ArticleSri Lankan journal of applied statistics
The Use of Multiple Imputation for Data Subject to Limits of Detection.
Article in Sri Lankan journal of applied statistics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
20 citing papers in PubMed.
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- Sociodemographic predictors of PFAS exposure among a combined sample of U.S. pregnant women: an Environmental influences on Child Health Outcomes (ECHO) public-use dataset analysis.Journal of exposure science & environmental epidemiology · 2026Article
- Maternal characteristics are associated with human milk anti-inflammatory proteins in two populations.Scientific reports · 2024Article
- Factors associated with dementia-related stigma in British adolescents.BMC public health · 2024Article
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- Chronic Stress Related to Cancer Incidence, including the Role of Metabolic Syndrome Components.Cancers · 2024Article
- Total Effective Xenoestrogen Burden in Serum Samples and Risk of Endometrial Cancer in the Spanish Screenwide Case-Control Study.Environmental health perspectives · 2024Article
- Addressing Multiple Detection Limits with Semiparametric Cumulative Probability Models.Journal of the American Statistical Association · 2024Article
- Factors associated with dementia attitudes in an adolescent cohort: Structural Equation Modelling.Cogent psychology · 2023Article
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- Multiparametric Quantitative Imaging Biomarker as a Multivariate Descriptor of Health: A Roadmap.Academic radiology · 2023Review
- Impacts of dietary exposure to pesticides on faecal microbiome metabolism in adult twins.Environmental health : a global access science source · 2022Article
- Visually guided preprocessing of bioanalytical laboratory data using an interactive R notebook (pguIMP).CPT: pharmacometrics & systems pharmacology · 2021Article
- Association between urinary per- and poly-fluoroalkyl substances and COVID-19 susceptibility.Environment international · 2021Article
- Urinary total arsenic and arsenic methylation capacity in pregnancy and gestational diabetes mellitus: A case-control study.Chemosphere · 2021Article
- Excretion of Heavy Metals and Glyphosate in Urine and Hair Before and After Long-Term Fasting in Humans.Frontiers in nutrition · 2021Article
- Dietary Sources of Melamine Exposure among US Children and Adults in the National Health and Nutrition Examination Survey 2003-2004.Nutrients · 2020Article
- Effect of Weather on the Die-Off of Escherichia coli and Attenuated Salmonella enterica Serovar Typhimurium on Preharvest Leafy Greens following Irrigation with Contaminated Water.Applied and environmental microbiology · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Missing data due to limit of detection and limit of quantification is a common obstacle in epidemiological and biomedical research. We are interested in methodologies that provide unbiased and efficient estimates of these missing data while using popular statistical software. We describe a multiple imputation (MI) procedure for cross-sectional and longitudinal data which examines the sources of variation of hormones levels throughout the menstrual cycle conditional on specific biomarkers. We describe the rational, procedure, advantages and disadvantages of the multiple imputation procedure. We also provide a comparison to commonly used missing data procedures (complete cases analysis and single imputation). We illustrate our approach using the BioCycle data where we are interested in the effects of Vitamin E and Beta-carotene on Progesterone levels. We also evaluate the longitudinal impact of changes in Vitamin E on Progesterone levels over time. Finaly, we demonstrate the advantages of using MI over complete case analysis or naive single replacement in both cross-sectional and longitudinal analysis where measurements below the limit of quantification (LOQ) are unreported. We also illustrate that if available, inclusion of potentially demined unreliable data below the limit of detection (LOD) improves simple estimation substantially.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.