the presymptomatic period. The model was parametrized by using longitudinal viral load data collected from 30 hospitalized patients. The probability of failing to detect a case decreased toward symptom onset, and the lowest probability was observed 2 days after symptom onset and increased afterwards. The probability on the day of symptom onset was 1.0% (95% CI: 0.5 to 1.9) and that 2 days before symptom onset was 60.2% (95% CI: 57.1 to 63.2). Our study suggests that the diagnosis of COVID-19 by PCR testing should be done carefully, especially when the test is performed before or way after symptom onset. Further study is needed of patient groups with potentially different viral dynamics, such as asymptomatic cases.

Correlates of disparities in syringe return ratios: A cross-sectional study of a syringe services program in New York.

BACKGROUND: Predictors of syringe exchange behavior are critical to informing secondary prevention measures needed to attenuate risk of blood-borne infections among persons who inject drugs (PWID). METHODS: Participants included PWIDs who attended a syringe services program in New York from 2015 to 2017 (n = 1777). We analyzed the syringe return ratio (receipts/returns) with two distinct but related methodological strategies-threshold logistic regression and quantile regression-to identify correlates of disparities in syringe return ratios. RESULTS: The majority of participants were white males negative for HIV (90% white, 63% male, 76% HIV-). Logistic and quantile regression models showed that the correlates of disparate syringe return ratios (i.e., magnitude and directionality of differences) changed across different percentile groups and quantile levels, respectively. At the median threshold, being single, urbanicity, and older age were associated with higher return ratios. Syringe return ratio disparities were more pronounced among subgroups of nontypical PWIDs (with extremely low or high return ratios) especially by urbanicity, race, relationship status, and type of housing. CONCLUSIONS: Irrespective of urbanicity classification, correlates of syringe return ratios such as older age, Black race, single relationship status, and unstable housing appear to be critical to informing targeted secondary prevention initiatives that promote harm reduction behavior.

Best (but oft-forgotten) practices: identifying and accounting for regression to the mean in nutrition and obesity research.

BACKGROUND: Regression to the mean (RTM) is a statistical phenomenon where initial measurements of a variable in a nonrandom sample at the extreme ends of a distribution tend to be closer to the mean upon a second measurement. Unfortunately, failing to account for the effects of RTM can lead to incorrect conclusions on the observed mean difference between the 2 repeated measurements in a nonrandom sample that is preferentially selected for deviating from the population mean of the measured variable in a particular direction. Study designs that are susceptible to misattributing RTM as intervention effects have been prevalent in nutrition and obesity research. This field often conducts secondary analyses of existing intervention data or evaluates intervention effects in those most at risk (i.e., those with observations at the extreme ends of a distribution). OBJECTIVES: To provide best practices to avoid unsubstantiated conclusions as a result of ignoring RTM in nutrition and obesity research. METHODS: We outlined best practices for identifying whether RTM is likely to be leading to biased inferences, using a flowchart that is available as a web-based app at https://dustyturner.shinyapps.io/DecisionTreeMeanRegression/. We also provided multiple methods to quantify the degree of RTM. RESULTS: Investigators can adjust analyses to include the RTM effect, thereby plausibly removing its biasing influence on estimating the true intervention effect. CONCLUSIONS: The identification of RTM and implementation of proper statistical practices will help advance the field by improving scientific rigor and the accuracy of conclusions. This trial was registered at clinicaltrials.gov as NCT00427193.

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.

Establishment of a multicomponent dietary bioactive human equivalent dose to delete damaged Lgr5+ stem cells using a mouse colon tumor initiation model.

Multicomponent therapy has gained interest for its potential to synergize and subsequently lower the effective dose of each constituent required to reduce colon cancer risk. We have previously showed that rapidly cycling Lgr5 stem cells are exquisitely sensitive to extrinsic dietary factors that modulate colon cancer risk. In the present study, we quantified the dose-dependent synergistic properties of dietary n-3 polyunsaturated fatty acids (PUFA) and curcumin (Cur) to promote targeted apoptotic deletion of damaged colonic Lgr5 stem cells. For this purpose, both heterogeneous bulk colonocytes and Lgr5 stem cells were isolated from Lgr5-EGFP-IRES-CreER knock-in mice injected with azoxymethane (AOM). Isolated cells were analyzed for DNA damage (γH2AX), apoptosis (cleaved caspase-3), and targeted apoptosis (both γH2AX and cleaved caspase-3) at 12 h post-AOM injection. Comparison of the percentage of targeted apoptosis in Lgr5 stem cells (GFP) across a broad bioactive dose-range revealed an ED50 of 16.0 mg/day n-3 PUFA + 15.9 mg/day Cur. This corresponded to a human equivalent dose of 3.0 g n-3 PUFA + 3.0 g Cur. In summary, our results provide evidence that a low dose (n-3 PUFA + Cur) combination diet reduces AOM-induced DNA damage in Lgr5 stem cells and enhances targeted apoptosis of DNA-damaged cells, implying that a lower human equivalent dose can be utilized in future human clinical trials.