ArticleCurrent epidemiology reports2018
Missingness in the Setting of Competing Risks: from missing values to missing potential outcomes.
Article in Current epidemiology reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Decreased Alcohol Consumption in an Implementation Study of Computerized Brief Intervention among HIV Patients in Clinical Care.AIDS and behavior · 2021Trial
- Pregnancy identification method as a source of bias in studies of prenatal exposures using real-world data.American journal of epidemiology · 2026Article
- Predicting hypotension, syncope, and fracture risk in patients indicated for antihypertensive treatment: the STRATIFY models.Nature communications · 2025Article
- Multiple Imputation of Missing Covariates When Using the Fine-Gray Model.Statistics in medicine · 2025Article
- Multiple imputation with competing risk outcomes.Computational statistics · 2025Article
- Article
- Development and external validation of a risk prediction model for falls in patients with an indication for antihypertensive treatment: retrospective cohort study.BMJ (Clinical research ed.) · 2022Article
- Multiple imputation for cause-specific Cox models: Assessing methods for estimation and prediction.Statistical methods in medical research · 2022Article
- Changing Patterns of Alcohol Use and Probability of Unsuppressed Viral Load Among Treated Patients with HIV Engaged in Routine Care in the United States.AIDS and behavior · 2021Article
- Causal inference in the face of competing events.Current epidemiology reports · 2020Article
- Association of History of Injection Drug Use with External Cause-Related Mortality Among Persons Linked to HIV Care in an Urban Clinic, 2001-2015.AIDS and behavior · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
purpose of reviewThe setting of competing risks in which there is an event that precludes the event of interest from occurring is prevalent in epidemiological research. Unless studying all-cause mortality, any study following up individuals is subject to having a competing risk should individuals die during time period that the study covers. While there are prior papers discussing the need for competing risk methods in epidemiologic research, we are not aware of any review that discusses issues of missing data in a competing risk setting. RECENT
findingsWe provide an overview of causal inference in competing risks as potential outcomes are missing, provide some strategies in dealing with missing (or misclassified) event type, and missing covariate data in competing risks. The strategies presented are specifically focused on those that may easily be implemented in standard statistical packages. There is ongoing work in terms of causal analyses, dealing with missing event type information, and missing covariate values specific to competing risk analyses. SUMMARY: Competing events are common in epidemiologic research. While there has been a focus on why one should conduct a proper competing risk analysis, a perhaps unrecognized issue is in terms of missingness. Strategies exist to minimize the impact of missingness in analyses of competing risks.
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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.