Cumulative risk regression in case–cohort studies using pseudo-observations
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Cumulative risk regression in case–cohort studies using pseudo-observations. / Parner, Erik T.; Andersen, Per K.; Overgaard, Morten.
I: Lifetime Data Analysis, Bind 26, Nr. 4, 2020, s. 639-658.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Cumulative risk regression in case–cohort studies using pseudo-observations
AU - Parner, Erik T.
AU - Andersen, Per K.
AU - Overgaard, Morten
PY - 2020
Y1 - 2020
N2 - Case–cohort studies are useful when information on certain risk factors is difficult or costly to ascertain. Particularly, a case–cohort study may be well suited in situations where several case series are of interest, e.g. in studies with competing risks, because the same sub-cohort may serve as a comparison group for all case series. Previous analyses of this kind of sampled cohort data most often involved estimation of rate ratios based on a Cox regression model. However, with competing risks this method will not provide parameters that directly describe the association between covariates and cumulative risks. In this paper, we study regression analysis of cause-specific cumulative risks in case–cohort studies using pseudo-observations. We focus mainly on the situation with competing risks. However, as a by-product, we also develop a method by which absolute mortality risks may be analyzed directly from case–cohort survival data. We adjust for the case–cohort sampling by inverse sampling probabilities applied to a generalized estimation equation. The large-sample properties of the proposed estimator are developed and small-sample properties are evaluated in a simulation study. We apply the methodology to study the effect of a specific diet component and a specific gene on the absolute risk of atrial fibrillation.
AB - Case–cohort studies are useful when information on certain risk factors is difficult or costly to ascertain. Particularly, a case–cohort study may be well suited in situations where several case series are of interest, e.g. in studies with competing risks, because the same sub-cohort may serve as a comparison group for all case series. Previous analyses of this kind of sampled cohort data most often involved estimation of rate ratios based on a Cox regression model. However, with competing risks this method will not provide parameters that directly describe the association between covariates and cumulative risks. In this paper, we study regression analysis of cause-specific cumulative risks in case–cohort studies using pseudo-observations. We focus mainly on the situation with competing risks. However, as a by-product, we also develop a method by which absolute mortality risks may be analyzed directly from case–cohort survival data. We adjust for the case–cohort sampling by inverse sampling probabilities applied to a generalized estimation equation. The large-sample properties of the proposed estimator are developed and small-sample properties are evaluated in a simulation study. We apply the methodology to study the effect of a specific diet component and a specific gene on the absolute risk of atrial fibrillation.
KW - Case–cohort study
KW - Competing risks
KW - Cumulative incidence
KW - Cumulative risk
KW - Pseudo-observations
U2 - 10.1007/s10985-020-09492-3
DO - 10.1007/s10985-020-09492-3
M3 - Journal article
C2 - 31933047
AN - SCOPUS:85077840745
VL - 26
SP - 639
EP - 658
JO - Lifetime Data Analysis
JF - Lifetime Data Analysis
SN - 1380-7870
IS - 4
ER -
ID: 248847722