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 noncompliance would result in a reduction in GFR compared with a work-rest matched compliant trial. Twelve healthy adults completed five trials (four NIOSH compliant and one noncompliant) that consisted of 4 h of exposure to a range of WBGTs. Subjects walked on a treadmill (heat production: approximately 430 W) and work-rest ratios (work/h: 60, 45, 30, and 15 min) were prescribed as a function of WBGT (24°C, 26.5°C, 28.5°C, 30°C, and 36°C), and subjects drank a sport drink ad libitum. Peak core temperature (TC) and percentage change in body weight (%ΔBW) 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 versus noncompliant trials (P < 0.001). %ΔBW did not differ among NIOSH-compliant trials (P = 0.131) or between compliant versus noncompliant trials (P = 0.185). Creatinine clearance did not change or differ among compliant trials (P ≥ 0.079). Creatinine clearance did not change or differ between compliant versus noncompliant trials (P ≥ 0.661). Compliance with the NIOSH recommendations maintained GFR. Surprisingly, despite a greater heat strain in a noncompliant trial, GFR was maintained highlighting the potential relative importance of hydration.NEW & NOTEWORTHY We highlight that glomerular filtration rate (GFR) is maintained during simulated occupational heat stress across a range of total work, work-rest ratios, and wet-bulb globe temperatures with ad libitum consumption of an electrolyte and sugar-containing sports drink. Compared with a work-rest matched compliant trial, noncompliance resulted in augmented heat strain but did not induce a reduction in GFR likely due to an increased relative fluid intake and robust fluid conservatory responses.

Clustering Functional Data With Measurement Errors: A Simulation-Based Approach.

Clustering analysis of functional data, which comprises observations that evolve continuously over time or space, has gained increasing attention across various scientific disciplines. Practical applications often involve functional data that are contaminated with measurement errors arising from imprecise instruments, sampling errors, or other sources. These errors can significantly distort the inherent data structure, resulting in erroneous clustering outcomes. In this article, we propose a simulation-based approach designed to mitigate the impact of measurement errors. Our proposed method estimates the distribution of functional measurement errors through repeated measurements. Subsequently, the clustering algorithm is applied to simulated data generated from the conditional distribution of the unobserved true functional data given the observed contaminated functional data, accounting for the adjustments made to rectify measurement errors. We illustrate through simulations show that the proposed method has improved numerical performance than the naive methods that neglect such errors. Our proposed method was applied to a childhood obesity study, giving more reliable clustering results.

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.

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.

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.

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.

Comparative effects of diet and carcinogen on microRNA expression in the stem cell niche of the mouse colonic crypt.

There is mounting evidence that noncoding microRNAs (miRNA) are modulated by select chemoprotective dietary agents. For example, recently we demonstrated that the unique combination of dietary fish oil (containing n-3 fatty acids) plus pectin (fermented to butyrate in the colon) (FPA) up-regulates a subset of putative tumor suppressor miRNAs in intestinal mucosa, and down-regulates their predicted target genes following carcinogen exposure as compared to control (corn oil plus cellulose (CCA)) diet. To further elucidate the biological effects of diet and carcinogen modulated miR's in the colon, we verified that miR-26b and miR-203 directly target PDE4B and TCF4, respectively. Since perturbations in adult stem cell dynamics are generally believed to represent an early step in colon tumorigenesis and to better understand how the colonic stem cell population responds to environmental factors such as diet and carcinogen, we additionally determined the effects of the chemoprotective FPA diet on miRNAs and mRNAs in colonic stem cells obtained from Lgr5-EGFP-IRES-creER(T2) knock-in mice. Following global miRNA profiling, 26 miRNAs (P<0.05) were differentially expressed in Lgr5(high) stem cells as compared to Lgr5(negative) differentiated cells. FPA treatment up-regulated miR-19b, miR-26b and miR-203 expression as compared to CCA specifically in Lgr5(high) cells. In contrast, in Lgr5(negative) cells, only miR-19b and its indirect target PTK2B were modulated by the FPA diet. These data indicate for the first time that select dietary cues can impact stem cell regulatory networks, in part, by modulating the steady-state levels of miRNAs. To our knowledge, this is the first study to utilize Lgr5(+) reporter mice to determine the impact of diet and carcinogen on miRNA expression in colonic stem cells and their progeny.

Homeostatic responses of colonic LGR5+ stem cells following acute in vivo exposure to a genotoxic carcinogen.

Perturbations in DNA damage, DNA repair, apoptosis and cell proliferation in the base of the crypt where stem cells reside are associated with colorectal cancer (CRC) initiation and progression. Although the transformation of leucine-rich repeat-containing G protein-coupled receptor 5 (Lgr5)(+) cells is an extremely efficient route towards initiating small intestinal adenomas, the role of Lgr5(+) cells in CRC pathogenesis has not been well investigated. Therefore, we further characterized the properties of colonic Lgr5(+) cells compared to differentiated cells in Lgr5-EGFP-IRES-creER(T2) knock-in mice at the initiation stage of carcinogen azoxymethane (AOM)-induced tumorigenesis using a quantitative immunofluorescence microscopy approach. At 12 and 24h post-AOM treatment, colonic Lgr5(+) stem cells (GFP(high)) were preferentially damaged by carcinogen, exhibiting a 4.7-fold induction of apoptosis compared to differentiated (GFP(neg)) cells. Furthermore, with respect to DNA repair, O(6)-methylguanine DNA methyltransferase (MGMT) expression was preferentially induced (by 18.5-fold) in GFP(high) cells at 24h post-AOM treatment compared to GFP(neg) differentiated cells. This corresponded with a 4.3-fold increase in cell proliferation in GFP(high) cells. These data suggest that Lgr5(+) stem cells uniquely respond to alkylation-induced DNA damage by upregulating DNA damage repair, apoptosis and cell proliferation compared to differentiated cells in order to maintain genomic integrity. These findings highlight the mechanisms by which colonic Lgr5(+) stem cells respond to cancer-causing environmental factors.

PCAN: Probabilistic correlation analysis of two non-normal data sets.

Most cancer research now involves one or more assays profiling various biological molecules, e.g., messenger RNA and micro RNA, in samples collected on the same individuals. The main interest with these genomic data sets lies in the identification of a subset of features that are active in explaining the dependence between platforms. To quantify the strength of the dependency between two variables, correlation is often preferred. However, expression data obtained from next-generation sequencing platforms are integer with very low counts for some important features. In this case, the sample Pearson correlation is not a valid estimate of the true correlation matrix, because the sample correlation estimate between two features/variables with low counts will often be close to zero, even when the natural parameters of the Poisson distribution are, in actuality, highly correlated. We propose a model-based approach to correlation estimation between two non-normal data sets, via a method we call Probabilistic Correlations ANalysis, or PCAN. PCAN takes into consideration the distributional assumption about both data sets and suggests that correlations estimated at the model natural parameter level are more appropriate than correlations estimated directly on the observed data. We demonstrate through a simulation study that PCAN outperforms other standard approaches in estimating the true correlation between the natural parameters. We then apply PCAN to the joint analysis of a microRNA (miRNA) and a messenger RNA (mRNA) expression data set from a squamous cell lung cancer study, finding a large number of negative correlation pairs when compared to the standard approaches.

Demand-driven capacity building for public health nutrition research in Lao PDR.

In Laos, rates of undernutrition, especially among children under 5 years of age, remain high. In response, a large multidisciplinary team embarked on a multi-year project in Laos beginning in 2019 with the purpose of institutional strengthening around public health nutrition research. This paper summarizes the Applied Nutrition Research Capacity Building project's activities, immediate project results, and prospects for sustaining impacts into the future. Eight primary activities were undertaken, including back-office strengthening, mentored research, and curriculum review and development. Requested training and skill development in areas related to public health nutrition, anthropometry, and research methods reached more than 1000 professionals. The first edition of a Lao-English Nutrition Glossary was produced, as was the country's first National Nutrition Research Agenda, a document which sets locally-identified priorities for future research. Project success was achieved by focusing on the priorities of project partners and the Lao government, as articulated in the Lao National Nutrition Strategy and Action Plan. Project design elements that could guide similar efforts undertaken elsewhere include multi-year engagement, an emphasis on sustained peer mentorship, and the use of an extended period of pre-planning in collaboration with project stakeholders prior to the start of activities.

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.

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.

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.

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.

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)'.

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.

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.

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.