ArticleAnnals of epidemiology2019
The missing indicator approach for censored covariates subject to limit of detection in logistic regression models.
Article in Annals of epidemiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
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- The non-linear relationship between atherogenic index of plasma and stroke: a study among middle-aged and elderly Chinese adults with depression.Lipids in health and disease · 2025Article
- Association between inspired oxygen fraction and development of postoperative pulmonary complications in thoracic surgery: a multicentre retrospective cohort study.British journal of anaesthesia · 2024Observational
- A Comparison of Statistical Methods for Studying Interactions of Chemical Mixtures.Statistics in biosciences · 2024Article
- Accommodating detection limits of multiple exposures in environmental mixture analyses: an overview of statistical approaches.Environmental health : a global access science source · 2024Article
- An epidemiological introduction to human metabolomic investigations.Trends in endocrinology and metabolism: TEM · 2023Review
- Eating habit of adding salt to foods and incident sleep apnea: a prospective cohort study.Respiratory research · 2023Article
- Diagnosing Sport-Related Flow Limitations in the Iliac Arteries Using Near-Infrared Spectroscopy.Journal of clinical medicine · 2022Article
- The Influence of Hospital Urbanicity on Mortality in Patients With Acute Respiratory Failure: A National Cohort Retrospective Analysis.Respiratory care · 2021Article
- Measuring association among censored antibody titer data.Statistics in medicine · 2021Article
- New insights into modeling exposure measurements below the limit of detection.Environmental epidemiology (Philadelphia, Pa.) · 2021Article
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Authors and funding
3 authors.
Funding
Abstract
purposeIn several biomedical studies, one or more exposures of interest may be subject to nonrandom missingness because of the failure of the measurement assay at levels below its limit of detection. This issue is commonly encountered in studies of the metabolome using tandem mass spectrometry-based technologies. Owing to a large number of metabolites measured in these studies, preserving statistical power is of utmost interest. In this article, we evaluate the small sample properties of the missing indicator approach in logistic and conditional logistic regression models.
methodsFor nested case-control or matched case control study designs, we evaluate the bias, power, and type I error associated with the missing indicator method using simulation. We compare the missing indicator approach to complete case analysis and several imputation approaches.
resultsWe show that under a variety of settings, the missing indicator approach outperforms complete case analysis and other imputation approaches with regard to bias, mean squared error, and power.
conclusionsFor nested case-control and matched study designs of modest sample sizes, the missing indicator model minimizes loss of information and thus provides an attractive alternative to the oft-used complete case analysis and other imputation approaches.
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