scholarly journals The impact of exposure-biased sampling designs on detection of gene–environment interactions in case–control studies with potential exposure misclassification

2014 ◽  
Vol 30 (5) ◽  
pp. 413-423 ◽  
Author(s):  
Stephanie L. Stenzel ◽  
Jaeil Ahn ◽  
Philip S. Boonstra ◽  
Stephen B. Gruber ◽  
Bhramar Mukherjee
Keyword(s):  
Case Control ◽  
Biased Sampling ◽  
Exposure Misclassification ◽  
Case Control Studies ◽  
Potential Exposure ◽  
Gene Environment ◽  
The Impact

scholarly journals Evaluating and comparing biomarkers with respect to the area under the receiver operating characteristics curve in two-phase case–control studies

Biostatistics ◽  
2016 ◽  
Vol 17 (3) ◽  
pp. 499-522 ◽  
Author(s):  
Ying Huang
Keyword(s):  
Case Control ◽  
Receiver Operating Characteristics ◽  
Biased Sampling ◽  
Second Phase ◽  
Operating Characteristics ◽  
Case Control Studies ◽  
Two Phase ◽  
Receiver Operating Characteristics Curve ◽  
The Impact ◽  
Receiver Operating

Abstract Two-phase sampling design, where biomarkers are subsampled from a phase-one cohort sample representative of the target population, has become the gold standard in biomarker evaluation. Many two-phase case–control studies involve biased sampling of cases and/or controls in the second phase. For example, controls are often frequency-matched to cases with respect to other covariates. Ignoring biased sampling of cases and/or controls can lead to biased inference regarding biomarkers' classification accuracy. Considering the problems of estimating and comparing the area under the receiver operating characteristics curve (AUC) for a binary disease outcome, the impact of biased sampling of cases and/or controls on inference and the strategy to efficiently account for the sampling scheme have not been well studied. In this project, we investigate the inverse-probability-weighted method to adjust for biased sampling in estimating and comparing AUC. Asymptotic properties of the estimator and its inference procedure are developed for both Bernoulli sampling and finite-population stratified sampling. In simulation studies, the weighted estimators provide valid inference for estimation and hypothesis testing, while the standard empirical estimators can generate invalid inference. We demonstrate the use of the analytical variance formula for optimizing sampling schemes in biomarker study design and the application of the proposed AUC estimators to examples in HIV vaccine research and prostate cancer research.

scholarly journals Case-Control Studies of Gene-Environment Interaction: Bayesian Design and Analysis

Biometrics ◽  
2009 ◽  
Vol 66 (3) ◽  
pp. 934-948 ◽  
Author(s):  
Bhramar Mukherjee ◽  
Jaeil Ahn ◽  
Stephen B. Gruber ◽  
Malay Ghosh ◽  
Nilanjan Chatterjee
Keyword(s):  
Case Control ◽  
Environment Interaction ◽  
Case Control Studies ◽  
Bayesian Design ◽  
Gene Environment Interaction ◽  
Gene Environment

Assessment of the relationship between methylenetetrahydrofolate reductase polymorphism and acute lymphoblastic leukemia: Evidence from an updated meta-analysis

2020 ◽  
Vol 26 (7) ◽  
pp. 1598-1610
Author(s):  
Rim Frikha
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Methylenetetrahydrofolate Reductase ◽  
Lymphoblastic Leukemia ◽  
Meta Analysis ◽  
Case Control ◽  
C677t Polymorphism ◽  
Case Control Studies ◽  
Gene Environment ◽  
Reductase Gene ◽  
The Relationship

Objective The methylenetetrahydrofolate reductase gene C677T polymorphism is closely related to the acute lymphoblastic leukemia. Several case–control studies have investigated this association; however, no conclusions could be drawn. A comprehensive updated meta-analysis is established to explain these contradictions and clarify the overall impact of this variant on the susceptibility to acute lymphoblastic leukemia. Methods Electronic searches were conducted to select published studies prior to June 2018. Pooled odds ratios and stratification analysis were performed under different genetic comparison models, age, and ethnicity. Results Totally, 66 case–control studies including 9619 acute lymphoblastic leukemia cases and 17,396 controls were selected. Our analyses showed that methylenetetrahydrofolate reductase C677T polymorphism was protective mainly in Asian and European countries, under all genetic models and regardless of age, but leukemogenic in mixed population. Conclusion Thus, C677T polymorphism may be a promising acute lymphoblastic leukemia biomarker, but they should be interpreted with caution considering other factors such as folic acid intake, gene–gene and gene–environment interactions.

scholarly journals Association of the MTHFR 677C>T and 1298A>C polymorphisms and male infertility risk: a meta-analysis

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Fereshteh Aliakbari ◽  
Farkhondeh Pouresmaeili ◽  
Nahal Eshghifar ◽  
Zahra Zolghadr ◽  
Faezeh Azizi
Keyword(s):  
Male Infertility ◽  
Methylenetetrahydrofolate Reductase ◽  
Meta Analysis ◽  
Case Control ◽  
Inclusion Criteria ◽  
Case Control Studies ◽  
Crude Odds Ratio ◽  
Mthfr Polymorphism ◽  
Gene Environment ◽  
The Relationship

Abstract Background and objectives One of the possible male sterility risk factors are polymorphisms of Methylenetetrahydrofolate reductase (MTHFR). However, the epidemiologic investigations described inconsistent results regarding MTHFR polymorphism and the risk of male infertility. For that reason, we carried out a meta-analysis of published case-control studies to re-examine the controversy. Methods Electronic searches of Cochrane, EMBASE, Google Scholar, and PubMed were conducted to select eligible studies for this meta-analysis (updated to May 2019). According to our exclusion and inclusion criteria, only high-quality studies that remarked the association between MTHFR polymorphisms and male infertility risk were included. The Crude odds ratio (OR) with a confidence interval of 95% (CI) was used to assess the relationship between MTHFR polymorphism and male infertility risk. Results Thirty-four case-control studies with 9662 cases and 9154 controls concerning 677C/T polymorphism and 22 case-control studies with 5893 cases and 6303 controls concerning 1298A/C polymorphism were recruited. Both MTHFR polymorphisms had significant associations with male infertility risk (CT + TT vs. CC: OR = 1.37, 95% CI: 1.21–1.55, P = 0.00, I2 = 41.9%); (CC vs. CA + AA: OR = 0.82, 95% CI: 0.52–1.30, P = 0.04, I2 = 50.1%). Further, when stratified by ethnicity, the significant association results were observed in Asians and Caucasians for 677C/T and just Asians for 1298A/C. Conclusions Some of MTHFR polymorphisms like MTHFR 677C > T are associated with an elevated male infertility risk. To confirm our conclusion and to provide more accurate and complete gene-environment communication with male infertility risk, more analytical studies are needed.

Correction of odds ratios in case-control studies for exposure misclassification with partial knowledge of the degree of agreement among experts who assessed exposures

2017 ◽  
Vol 75 (2) ◽  
pp. 155-159 ◽  
Author(s):  
Igor Burstyn ◽  
Paul Gustafson ◽  
Javier Pintos ◽  
Jérôme Lavoué ◽  
Jack Siemiatycki
Keyword(s):  
Exposure Assessment ◽  
Sensitivity And Specificity ◽  
Interrater Reliability ◽  
Credible Interval ◽  
Case Control ◽  
Point Estimate ◽  
Exposure Misclassification ◽  
Odds Ratios ◽  
Case Control Studies ◽  
Distribution Centre

ObjectivesEstimates of association between exposures and diseases are often distorted by error in exposure classification. When the validity of exposure assessment is known, this can be used to adjust these estimates. When exposure is assessed by experts, even if validity is not known, we sometimes have information about interrater reliability. We present a Bayesian method for translating the knowledge of interrater reliability, which is often available, into knowledge about validity, which is often needed but not directly available, and applying this to correct odds ratios (OR).MethodsThe method allows for inclusion of observed potential confounders in the analysis, as is common in regression-based control for confounding. Our method uses a novel type of prior on sensitivity and specificity. The approach is illustrated with data from a case-control study of lung cancer risk and occupational exposure to diesel engine emissions, in which exposure assessment was made by detailed job history interviews with study subjects followed by expert judgement.ResultsUsing interrater agreement measured by kappas (κ), we estimate sensitivity and specificity of exposure assessment and derive misclassification-corrected confounder-adjusted OR. Misclassification-corrected and confounder-adjusted OR obtained with the most defensible prior had a posterior distribution centre of 1.6 with 95% credible interval (Crl) 1.1 to 2.6. This was on average greater in magnitude than frequentist point estimate of 1.3 (95% Crl 1.0 to 1.7).ConclusionsThe method yields insights into the degree of exposure misclassification and appears to reduce attenuation bias due to misclassification of exposure while the estimated uncertainty increased.

scholarly journals Robust Bayesian Sensitivity Analysis for Case–Control Studies with Uncertain Exposure Misclassification Probabilities

2015 ◽  
Vol 11 (1) ◽  
Author(s):  
Timothy Shin Heng Mak ◽  
Nicky Best ◽  
Lesley Rushton
Keyword(s):  
Prior Distribution ◽  
Odds Ratio ◽  
Case Control ◽  
Sensitivity Analyses ◽  
Feasible Region ◽  
Exposure Misclassification ◽  
Case Control Studies ◽  
Unknown Parameters ◽  
Prior Distributions ◽  
Posterior Inference

AbstractExposure misclassification in case–control studies leads to bias in odds ratio estimates. There has been considerable interest recently to account for misclassification in estimation so as to adjust for bias as well as more accurately quantify uncertainty. These methods require users to elicit suitable values or prior distributions for the misclassification probabilities. In the event where exposure misclassification is highly uncertain, these methods are of limited use, because the resulting posterior uncertainty intervals tend to be too wide to be informative. Posterior inference also becomes very dependent on the subjectively elicited prior distribution. In this paper, we propose an alternative “robust Bayesian” approach, where instead of eliciting prior distributions for the misclassification probabilities, a feasible region is given. The extrema of posterior inference within the region are sought using an inequality constrained optimization algorithm. This method enables sensitivity analyses to be conducted in a useful way as we do not need to restrict all of our unknown parameters to fixed values, but can instead consider ranges of values at a time.

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