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Resampling Procedures for Making Inference Under Nested Case--Control Studies

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  • Tianxi Cai
  • Yingye Zheng

Abstract

The nested case--control (NCC) design has been widely adopted as a cost-effective solution in many large cohort studies for risk assessment with expensive markers, such as the emerging biologic and genetic markers. To analyze data from NCC studies, conditional logistic regression and maximum likelihood-based methods have been proposed. However, most of these methods either cannot be easily extended beyond the Cox model or require additional modeling assumptions. More generally applicable approaches based on inverse probability weighting (IPW) have been proposed as useful alternatives. However, due to the complex correlation structure induced by repeated finite risk set sampling, interval estimation for such IPW estimators remain challenging especially when the estimation involves nonsmooth objective functions or when making simultaneous inferences about functions. Standard resampling procedures such as the bootstrap cannot accommodate the correlation and thus are not directly applicable. In this article, we propose a resampling procedure that can provide valid estimates for the distribution of a broad class of IPW estimators. Simulation results suggest that the proposed procedures perform well in settings when analytical variance estimator is infeasible to derive or gives less optimal performance. The new procedures are illustrated with data from the Framingham Offspring Study to characterize individual level cardiovascular risks over time based on the Framingham risk score, C-reactive protein, and a genetic risk score. Supplementary materials for this article are available online.

Suggested Citation

  • Tianxi Cai & Yingye Zheng, 2013. "Resampling Procedures for Making Inference Under Nested Case--Control Studies," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(504), pages 1532-1544, December.
  • Handle: RePEc:taf:jnlasa:v:108:y:2013:i:504:p:1532-1544
    DOI: 10.1080/01621459.2013.856715
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    Citations

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    Cited by:

    1. Rebecca Payne & Ming Yang & Yingye Zheng & Majken K. Jensen & Tianxi Cai, 2016. "Robust risk prediction with biomarkers under two‐phase stratified cohort design," Biometrics, The International Biometric Society, vol. 72(4), pages 1037-1045, December.
    2. Peng Jin & Anne Zeleniuch-Jacquotte & Mengling Liu, 2020. "Generalized mean residual life models for case-cohort and nested case-control studies," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(4), pages 789-819, October.
    3. Jon Arni Steingrimsson & Robert L. Strawderman, 2017. "Estimation in the Semiparametric Accelerated Failure Time Model With Missing Covariates: Improving Efficiency Through Augmentation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 1221-1235, July.
    4. Weining Shen & Jing Ning & Ying Yuan & Anna S. Lok & Ziding Feng, 2018. "Model†free scoring system for risk prediction with application to hepatocellular carcinoma study," Biometrics, The International Biometric Society, vol. 74(1), pages 239-248, March.
    5. J. Feifel & D. Dobler, 2021. "Dynamic inference in general nested case‐control designs," Biometrics, The International Biometric Society, vol. 77(1), pages 175-185, March.
    6. Jiayin Zheng & Yingye Zheng & Li Hsu, 2022. "Re‐calibrating pure risk integrating individual data from two‐phase studies with external summary statistics," Biometrics, The International Biometric Society, vol. 78(4), pages 1515-1529, December.

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