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Regularized regression for two phase failure time studies

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  • Soave, David
  • Lawless, Jerald F.

Abstract

Two-phase study designs are ideal for focused sub-studies based on large prospective cohorts when the outcome of interest is an event that is rare in the full cohort, and additional covariates are expensive or difficult to measure. Researchers often wish to examine large numbers of covariates for association with outcomes of interest. In the context of cancer, hundreds to millions of genetic markers may be considered, along with environmental exposures. A computationally efficient variable selection method is proposed for two-phase failure time studies with stratified sampling under the Cox proportional hazards model. The penalized estimator is obtained from a penalized (weighted) Cox log partial likelihood using a pathwise cyclical coordinate descent algorithm which is scalable for high dimensional datasets where the number of features is much larger than the sample size (p≫n). A detailed simulation study to examine the performance of the proposed methodology is described. The variable selection and estimation procedure is then used to obtain a model for predicting acute myeloid leukaemia using somatic stem cell mutation profiles derived from blood samples, based on a two-phase sample from the European Prospective Investigation into Cancer and Nutrition (EPIC) study.

Suggested Citation

  • Soave, David & Lawless, Jerald F., 2023. "Regularized regression for two phase failure time studies," Computational Statistics & Data Analysis, Elsevier, vol. 182(C).
    • Handle: RePEc:eee:csdana:v:182:y:2023:i:c:s0167947323000142
    DOI: 10.1016/j.csda.2023.107703
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    References listed on IDEAS

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    1. J. F. Lawless, 2018. "Two-phase outcome-dependent studies for failure times and testing for effects of expensive covariates," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(1), pages 28-44, January.
    2. Ai Ni & Jianwen Cai & Donglin Zeng, 2016. "Variable selection for case-cohort studies with failure time outcome," Biometrika, Biometrika Trust, vol. 103(3), pages 547-562.
    3. Simon, Noah & Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2011. "Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 39(i05).
    4. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    5. Sagi Abelson & Grace Collord & Stanley W. K. Ng & Omer Weissbrod & Netta Mendelson Cohen & Elisabeth Niemeyer & Noam Barda & Philip C. Zuzarte & Lawrence Heisler & Yogi Sundaravadanam & Robert Luben &, 2018. "Prediction of acute myeloid leukaemia risk in healthy individuals," Nature, Nature, vol. 559(7714), pages 400-404, July.
    6. Sven Ove Samuelsen & Hallvard Ånestad & Anders Skrondal, 2007. "Stratified Case‐Cohort Analysis of General Cohort Sampling Designs," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(1), pages 103-119, March.
    7. Dandan Liu & Tianxi Cai & Yingye Zheng, 2012. "Evaluating the Predictive Value of Biomarkers with Stratified Case-Cohort Design," Biometrics, The International Biometric Society, vol. 68(4), pages 1219-1227, December.
    8. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    9. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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