Measurement error adjustment in essential fatty acid intake from a food frequency questionnaire: alternative approaches and methods

Exposure Measurement Error in Air Pollution Studies: The Impact of Shared, Multiplicative Measurement Error on Epidemiological Health Risk Estimates

Spatiotemporal air pollution models are increasingly being used to estimate health effects in epidemiological studies. Although such exposure prediction models typically result in improved spatial and temporal resolution of air pollution predictions, they remain subject to shared measurement error, a type of measurement error common in spatiotemporal exposure models which occurs when measurement error is not independent of exposures. A fundamental challenge of exposure measurement error in air pollution assessment is the strong correlation and sometimes identical (shared) error of exposure estimates across geographic space and time. When exposure estimates with shared measurement error are used to estimate health risk in epidemiological analyses, complex errors are potentially introduced, resulting in biased epidemiological conclusions. We demonstrate the influence of using a three-stage spatiotemporal exposure prediction model and introduce formal methods of shared, multiplicative measurement error (SMME) correction of epidemiological health risk estimates. Using our three-stage, ensemble learning based nitrogen oxides (NOx) exposure prediction model, we quantified SMME. We conducted an epidemiological analysis of wheeze risk in relation to NOx exposure among school-aged children. To demonstrate the incremental influence of exposure modeling stage, we iteratively estimated the health risk using assigned exposure predictions from each stage of the NOx model. We then determined the impact of SMME on the variance of the health risk estimates under various scenarios. Depending on the stage of the spatiotemporal exposure model used, we found that wheeze odds ratio ranged from 1.16 to 1.28 for an interquartile range increase in NOx. With each additional stage of exposure modeling, the health effect estimate moved further away from the null (OR=1). When corrected for observed SMME, the health effects confidence intervals slightly lengthened, but our epidemiological conclusions were not altered. When the variance estimate was corrected for the potential "worst case scenario" of SMME, the standard error further increased, having a meaningful influence on epidemiological conclusions. Our framework can be expanded and used to understand the implications of using exposure predictions subject to shared measurement error in future health investigations.

Methods to account for uncertainties in exposure assessment in studies of environmental exposures

BACKGROUND

Accurate exposure estimation in environmental epidemiological studies is crucial for health risk assessment. Failure to account for uncertainties in exposure estimation could lead to biased results in exposure-response analyses. Assessment of the effects of uncertainties in exposure estimation on risk estimates received a lot of attention in radiation epidemiology and in several studies of diet and air pollution. The objective of this narrative review is to examine the commonly used statistical approaches to account for exposure estimation errors in risk analyses and to suggest how each could be applied in environmental epidemiological studies.

MAIN TEXT

We review two main error types in estimating exposures in epidemiological studies: shared and unshared errors and their subtypes. We describe the four main statistical approaches to adjust for exposure estimation uncertainties (regression calibration, simulation-extrapolation, Monte Carlo maximum likelihood and Bayesian model averaging) along with examples to give readers better understanding of their advantages and limitations. We also explain the advantages of using a 2-dimensional Monte-Carlo (2DMC) simulation method to quantify the effect of uncertainties in exposure estimates using full-likelihood methods. For exposures that are estimated independently between subjects and are more likely to introduce unshared errors, regression calibration and SIMEX methods are able to adequately account for exposure uncertainties in risk analyses. When an uncalibrated measuring device is used or estimation parameters with uncertain mean values are applied to a group of people, shared errors could potentially be large. In this case, Monte Carlo maximum likelihood and Bayesian model averaging methods based on estimates of exposure from the 2DMC simulations would work well. The majority of reviewed studies show relatively moderate changes (within 100%) in risk estimates after accounting for uncertainties in exposure estimates, except for the two studies which doubled/tripled naïve estimates.

CONCLUSIONS

In this paper, we demonstrate various statistical methods to account for uncertain exposure estimates in risk analyses. The differences in the results of various adjustment methods could be due to various error structures in datasets and whether or not a proper statistical method was applied. Epidemiological studies of environmental exposures should include exposure-response analyses accounting for uncertainties in exposure estimates.

Combining biomarker and self-reported dietary intake data: A review of the state of the art and an exposition of concepts

Classical approaches to assessing dietary intake are associated with measurement error. In an effort to address inherent measurement error in dietary self-reported data there is increased interest in the use of dietary biomarkers as objective measures of intake. Furthermore, there is a growing consensus of the need to combine dietary biomarker data with self-reported data. A review of state of the art techniques employed when combining biomarker and self-reported data is conducted. Two predominant methods, the calibration method and the method of triads, emerge as relevant techniques used when combining biomarker and self-reported data to account for measurement errors in dietary intake assessment. Both methods crucially assume measurement error independence. To expose and understand the performance of these methods in a range of realistic settings, their underpinning statistical concepts are unified and delineated, and thorough simulation studies are conducted. Results show that violation of the methods' assumptions negatively impacts resulting inference but that this impact is mitigated when the variation of the biomarker around the true intake is small. Thus there is much scope for the further development of biomarkers and models in tandem to achieve the ultimate goal of accurately assessing dietary intake.

MALDI-TOF peptidomic analysis of serum and post-prostatic massage urine specimens to identify prostate cancer biomarkers

Background

Lower urinary tract symptoms (LUTS) and prostate specific antigen-based parameters seem to have only a limited utility for the differential diagnosis of prostate cancer (PCa). MALDI-TOF/MS peptidomic profiling could be a useful diagnostic tool for biomarker discovery, although reproducibility issues have limited its applicability until now. The current study aimed to evaluate a new MALDI-TOF/MS candidate biomarker.

Methods

Within- and between-subject variability of MALDI-TOF/MS-based peptidomic urine and serum analyses were evaluated in 20 and 15 healthy donors, respectively. Normalizations and approaches for accounting below limit of detection (LOD) values were utilized to enhance reproducibility, while Monte Carlo experiments were performed to verify whether measurement error can be dealt with LOD data. Post-prostatic massage urine and serum samples from 148 LUTS patients were analysed using MALDI-TOF/MS. Regression-calibration and simulation and extrapolation methods were used to derive the unbiased association between peptidomic features and PCa.

Results

Although the median normalized peptidomic variability was 24.9%, the within- and between-subject variability showed that median normalization, LOD adjustment, and log₂ data transformation were the best combination in terms of reliability; in measurement error conditions, intraclass correlation coefficient was a reliable estimate when the LOD/2 was substituted for below LOD values. In the patients studied, 43 peptides were shared by the urine and serum, and several features were found to be associated with PCa. Only few serum features, however, show statistical significance after the multiple testing procedures were completed. Two serum fragmentation patterns corresponded to the complement C4-A.

Conclusions

MALDI-TOF/MS serum peptidome profiling was more efficacious with respect to post-prostatic massage urine analysis in discriminating PCa.

Electronic supplementary material

The online version of this article (10.1186/s12014-018-9199-8) contains supplementary material, which is available to authorized users.

Periconceptional maternal 'high fish and olive oil, low meat' dietary pattern is associated with increased embryonic growth: The Rotterdam Periconceptional Cohort (Predict) Study

OBJECTIVE

To investigate the association between periconceptional maternal dietary pattern and first-trimester embryonic growth.

METHODS

This was a prospective cohort study of 228 women with a singleton ongoing pregnancy, of which 135 were strictly dated spontaneous pregnancies and 93 were pregnancies achieved after in-vitro fertilization or intracytoplasmatic sperm injection (IVF/ICSI). All women underwent serial transvaginal three-dimensional ultrasound (3D-US) examinations from 6 + 0 to 13 + 0 weeks' gestation. Crown-rump length (CRL) and embryonic volume (EV) measurements were performed using a virtual reality system. Information on periconceptional maternal dietary intake was collected via food frequency questionnaires. Principal component analysis was performed to identify dietary patterns. Associations between dietary patterns and CRL and EV trajectories were investigated using linear mixed models adjusted for potential confounders.

RESULTS

A median of five (range, one to seven) 3D-US scans per pregnancy were performed. Of 1162 datasets, quality was sufficient to perform CRL measurements in 991 (85.3%) and EV measurements in 899 (77.4%). A dietary pattern comprising high intake of fish and olive oil and a very low intake of meat was identified as beneficial for embryonic growth. In strictly dated spontaneous pregnancies, strong adherence to the 'high fish and olive oil, low meat' dietary pattern was associated with a 1.9 mm (95% CI, 0.1-3.63 mm) increase in CRL (+14.6%) at 7 weeks and a 3.4 mm (95% CI, 0.2-7.81 mm) increase (+6.9%) at 11 weeks, whereas EV increased by 0.06 cm³ (95% CI, 0.01-0.13 cm³ ) (+20.4%) at 7 weeks and 1.43 cm³ (95% CI, 0.99-1.87 cm³ ) (+14.4%) at 11 weeks. No significant association was observed in the total study population or in the IVF/ICSI subgroup.

CONCLUSION

Periconceptional maternal adherence to a high fish and olive oil, low meat dietary pattern is positively associated with embryonic growth in spontaneously conceived pregnancies. Copyright © 2017 ISUOG. Published by John Wiley & Sons Ltd.

Systematic review of statistical approaches to quantify, or correct for, measurement error in a continuous exposure in nutritional epidemiology

BACKGROUND

Several statistical approaches have been proposed to assess and correct for exposure measurement error. We aimed to provide a critical overview of the most common approaches used in nutritional epidemiology.

METHODS

MEDLINE, EMBASE, BIOSIS and CINAHL were searched for reports published in English up to May 2016 in order to ascertain studies that described methods aimed to quantify and/or correct for measurement error for a continuous exposure in nutritional epidemiology using a calibration study.

RESULTS

We identified 126 studies, 43 of which described statistical methods and 83 that applied any of these methods to a real dataset. The statistical approaches in the eligible studies were grouped into: a) approaches to quantify the relationship between different dietary assessment instruments and "true intake", which were mostly based on correlation analysis and the method of triads; b) approaches to adjust point and interval estimates of diet-disease associations for measurement error, mostly based on regression calibration analysis and its extensions. Two approaches (multiple imputation and moment reconstruction) were identified that can deal with differential measurement error.

CONCLUSIONS

For regression calibration, the most common approach to correct for measurement error used in nutritional epidemiology, it is crucial to ensure that its assumptions and requirements are fully met. Analyses that investigate the impact of departures from the classical measurement error model on regression calibration estimates can be helpful to researchers in interpreting their findings. With regard to the possible use of alternative methods when regression calibration is not appropriate, the choice of method should depend on the measurement error model assumed, the availability of suitable calibration study data and the potential for bias due to violation of the classical measurement error model assumptions. On the basis of this review, we provide some practical advice for the use of methods to assess and adjust for measurement error in nutritional epidemiology.

Within-subject Pooling of Biological Samples to Reduce Exposure Misclassification in Biomarker-based Studies

Supplemental Digital Content is available in the text.

For chemicals with high within-subject temporal variability, assessing exposure biomarkers in a spot biospecimen poorly estimates average levels over long periods. The objective is to characterize the ability of within-subject pooling of biospecimens to reduce bias due to exposure misclassification when within-subject variability in biomarker concentrations is high.

Structural equation modeling for analyzing erythrocyte fatty acids in Framingham

Research has shown that several types of erythrocyte fatty acids (i.e., omega-3, omega-6, and trans) are associated with risk for cardiovascular diseases. However, there are complex metabolic and dietary relations among fatty acids, which induce correlations that are typically ignored when using them as risk predictors. A latent variable approach could summarize these complex relations into a few latent variable scores for use in statistical models. Twenty-two red blood cell (RBC) fatty acids were measured in Framingham (N = 3196). The correlation matrix of the fatty acids was modeled using structural equation modeling; the model was tested for goodness-of-fit and gender invariance. Thirteen fatty acids were summarized by three latent variables, and gender invariance was rejected so separate models were developed for men and women. A score was developed for the polyunsaturated fatty acid (PUFA) latent variable, which explained about 30% of the variance in the data. The PUFA score included loadings in opposing directions among three omega-3 and three omega-6 fatty acids, and incorporated the biosynthetic and dietary relations among them. Whether the PUFA factor score can improve the performance of risk prediction in cardiovascular diseases remains to be tested.

A comparative validation of a child food frequency questionnaire using red blood cell membrane fatty acids

BACKGROUND/OBJECTIVES

There are limited validated tools available for the assessment of dietary intake in pediatric populations. This report describes a comparative validation study of selected fatty acid intakes in children assessed by food frequency questionnaire (FFQ), compared with erythrocyte membrane fatty acids.

SUBJECTS/METHODS

Overall, 46 overweight and 47 healthy-weight children aged 5-12 years (mean±SD, 9.1±1.3years, body mass index 20.5±4.0) were recruited; dietary fatty acid intakes assessed by parent report using a 135-item semi-quantitative FFQ, were compared with selected child erythrocyte membrane fatty acids assessed from fasting samples using gas chromatography. Spearman's rank correlation coefficients were calculated between fatty acid intake estimates (% of energy) and erythrocyte membrane concentrations (%mol/mol).

RESULTS

Significant correlations were found between dietary and erythrocyte eicosapentanoic acid (EPA) concentration (r=0.24, P<0.05) with a statistical trend for total omega three (∑n-3) fatty acids (r=0.22, P=0.06) and linoleic acid (r=0.32, P=0.07) in the healthy-weight children only.

CONCLUSION

Parental report of selected child fatty acid intakes using an FFQ can be used to provide an estimate of child intake of EPA, but further work is required to quantify this relationship for other fatty acids and in other populations.