Dataset Information

An Empirical Validation of the Within-subject Biospecimens Pooling Approach to Minimize Exposure Misclassification in Biomarker-based Studies.

ABSTRACT:

Background

Within-subject biospecimens pooling can theoretically reduce bias in dose-response functions from biomarker-based studies when exposure assessment suffers from classical-type error. However, collecting many urine voids each day is cumbersome. We evaluated the empirical validity of a within-subject pooling approach and compared several options to avoid sampling each void.

Methods

In 16 pregnant women who collected a spot of each urine void over several nonconsecutive weeks, we compared concentrations of 10 phenols in daily, weekly, and pregnancy within-subject pools. We pooled either three or all daily samples. In a simulation study using these data, we quantified bias in dose-response functions when using one to 20 urine samples per subject to assess methylparaben (a compound with moderate within-subject variability) and bisphenol A (high variability) exposures.

Results

Correlations between exposure estimates from pools of all and of only three voids per day were above 0.80 for all time windows and compounds, except for benzophenone-3 and triclosan in the daily time window (correlations, 0.57-0.68). With one spot sample to assess pregnancy exposure, correlations were all below 0.74. Using only one biospecimen led to attenuation bias in the dose-response functions of 29% (methylparaben) and 69% (bisphenol A); four samples for methylparaben and 18 for bisphenol A decreased bias to 10%.

Conclusions

For nonpersistent chemicals, collecting and pooling three samples per day instead of all daily samples efficiently estimates exposures over a week or more. Collecting around 20 biospecimens can strongly limit attenuation bias for nonpersistent chemicals such as bisphenol A.

SUBMITTER: Vernet C

PROVIDER: S-EPMC8988263 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Publications

An Empirical Validation of the Within-subject Biospecimens Pooling Approach to Minimize Exposure Misclassification in Biomarker-based Studies.

Vernet Céline C Philippat Claire C Agier Lydiane L Calafat Antonia M AM Ye Xiaoyun X Lyon-Caen Sarah S Hainaut Pierre P Siroux Valérie V Schisterman Enrique F EF Slama Rémy R

Epidemiology (Cambridge, Mass.) 20190901 5

<h4>Background</h4>Within-subject biospecimens pooling can theoretically reduce bias in dose-response functions from biomarker-based studies when exposure assessment suffers from classical-type error. However, collecting many urine voids each day is cumbersome. We evaluated the empirical validity of a within-subject pooling approach and compared several options to avoid sampling each void.<h4>Methods</h4>In 16 pregnant women who collected a spot of each urine void over several nonconsecutive wee ...[more]

PMID: 31373935

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Project description:BackgroundLarge prospective cohort studies have been fruitful for identifying exposure-disease associations. In a cohort where biospecimens (e.g., blood, urine) were collected at enrollment, analysts can exploit a case-cohort approach: Biospecimens from a random sample of cohort participants, called the "subcohort," plus a sample of incident cases that were not part of the subcohort are assayed. Reusing subcohort data for multiple disease outcomes can reduce costs and conserve specimen archives. Pooling biospecimen samples before assay could both save money and reduce depletion of the archive but has not been studied for cohort studies.ObjectivesWe develop and evaluate a biospecimen pooling strategy for case-cohort analyses that relate an exposure to risk of a rare disease.MethodsOur approach involves constructing pooling sets for cases not in the subcohort after grouping them according to time of diagnosis (e.g., age). In contrast, members of the subcohort are grouped by age at entry before constructing pooling sets. The analyst then fits a logistic regression model that jointly stratifies by age at risk and pooling set size and adjusts for confounders. We used simulations (288 sampling scenarios with 1,000 simulated datasets each) to evaluate the performance of this approach for several sizes of pooling sets and illustrated its application to environmental epidemiologic studies by reanalyzing Sister Study data.ResultsParameter estimates were nearly unbiased, and 95% confidence intervals constructed using a bootstrap estimate of the standard error performed well. In statistical tests also based on the bootstrap standard error, pooling up to 8 specimens per pool caused only modest loss of power. Assigning more cohort members to the subcohort and commensurately increasing the number of specimens per pool improved power and precision substantially while reducing the number of assays.DiscussionWhen using case-cohort analysis to study disease outcomes in relation to exposures assessed using biospecimens in a cohort study, epidemiologists should consider biospecimen pooling as a way to improve statistical power, conserve irreplaceable archives, and save money. https://doi.org/10.1289/EHP14476.

Project description:BackgroundCase-crossover studies have been widely used in various fields including pharmacoepidemiology. Vines and Farrington indicated in 2001 that when within-subject exposure dependency exists, conditional logistic regression can be biased. However, this bias has not been well studied.MethodsWe have extended findings by Vines and Farrington to develop a weighting method for the case-crossover study which removes bias from within-subject exposure dependency. Our method calculates the exposure probability at the case period in the case-crossover study which is used to weight the likelihood formulae presented by Greenland in 1999. We simulated data for the population with a disease where most patients receive a cyclic treatment pattern with within-subject exposure dependency but no time trends while some patients stop and start treatment. Finally, the method was applied to real-world data from Japan to study the association between celecoxib and peripheral edema and to study the association between selective serotonin reuptake inhibitor (SSRI) and hip fracture in Australia.ResultsWhen the simulated rate ratio of the outcome was 4.0 in a case-crossover study with no time-varying confounder, the proposed weighting method and the Mantel-Haenszel odds ratio reproduced the true rate ratio. When a time-varying confounder existed, the Mantel-Haenszel method was biased but the weighting method was not. When more than one control period was used, standard conditional logistic regression was biased either with or without time-varying confounding and the bias increased (up to 8.7) when the study period was extended. In real-world analysis with a binary exposure variable in Japan and Australia, the point estimate of the odds ratio (around 2.5 for the association between celecoxib and peripheral edema and around 1.6 between SSRI and hip fracture) by our weighting method was equal to the Mantel-Haenszel odds ratio and stable compared with standard conditional logistic regression.ConclusionCase-crossover studies may be biased from within-subject exposure dependency, even without exposure time trends. This bias can be identified by comparing the odds ratio by the Mantel-Haenszel method and that by standard conditional logistic regression. We recommend using our proposed method which removes bias from within-subject exposure dependency and can account for time-varying confounders.

Dataset Information

An Empirical Validation of the Within-subject Biospecimens Pooling Approach to Minimize Exposure Misclassification in Biomarker-based Studies.

Background

Methods

Results

Conclusions

Publications

An Empirical Validation of the Within-subject Biospecimens Pooling Approach to Minimize Exposure Misclassification in Biomarker-based Studies.

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