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Analysis of incidence and prognosis from 'extreme' case-control designs
La Trobe University, Melbourne Vic, Australia.
Saw Swee Hock School of Public Health, National University, Singapore, Singapore.
Örebro University, School of Health and Medical Sciences, Örebro University, Sweden. (Clinical Epidemiology and Biostatistics)ORCID iD: 0000-0002-3649-2639
Örebro University, School of Health and Medical Sciences, Örebro University, Sweden. Örebro University Hospital. (Clinical Epidemiology and Biostatistics)
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2014 (English)In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 33, no 30, p. 5388-5398Article in journal (Refereed) Published
Abstract [en]

The significant investment in measuring biomarkers has prompted investigators to improve cost-efficiency by sub-sampling in non-standard study designs. For example, investigators studying prognosis may assume that any differences in biomarkers are likely to be most apparent in an extreme sample of the earliest deaths and the longest-surviving controls. Simple logistic regression analysis of such data does not exploit the information available in the survival time, and statistical methods that model the sampling scheme may be more efficient. We derive likelihood equations that reflect the complex sampling scheme in unmatched and matched extreme' case-control designs. We investigated the performance and power of the method in simulation experiments, with a range of underlying hazard ratios and study sizes. Our proposed method resulted in hazard ratio estimates close to those obtained from the full cohort. The standard error estimates also performed well when compared with the empirical variance. In an application to a study investigating markers for lethal prostate cancer, an extreme case-control sample of lethal cases and the longest-surviving controls provided estimates of the effect of Gleason score in close agreement with analysis of all the data. By using the information in the sampling design, our method enables efficient and valid estimation of the underlying hazard ratio from a study design that is intuitive and easily implemented.

Place, publisher, year, edition, pages
Wiley-Blackwell, 2014. Vol. 33, no 30, p. 5388-5398
Keywords [en]
weighted likelihood, matched design, Cox proportional hazards model, baseline hazard, Kaplan-Meier, logistic regression
National Category
Public Health, Global Health, Social Medicine and Epidemiology
Identifiers
URN: urn:nbn:se:oru:diva-41124DOI: 10.1002/sim.6245ISI: 000346055000014PubMedID: 24980445Scopus ID: 2-s2.0-84918500310OAI: oai:DiVA.org:oru-41124DiVA, id: diva2:779702
Funder
Swedish Cancer Society, 11 0343Available from: 2015-01-13 Created: 2015-01-13 Last updated: 2018-07-23Bibliographically approved

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Fall, KatjaAndrén, Ove

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School of Health and Medical Sciences, Örebro University, SwedenÖrebro University Hospital
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