IDEAS home Printed from https://ideas.repec.org/a/gam/jstats/v5y2022i2p29-506d812524.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-905X/5/2/29/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2571-905X/5/2/29/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    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. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Joost R. Ginkel, 2020. "Standardized Regression Coefficients and Newly Proposed Estimators for $${R}^{{2}}$$R2 in Multiply Imputed Data," Psychometrika, Springer;The Psychometric Society, vol. 85(1), pages 185-205, March.
    2. Gowri Gopalakrishna & Gerben ter Riet & Gerko Vink & Ineke Stoop & Jelte M Wicherts & Lex M Bouter, 2022. "Prevalence of questionable research practices, research misconduct and their potential explanatory factors: A survey among academic researchers in The Netherlands," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-16, February.
    3. Paolo Brunori & Pedro Salas-Rojo & Paolo Verme, 2022. "Estimating Inequality with Missing Incomes," Working Papers 616, ECINEQ, Society for the Study of Economic Inequality.
    4. Saeideh Kamgar & Florian Meinfelder & Ralf Münnich & Hamidreza Navvabpour, 2020. "Estimation within the new integrated system of household surveys in Germany," Statistical Papers, Springer, vol. 61(5), pages 2091-2117, October.
    5. Kristian Kleinke & Mark Stemmler & Jost Reinecke & Friedrich Lösel, 2011. "Efficient ways to impute incomplete panel data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(4), pages 351-373, December.
    6. Jana Emmenegger & Ralf Münnich & Jannik Schaller, 2022. "Evaluating Data Fusion Methods to Improve Income Modelling," Research Papers in Economics 2022-03, University of Trier, Department of Economics.
    7. Landau, E.R. & Raniti, M.B. & Blake, M. & Waloszek, J.M. & Blake, L. & Simmons, J.G. & Schwartz, O. & Murray, G. & Trinder, J. & Allen, N.B. & Byrne, M.L., 2021. "The ratio of morning cortisol to CRP prospectively predicts first-onset depression in at-risk adolescents," Social Science & Medicine, Elsevier, vol. 281(C).
    8. Lee, Min Cherng & Mitra, Robin, 2016. "Multiply imputing missing values in data sets with mixed measurement scales using a sequence of generalised linear models," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 24-38.
    9. Gessendorfer Jonathan & Beste Jonas & Drechsler Jörg & Sakshaug Joseph W., 2018. "Statistical Matching as a Supplement to Record Linkage: A Valuable Method to Tackle Nonconsent Bias?," Journal of Official Statistics, Sciendo, vol. 34(4), pages 909-933, December.
    10. Mehboob Ali & Göran Kauermann, 2021. "A split questionnaire survey design in the context of statistical matching," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(4), pages 1219-1236, October.
    11. Speidel, Matthias & Drechsler, Jörg & Jolani, Shahab, 2018. "R package hmi: a convenient tool for hierarchical multiple imputation and beyond," IAB-Discussion Paper 201816, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    12. Claramunt González, Juan & van Delden, Arnout & de Waal, Ton, 2023. "Assessment of the effect of constraints in a new multivariate mixed method for statistical matching," Computational Statistics & Data Analysis, Elsevier, vol. 177(C).
    13. Mingyang Cai & Gerko Vink, 2022. "A note on imputing squares via polynomial combination approach," Computational Statistics, Springer, vol. 37(5), pages 2185-2201, November.
    14. Noémi Kreif & Richard Grieve & Iván Díaz & David Harrison, 2015. "Evaluation of the Effect of a Continuous Treatment: A Machine Learning Approach with an Application to Treatment for Traumatic Brain Injury," Health Economics, John Wiley & Sons, Ltd., vol. 24(9), pages 1213-1228, September.
    15. François Gardes, 2021. "On the value of time and human life," Documents de travail du Centre d'Economie de la Sorbonne 21023, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    16. Abhilash Bandam & Eedris Busari & Chloi Syranidou & Jochen Linssen & Detlef Stolten, 2022. "Classification of Building Types in Germany: A Data-Driven Modeling Approach," Data, MDPI, vol. 7(4), pages 1-23, April.
    17. Boonstra Philip S. & Little Roderick J.A. & West Brady T. & Andridge Rebecca R. & Alvarado-Leiton Fernanda, 2021. "A Simulation Study of Diagnostics for Selection Bias," Journal of Official Statistics, Sciendo, vol. 37(3), pages 751-769, September.
    18. François Gardes, 2021. "A Solution to the Estimation of an Enlarged GDP Including Domestic Production: An Estimation on Micro Data," Post-Print halshs-03325362, HAL.
    19. Joost Ginkel & Pieter Kroonenberg, 2014. "Using Generalized Procrustes Analysis for Multiple Imputation in Principal Component Analysis," Journal of Classification, Springer;The Classification Society, vol. 31(2), pages 242-269, July.
    20. Peter ven de Ven & Anne Harrison & Barbara Fraumeni & Dennis Fixler & David Johnson & Andrew Craig & Kevin Furlong, 2017. "A Consistent Data Series to Evaluate Growth and Inequality in the National Accounts Note: The views expressed in this research, including those related to statistical, methodological, technical, or op," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 63, pages 437-459, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jstats:v:5:y:2022:i:2:p:29-506:d:812524. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.