Can we use biomarkers in combination with self-reports to strengthen the analysis of nutritional epidemiologic studies?

Identifying diet-disease relationships in nutritional cohort studies is plagued by the measurement error in self-reported intakes

Intake_epis_food(): An R Function for Fitting a Bivariate Nonlinear Measurement Error Model to Estimate Usual and Energy Intake for Episodically Consumed Foods

We consider a Bayesian analysis using WinBUGS to estimate the distribution of usual intake for episodically consumed foods and energy (calories). The model uses measures of nutrition and energy intakes via a food frequency questionnaire along with repeated 24 hour recalls and adjusting covariates. In order to estimate the usual intake of the food, we phrase usual intake in terms of person-specific random effects, along with day-to-day variability in food and energy consumption. Three levels are incorporated in the model. The first level incorporates information about whether an individual reported consumption of a particular food item. The second level incorporates the amount of food consumption equalling to zero if not consumed, and the third level incorporates the amount of energy intake. Estimates of posterior means of parameters and distributions of usual intakes are obtained by using Markov chain Monte Carlo calculations which can be thought as mean estimates for frequentists. This R function reports to users point estimates and credible intervals for parameters in the model, samples from their posterior distribution, samples from the distribution of usual intake and usual energy intake, trace plots of parameters and summary statistics of usual intake, usual energy intake and energy adjusted usual intake

Introduction to statistical simulations in health research

In health research, statistical methods are frequently used to address a wide variety of research questions. For almost every analytical challenge, different methods are available. But how do we choose between different methods and how do we judge whether the chosen method is appropriate for our specific study? Like in any science, in statistics, experiments can be run to find out which methods should be used under which circumstances. The main objective of this paper is to demonstrate that simulation studies, that is, experiments investigating synthetic data with known properties, are an invaluable tool for addressing these questions. We aim to provide a first introduction to simulation studies for data analysts or, more generally, for researchers involved at different levels in the analyses of health data, who (1) may rely on simulation studies published in statistical literature to choose their statistical methods and who, thus, need to understand the criteria of assessing the validity and relevance of simulation results and their interpretation; and/or (2) need to understand the basic principles of designing statistical simulations in order to efficiently collaborate with more experienced colleagues or start learning to conduct their own simulations. We illustrate the implementation of a simulation study and the interpretation of its results through a simple example inspired by recent literature, which is completely reproducible using the R-script available from online supplemental file 1

A new multivariate measurement error model with zero-inflated dietary data, and its application to dietary assessment

In the United States the preferred method of obtaining dietary intake data is the 24-hour dietary recall, yet the measure of most interest is usual or long-term average daily intake, which is impossible to measure. Thus, usual dietary intake is assessed with considerable measurement error. Also, diet represents numerous foods, nutrients and other components, each of which have distinctive attributes. Sometimes, it is useful to examine intake of these components separately, but increasingly nutritionists are interested in exploring them collectively to capture overall dietary patterns. Consumption of these components varies widely: some are consumed daily by almost everyone on every day, while others are episodically consumed so that 24-hour recall data are zero-inflated. In addition, they are often correlated with each other. Finally, it is often preferable to analyze the amount of a dietary component relative to the amount of energy (calories) in a diet because dietary recommendations often vary with energy level. The quest to understand overall dietary patterns of usual intake has to this point reached a standstill. There are no statistical methods or models available to model such complex multivariate data with its measurement error and zero inflation. This paper proposes the first such model, and it proposes the first workable solution to fit such a model. After describing the model, we use survey-weighted MCMC computations to fit the model, with uncertainty estimation coming from balanced repeated replication.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS446 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Sugar-sweetened beverage intake and cardiovascular risk factor profile in youth with type 1 diabetes: application of measurement error methodology in the SEARCH Nutrition Ancillary Study

The SEARCH Nutrition Ancillary Study aims to investigate the role of dietary intake on the development of long-term complications of type 1 diabetes in youth, and capitalise on measurement error (ME) adjustment methodology. Using the National Cancer Institute (NCI) method for episodically consumed foods, we evaluated the relationship between sugar-sweetened beverage (SSB) intake and cardiovascular risk factor profile, with the application of ME adjustment methodology. The calibration sample included 166 youth with two FFQ and three 24 h dietary recall data within 1 month. The full sample included 2286 youth with type 1 diabetes. SSB intake was significantly associated with higher TAG, total and LDL-cholesterol concentrations, after adjusting for energy, age, diabetes duration, race/ethnicity, sex and education. The estimated effect size was larger (model coefficients increased approximately 3-fold) after the application of the NCI method than without adjustment for ME. Compared with individuals consuming one serving of SSB every 2 weeks, those who consumed one serving of SSB every 2 d had 3·7 mg/dl (0·04 mmol/l) higher TAG concentrations and 4·0 mg/dl (0·10 mmol/l) higher total cholesterol and LDL-cholesterol concentrations, after adjusting for ME and covariates. SSB intake was not associated with measures of adiposity and blood pressure. Our findings suggest that SSB intake is significantly related to increased lipid levels in youth with type 1 diabetes, and that estimates of the effect size of SSB on lipid levels are severely attenuated in the presence of ME. Future studies in youth with diabetes should consider a design that will allow for the adjustment for ME when studying the influence of diet on health status

Epidemiologic analyses with error-prone exposures: review of current practice and recommendations.

PURPOSE: Variables in observational studies are commonly subject to measurement error, but the impact of such errors is frequently ignored. As part of the STRengthening Analytical Thinking for Observational Studies Initiative, a task group on measurement error and misclassification seeks to describe the current practice for acknowledging and addressing measurement error. METHODS: Task group on measurement error and misclassification conducted a literature survey of four types of research studies that are typically impacted by exposure measurement error: (1) dietary intake cohort studies, (2) dietary intake population surveys, (3) physical activity cohort studies, and (4) air pollution cohort studies. RESULTS: The survey revealed that while researchers were generally aware that measurement error affected their studies, very few adjusted their analysis for the error. Most articles provided incomplete discussion of the potential effects of measurement error on their results. Regression calibration was the most widely used method of adjustment. CONCLUSIONS: Methods to correct for measurement error are available but require additional data regarding the error structure. There is a great need to incorporate such data collection within study designs and improve the analytical approach. Increased efforts by investigators, editors, and reviewers are needed to improve presentation of research when data are subject to error

Opposing Effects of the Angiopoietins on the Thrombin-Induced Permeability of Human Pulmonary Microvascular Endothelial Cells

BACKGROUND: Angiopoietin-2 (Ang-2) is associated with lung injury in ALI/ARDS. As endothelial activation by thrombin plays a role in the permeability of acute lung injury and Ang-2 may modulate the kinetics of thrombin-induced permeability by impairing the organization of vascular endothelial (VE-)cadherin, and affecting small Rho GTPases in human pulmonary microvascular endothelial cells (HPMVECs), we hypothesized that Ang-2 acts as a sensitizer of thrombin-induced hyperpermeability of HPMVECs, opposed by Ang-1. METHODOLOGY/PRINCIPAL FINDINGS: Permeability was assessed by measuring macromolecule passage and transendothelial electrical resistance (TEER). Angiopoietins did not affect basal permeability. Nevertheless, they had opposing effects on the thrombin-induced permeability, in particular in the initial phase. Ang-2 enhanced the initial permeability increase (passage, P = 0.010; TEER, P = 0.021) in parallel with impairment of VE-cadherin organization without affecting VE-cadherin Tyr685 phosphorylation or increasing RhoA activity. Ang-2 also increased intercellular gap formation. Ang-1 preincubation increased Rac1 activity, enforced the VE-cadherin organization, reduced the initial thrombin-induced permeability (TEER, P = 0.027), while Rac1 activity simultaneously normalized, and reduced RhoA activity at 15 min thrombin exposure (P = 0.039), but not at earlier time points. The simultaneous presence of Ang-2 largely prevented the effect of Ang-1 on TEER and macromolecule passage. CONCLUSIONS/SIGNIFICANCE: Ang-1 attenuated thrombin-induced permeability, which involved initial Rac1 activation-enforced cell-cell junctions, and later RhoA inhibition. In addition to antagonizing Ang-1, Ang-2 had also a direct effect itself. Ang-2 sensitized the initial thrombin-induced permeability accompanied by destabilization of VE-cadherin junctions and increased gap formation, in the absence of increased RhoA activity

New insights into the effects of error-prone exposure in the analysis of longitudinal studies with mixed models (TG4)

Mixed effects models have become one of the major approaches to the analysis of longitudinal studies. Random effects in those models play a twofold role. First, they address heterogeneity among individual temporal trajectories, and, second, they induce a correlational structure among temporal observations of the same subject. If both the exposure and outcome vary with time, it is natural to specify mixed effects model for both. If heterogeneity in temporal trajectories is related to unknown subject-level confounders, the corresponding random effects will be correlated, inducing correlation between random effects in the outcome model and the exposure. In this case, there are three different effects of the exposure on outcome, the within-subject or individual level effect, the between-subject effect of mean individual exposure, and the marginal or the population-average effect. If the existing correlation between random effects and exposure is ignored, the estimated exposure effect(s) will be biased. If exposure is measured with error, there will always be a nonzero correlation between random effects in the outcome model and error-prone exposure, even if this correlation was zero in the model with true exposure. Due to this critical result, the unbiased estimation of the effect of measurement error in the mixed model requires taking the correlation between random effects and error-prone exposure into account. Theoretical developments are exemplified by the analysis of data on physical activity energy expenditure from a large validation study of different physical activity instruments using doubly labeled water as unbiased reference measurements.Non UBCUnreviewedAuthor affiliation: National Cancer InstituteOthe