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Estimation of Relative and Absolute Risks in a Competing-Risks Setting Using a Nested Case-Control Study Design: Example From the ProMort Study
Clinical Epidemiology Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.
Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin, Turin, Italy ; Centro di Riferimento per l ’ Epidemiologia e la Prevenzione Oncologica (CPO) in Piemonte, Turin, Italy .
Unit of Biostatistics, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden .
Örebro University, School of Medical Sciences. Department of Urology.
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2019 (English)In: American Journal of Epidemiology, ISSN 0002-9262, E-ISSN 1476-6256, Vol. 188, no 6, p. 1165-1173Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Oxford University Press, 2019. Vol. 188, no 6, p. 1165-1173
Keywords [en]
absolute risk, competing risks, cumulative incidence function, flexible parametric survival model, inverse probability weighting, nested case-control studies, weighted likelihood
National Category
Cancer and Oncology Urology and Nephrology
Identifiers
URN: urn:nbn:se:oru:diva-75236DOI: 10.1093/aje/kwz026ISI: 000473760200020PubMedID: 30976789Scopus ID: 2-s2.0-85067089473OAI: oai:DiVA.org:oru-75236DiVA, id: diva2:1339055
Funder
Swedish Cancer SocietyAvailable from: 2019-07-25 Created: 2019-07-25 Last updated: 2019-07-25Bibliographically approved

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Andrén, OveCarlsson, JessicaDavidsson, Sabina

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