Weighted Cox regression models to correct for outcome dependent sampling in clustered survival data: Simulation study and application to familiar cancer studies

Ascertainment bias is common in genetic-epidemiological cancer studies, where sampling of high-risk families is outcome-dependent. This results in too many events in comparison to the population and an overrepresentation of young, affected subjects in the sample. The motivating example for this thesis is a family study where the goal is to estimate an unbiased hazard ratio (HR) for the effect of Polygenic Risk Score (PRS), a continuous score based on several Single Nucleotide Polymorphisms (SNPs), on age of breast cancer diagnosis. Weighted Cox model approaches have been proposed in this context, however their performance has never been evaluated for a continuous covariate. Two different approaches were considered, using time fixed and time dependent weights. A simulation study was conducted to assess the performance of the different approaches for scenarios where different family correlation, family size, sample size and selection criterium have been chosen. We found that under the...Show moreAscertainment bias is common in genetic-epidemiological cancer studies, where sampling of high-risk families is outcome-dependent. This results in too many events in comparison to the population and an overrepresentation of young, affected subjects in the sample. The motivating example for this thesis is a family study where the goal is to estimate an unbiased hazard ratio (HR) for the effect of Polygenic Risk Score (PRS), a continuous score based on several Single Nucleotide Polymorphisms (SNPs), on age of breast cancer diagnosis. Weighted Cox model approaches have been proposed in this context, however their performance has never been evaluated for a continuous covariate. Two different approaches were considered, using time fixed and time dependent weights. A simulation study was conducted to assess the performance of the different approaches for scenarios where different family correlation, family size, sample size and selection criterium have been chosen. We found that under the null hypothesis, (un)weighted models behave similarly. When a covariate effect is assumed, in any scenario where the within-family correlation is low, weighting methods perform better than a naive approach; the same holds for moderate within-family correlation in combination with weak ascertainment. For strong ascertainment and/or strong within-family correlation, coverage of weighting methods is very poor and bias is high. To obtain an unbiased HR for PRS, we used high-risk breast cancer families data. Inclusion criteria were absence of high-risk mutations BRCA1 and BRCA2 and at least three affected female family members or in two members if at least one had bilateral breast cancer before age 60. A total of 101 families were selected between 1990 and 2012 by Clinical Genetic Services in four Dutch cities and one Hungarian city, with 323 (55.1%) events. The HR of PRS, adjusted by family history, was 1.29 (95% CI 1.04; 1.60), for the naive model, with a frailty variance of 0.53 which indicates rather strong within-family correlation. For none of the weighting approaches, the covariate effect of PRS adjusted for family history in a Cox model was significant (HR 1.09 and 1.09). For analysis of outcome dependently sampled survival data, weighting approaches may be used to limit ascertainment bias, for some scenarios. A note of caution is required when this approach is used in scenarios with (moderate to) strong within-family correlation. No evidence for a significant effect of PRS on age of breast cancer diagnosis was found in this studyShow less

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Weighted Cox regression models to correct for outcome dependent sampling in clustered survival data: Simulation study and application to familiar cancer studies