Dataset Information

Nested case-control data analysis using weighted conditional logistic regression in The Environmental Determinants of Diabetes in the Young (TEDDY) study: A novel approach.

ABSTRACT:

Background

A nested case-control (NCC) design within a prospective cohort study can realize substantial benefits for biomarker studies. In this context, it is natural to consider the sample availability in the selection of controls to minimize data loss when implementing the design. However, this violates the randomness required for selection, and it leads to biased analyses. An inverse probability weighting may improve the analysis, but the current approach using weighted Cox regression fails to maintain the benefits of NCC design.

Methods

This paper introduces weighted conditional logistic regression. We illustrate our proposed analysis using data recently investigated in The Environmental Determinants of Diabetes in the Young (TEDDY). Considering the potential data loss, the TEDDY NCC design was moderately selective in its selection of controls. A data-driven simulation study was performed to present the bias correction when a nonrandom control selection was ignored in the analysis.

Results

The TEDDY data analysis showed that the standard analysis using conditional logistic regression estimated the parameter: -0.015 (-0.023, -0.007). The biased estimate using Cox regression was -0.011 (95% confidence interval: -0.019, -0.003). Weighted Cox regression estimated -0.013 (-0.026, 0.0004). The proposed weighted conditional logistic regression estimated -0.020 (-0.033, -0.007), showing a stronger negative effect size than the one using conditional logistic regression. The simulation study also showed that the standard estimate of ? ignoring the nonrandom control selection tends to be greater than the true ? (ie, positive relative biases).

Conclusion

Weighted conditional logistic regression can enhance the analysis by offering flexibility in the selection of controls, while maintaining the matching.

SUBMITTER: Lee HS

PROVIDER: S-EPMC6952534 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Publications

Nested case-control data analysis using weighted conditional logistic regression in The Environmental Determinants of Diabetes in the Young (TEDDY) study: A novel approach.

Lee Hye-Seung HS Lynch Kristian F KF Krischer Jeffrey P JP

Diabetes/metabolism research and reviews 20190731 1

<h4>Background</h4>A nested case-control (NCC) design within a prospective cohort study can realize substantial benefits for biomarker studies. In this context, it is natural to consider the sample availability in the selection of controls to minimize data loss when implementing the design. However, this violates the randomness required for selection, and it leads to biased analyses. An inverse probability weighting may improve the analysis, but the current approach using weighted Cox regression ...[more]

PMID: 31322810

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Project description:ObjectiveTo assess parents' opinions about their participation in the longitudinal, multicenter study - The Environmental Determinants of Diabetes in the Young (TEDDY) consortium.MethodsA survey was given to parents who had been in the study for ≥ 1 year. Parents rated the importance of different reasons for staying in TEDDY and how well different study components were working. Parents were also asked if they had suggestions for making TEDDY better and if they ever had thought of leaving TEDDY and if so, why.ResultsOut of the 3336 eligible families, 2000 completed the survey (59.1%); most (77.6%) were mothers. Survey completion was more common in European than US TEDDY sites and was associated with greater maternal education, more accurate perceptions about their child's risk of type 1 diabetes, longer participation in TEDDY and excellent attendance at TEDDY visits. "Having someone watching the child for development of T1DM" was most important reason given for staying in the study; other important reasons included "Helping science discover causes of diabetes" and "Getting child's antibody results". Most parents were very satisfied with the different components of TEDDY and had not thought of leaving the study. A minority (24%) of parents acknowledged some thoughts of leaving TEDDY and cited the blood draws, being too busy/not having enough time, the demanding protocol, and food diaries as their reasons for considering leaving.ConclusionsThe study highlights factors important for successful implementation of demanding, longitudinal protocols. Friendly, devoted, skilled and knowledgeable staff with continuity makes the family comfortable. Keeping parents involved and informed on study progress is essential as is making procedures as smooth and painless as possible. Although the study is international the survey results were convergent across countries suggesting that the results have relevance to other similar studies to retain study participants.

Project description:BackgroundINTEROCC is a seven-country cohort study of occupational exposures and brain cancer risk, including occupational exposure to electromagnetic fields (EMF). In the absence of data on individual exposures, a Job Exposure Matrix (JEM) may be used to construct likely exposure scenarios in occupational settings. This tool was constructed using statistical summaries of exposure to EMF for various occupational categories for a comparable group of workers.MethodsIn this study, we use the Canadian data from INTEROCC to determine the best EMF exposure surrogate/estimate from three appropriately chosen surrogates from the JEM, along with a fourth surrogate based on Berkson error adjustments obtained via numerical approximation of the likelihood function. In this article, we examine the case in which exposures are gamma-distributed for each occupation in the JEM, as an alternative to the log-normal exposure distribution considered in a previous study conducted by our research team. We also study using those surrogates and the Berkson error adjustment in Poisson regression and conditional logistic regression.ResultsSimulations show that the introduced methods of Berkson error adjustment for non-stratified analyses provide accurate estimates of the risk of developing tumors in case of gamma exposure model. Alternatively, and under some technical assumptions, the arithmetic mean is the best surrogate when a gamma-distribution is used as an exposure model. Simulations also show that none of the present methods could provide an accurate estimate of the risk in case of stratified analyses.ConclusionWhile our previous study found the geometric mean to be the best exposure surrogate, the present study suggests that the best surrogate is dependent on the exposure model; the arithmetic means in case of gamma-exposure model and the geometric means in case of log-normal exposure model. However, we could present a better method of Berkson error adjustment for each of the two exposure models. Our results provide useful guidance on the application of JEMs for occupational exposure assessments, with adjustment for Berkson error.

Project description:Conditional logistic regression (CLR) is widely used to analyze habitat selection and movement of animals when resource availability changes over space and time. Observations used for these analyses are typically autocorrelated, which biases model-based variance estimation of CLR parameters. This bias can be corrected using generalized estimating equations (GEE), an approach that requires partitioning the data into independent clusters. Here we establish the link between clustering rules in GEE and their effectiveness to remove statistical biases in variance estimation of CLR parameters. The current lack of guidelines is such that broad variation in clustering rules can be found among studies (e.g., 14-450 clusters) with unknown consequences on the robustness of statistical inference. We simulated datasets reflecting conditions typical of field studies. Longitudinal data were generated based on several parameters of habitat selection with varying strength of autocorrelation and some individuals having more observations than others. We then evaluated how changing the number of clusters impacted the effectiveness of variance estimators. Simulations revealed that 30 clusters were sufficient to get unbiased and relatively precise estimates of variance of parameter estimates. The use of destructive sampling to increase the number of independent clusters was successful at removing statistical bias, but only when observations were temporally autocorrelated and the strength of inter-individual heterogeneity was weak. GEE also provided robust estimates of variance for different magnitudes of unbalanced datasets. Our simulations demonstrate that GEE should be estimated by assigning each individual to a cluster when at least 30 animals are followed, or by using destructive sampling for studies with fewer individuals having intermediate level of behavioural plasticity in selection and temporally autocorrelated observations. The simulations provide valuable information to build reliable habitat selection and movement models that allow for robustness of statistical inference without removing excessive amounts of ecological information.

Dataset Information

Nested case-control data analysis using weighted conditional logistic regression in The Environmental Determinants of Diabetes in the Young (TEDDY) study: A novel approach.

Background

Methods

Results

Conclusion

Publications

Nested case-control data analysis using weighted conditional logistic regression in The Environmental Determinants of Diabetes in the Young (TEDDY) study: A novel approach.

Similar Datasets

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