A Simulation Study of Control Sampling Methods for Nested Case-Control Studies of Genetic and Molecular Biomarkers and Prostate Cancer Progression

Abstract

Incidence density sampling is typically the least biased efficient method for control sampling in nested case-control studies. However, in studies of genetic variants and prostate cancer progression, some argue that controls should be sampled from men who did not progress by end of follow-up. Thus, we examined the validity of relative risk (RR) estimates of prostate cancer progression using three methods for control sampling from cohorts of men with prostate cancer generated by Monte Carlo simulation. Data were simulated for nine scenarios for combinations of genotype frequency (10%, 30%, and 50%) and association (RR, 1.0, 1.5, and 2.0) using prostate progression rates from Johns Hopkins Hospital. RRs estimated from conditional logistic regression for the genetic association from case-control studies nested in the nine cohort scenarios using three control sampling methods, (a) incidence density sampling, (b) incidence density sampling without replacement of selected controls, and (c) "pure" control sampling (i.e., men who did not progress by end of long-term follow-up), were compared with the true RRs. Use of controls selected by incidence density sampling produced unbiased RR estimates of progression. In our setting, only a slight bias was produced by use of incidence density sampling without replacement. In contrast, use of controls selected by pure control sampling produced biased RR estimates, except when there was no association; extent of bias increased with increasing size of the association and duration of follow-up. Nested case-control studies designed to estimate the association of genetic variants with risk of prostate cancer progression should use incidence density sampling to provide a valid RR estimate.