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

Norah Alyabs and Sy Han Chiou
Additional contact information
Norah Alyabs: Department of Mathematical Sciences, The University of Texas at Dallas, Richardson, TX 75080, USA
Sy Han Chiou: Department of Mathematical Sciences, The University of Texas at Dallas, Richardson, TX 75080, USA

Stats, 2022, vol. 5, issue 2, 1-13

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.

Keywords: complete-case analysis; imputation; missing by design; substitution (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2571-905X/5/2/29/pdf (application/pdf)
https://www.mdpi.com/2571-905X/5/2/29/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jstats:v:5:y:2022:i:2:p:29-506:d:812524

Access Statistics for this article

Stats is currently edited by Mrs. Minnie Li

More articles in Stats from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().