ArticleLifetime data analysis2018
The competing risks Cox model with auxiliary case covariates under weaker missing-at-random cause of failure.
Article in Lifetime data analysis, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Article
- Semiparametric marginal regression for clustered competing risks data with missing cause of failure.Biostatistics (Oxford, England) · 2023Article
- Reflection on modern methods: causal inference considerations for heterogeneous disease etiology.International journal of epidemiology · 2021Article
- The microbiome, genetics, and gastrointestinal neoplasms: the evolving field of molecular pathological epidemiology to analyze the tumor-immune-microbiome interaction.Human genetics · 2021Review
- Analysis of the time-varying Cox model for the cause-specific hazard functions with missing causes.Lifetime data analysis · 2020Article
- Semiparametric regression and risk prediction with competing risks data under missing cause of failure.Lifetime data analysis · 2020Article
- Causal inference in the face of competing events.Current epidemiology reports · 2020Article
- Integration of microbiology, molecular pathology, and epidemiology: a new paradigm to explore the pathogenesis of microbiome-driven neoplasms.The Journal of pathology · 2019Review
- Insights into Pathogenic Interactions Among Environment, Host, and Tumor at the Crossroads of Molecular Pathology and Epidemiology.Annual review of pathology · 2019Review
- Missingness in the Setting of Competing Risks: from missing values to missing potential outcomes.Current epidemiology reports · 2018Article
- Integrative analysis of exogenous, endogenous, tumour and immune factors for precision medicine.Gut · 2018Review
- Utility of inverse probability weighting in molecular pathological epidemiology.European journal of epidemiology · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
In the analysis of time-to-event data with multiple causes using a competing risks Cox model, often the cause of failure is unknown for some of the cases. The probability of a missing cause is typically assumed to be independent of the cause given the time of the event and covariates measured before the event occurred. In practice, however, the underlying missing-at-random assumption does not necessarily hold. Motivated by colorectal cancer molecular pathological epidemiology analysis, we develop a method to conduct valid analysis when additional auxiliary variables are available for cases only. We consider a weaker missing-at-random assumption, with missing pattern depending on the observed quantities, which include the auxiliary covariates. We use an informative likelihood approach that will yield consistent estimates even when the underlying model for missing cause of failure is misspecified. The superiority of our method over naive methods in finite samples is demonstrated by simulation study results. We illustrate the use of our method in an analysis of colorectal cancer data from the Nurses' Health Study cohort, where, apparently, the traditional missing-at-random assumption fails to hold.
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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.