ArticleAmerican journal of epidemiology2019
Estimation of Relative and Absolute Risks in a Competing-Risks Setting Using a Nested Case-Control Study Design: Example From the ProMort Study.
Article in American journal of epidemiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Article
- Prediction of transition probabilities in multi-state models with nested case-control data.Biometrics · 2025Article
- Absolute risk from double nested case-control designs: cause-specific proportional hazards models with and without augmented estimating equations.Biometrics · 2024Article
- Weighted metrics are required when evaluating the performance of prediction models in nested case-control studies.BMC medical research methodology · 2024Article
- Cohort profile: the Turin prostate cancer prognostication (TPCP) cohort.Frontiers in oncology · 2023Article
- Interchangeability of light and virtual microscopy for histopathological evaluation of prostate cancer.Scientific reports · 2021Article
- ERRATUM: "ESTIMATION OF RELATIVE AND ABSOLUTE RISKS IN A COMPETING-RISKS SETTING USING A NESTED CASE-CONTROL STUDY DESIGN: EXAMPLE FROM THE PROMORT STUDY".American journal of epidemiology · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, we describe the Prognostic Factors for Mortality in Prostate Cancer (ProMort) study and use it to demonstrate how the weighted likelihood method can be used in nested case-control studies to estimate both relative and absolute risks in the competing-risks setting. ProMort is a case-control study nested within the National Prostate Cancer Register (NPCR) of Sweden, comprising 1,710 men diagnosed with low- or intermediate-risk prostate cancer between 1998 and 2011 who died from prostate cancer (cases) and 1,710 matched controls. Cause-specific hazard ratios and cumulative incidence functions (CIFs) for prostate cancer death were estimated in ProMort using weighted flexible parametric models and compared with the corresponding estimates from the NPCR cohort. We further drew 1,500 random nested case-control subsamples of the NPCR cohort and quantified the bias in the hazard ratio and CIF estimates. Finally, we compared the ProMort estimates with those obtained by augmenting competing-risks cases and by augmenting both competing-risks cases and controls. The hazard ratios for prostate cancer death estimated in ProMort were comparable to those in the NPCR. The hazard ratios for dying from other causes were biased, which introduced bias in the CIFs estimated in the competing-risks setting. When augmenting both competing-risks cases and controls, the bias was reduced.
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