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The Missing Indicator Approach for Accelerated Failure Time Model with Covariates Subject to Limits of Detection

Author

Listed:
  • Norah Alyabs

    (Department of Mathematical Sciences, The University of Texas at Dallas, Richardson, TX 75080, USA
    College of Sciences and Theoretical Studies, Saudi Electronic University, Riyadh 13316, Saudi Arabia)

  • Sy Han Chiou

    (Department of Mathematical Sciences, The University of Texas at Dallas, Richardson, TX 75080, USA)

Abstract

The limit of detection (LOD) is commonly encountered in observational studies when one or more covariate values fall outside the measuring ranges. Although the complete-case (CC) approach is widely employed in the presence of missing values, it could result in biased estimations or even become inapplicable in small sample studies. On the other hand, approaches such as the missing indicator (MDI) approach are attractive alternatives as they preserve sample sizes. This paper compares the effectiveness of different alternatives to the CC approach under different LOD settings with a survival outcome. These alternatives include substitution methods, multiple imputation (MI) methods, MDI approaches, and MDI-embedded MI approaches. We found that the MDI approach outperformed its competitors regarding bias and mean squared error in small sample sizes through extensive simulation.

Suggested Citation

  • Norah Alyabs & Sy Han Chiou, 2022. "The Missing Indicator Approach for Accelerated Failure Time Model with Covariates Subject to Limits of Detection," Stats, MDPI, vol. 5(2), pages 1-13, May.
    • Handle: RePEc:gam:jstats:v:5:y:2022:i:2:p:29-506:d:812524
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    References listed on IDEAS

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    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. Bernhardt, Paul W. & Wang, Huixia Judy & Zhang, Daowen, 2014. "Flexible modeling of survival data with covariates subject to detection limits via multiple imputation," Computational Statistics & Data Analysis, Elsevier, vol. 69(C), pages 81-91.
    3. Rubin, Donald B, 1986. "Statistical Matching Using File Concatenation with Adjusted Weights and Multiple Imputations," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 87-94, January.
    4. Jing Qian & Sy Han Chiou & Jacqueline E. Maye & Folefac Atem & Keith A. Johnson & Rebecca A. Betensky, 2018. "Threshold regression to accommodate a censored covariate," Biometrics, The International Biometric Society, vol. 74(4), pages 1261-1270, December.
    5. Shengchun Kong & Bin Nan, 2016. "Semiparametric approach to regression with a covariate subject to a detection limit," Biometrika, Biometrika Trust, vol. 103(1), pages 161-174.
    6. Lee, Sungim & Park, S. H. & Park, Jinho, 2003. "The proportional hazards regression with a censored covariate," Statistics & Probability Letters, Elsevier, vol. 61(3), pages 309-319, February.
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