Wang Ming-Hsi, Shugart Yin Yao, Cole Stephen R, Platz Elizabeth A
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, and Department of Medicine, Saint Agnes Hospital, Room E6132, 615 North Wolfe Street, Baltimore, MD 21205, USA.
Cancer Epidemiol Biomarkers Prev. 2009 Mar;18(3):706-11. doi: 10.1158/1055-9965.EPI-08-0839. Epub 2009 Mar 3.
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.
在巢式病例对照研究中，发病密度抽样通常是用于对照抽样的偏差最小的有效方法。然而，在关于基因变异与前列腺癌进展的研究中，一些人认为对照组应从随访结束时未进展的男性中选取。因此，我们通过蒙特卡罗模拟，使用三种从前列腺癌男性队列中进行对照抽样的方法，检验了前列腺癌进展相对风险（RR）估计值的有效性。
利用约翰霍普金斯医院的前列腺癌进展率，针对基因型频率（10%、30%和50%）与关联度（RR，1.0、1.5和2.0）的九种组合情况进行数据模拟。使用三种对照抽样方法，即（a）发病密度抽样，（b）不放回已选对照的发病密度抽样，以及（c）“纯”对照抽样（即长期随访结束时未进展的男性），对嵌套于这九个队列情况中的病例对照研究的基因关联进行条件逻辑回归估计的RR，并与真实RR进行比较。
采用发病密度抽样选择的对照产生了无偏差的进展RR估计值。在我们的研究背景下，不放回的发病密度抽样仅产生了轻微偏差。相比之下，使用通过纯对照抽样选择的对照会产生有偏差的RR估计值，无关联情况除外；偏差程度随着关联度大小和随访时间的增加而增大。
旨在估计基因变异与前列腺癌进展风险关联的巢式病例对照研究应采用发病密度抽样，以提供有效的RR估计值。
