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Nested case-control data analysis using weighted conditional logistic regression in The Environmental Determinants of Diabetes in the Young (TEDDY) study: A novel approach
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July 31 2019
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Source: Diabetes Metab Res Rev. 36(1):e3204
Details:
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Alternative Title:Diabetes Metab Res Rev
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Personal Author:
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Corporate Authors:
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Description: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 the 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 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 non-random control selection was ignored in the analysis.
Results:
The TEDDY data analysis showed 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 β ignoring the non-random control selection tends to be greater than the true β (i.e., positive relative biases).
Conclusion:
Weighted conditional logistic regression can enhance the analysis by offering flexibility in the selection of controls, while maintaining the matching.
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Subjects:
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Source:
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Pubmed ID:31322810
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Pubmed Central ID:PMC6952534
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Document Type:
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Funding:U01 DK063836/DK/NIDDK NIH HHS/United States ; National Institute of Environmental Health Sciences (NIEHS)/ ; HHSN267200700014C/DK/NIDDK NIH HHS/United States ; U01 DK63790/National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)/ ; Centers for Disease Control and Prevention (CDC)/ ; ... More +
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Volume:36
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Issue:1
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File Type:
[PDF-529.75 KB]
