Comparison of nested case-control and survival analysis methodologies for analysis of time-dependent exposure

被引：195

作者：

Essebag V. ^{[1
,2
]}

Platt R.W. ^{[2
]}

Abrahamowicz M. ^{[2
,3
]}

Pilote L. ^{[2
,3
]}

机构：

[1] Division of Cardiology, Beth Israel Deaconess Medical Center, Harvard University, Boston, MA

[2] Dept. of Epidemiol. and Biostatist., McGill University, Montreal, Que.

[3] Division of Clinical Epidemiology, McGill University Health Center, Montreal, Que.

来源：

BMC Medical Research Methodology | / 5卷 / 1期

关键词：

Amiodarone; Central Processing Unit Time; Conditional Logistic Regression; Full Cohort; Pacemaker Insertion;

D O I：

10.1186/1471-2288-5-5

中图分类号：

学科分类号：

摘要：

Background: Epidemiological studies of exposures that vary with time require an additional level of methodological complexity to account for the time-dependence of exposure. This study compares a nested case-control approach for the study of time-dependent exposure with cohort analysis using Cox regression including time-dependent covariates. Methods: A cohort of 1340 subjects with four fixed and seven time-dependent covariates was used for this study. Nested case-control analyses were repeated 100 times for each of 4, 8, 16, 32, and 64 controls per case, and point estimates were compared to those obtained using Cox regression on the full cohort. Computational efficiencies were evaluated by comparing central processing unit times required for analysis of the cohort at sizes 1, 2, 4, 8, 16, and 32 times its initial size. Results: Nested case-control analyses yielded results that were similar to results of Cox regression on the full cohort. Cox regression was found to be 125 times slower than the nested case-control approach (using four controls per case). Conclusions: The nested case-control approach is a useful alternative for cohort analysis when studying time-dependent exposures. Its superior computational efficiency may be particularly useful when studying rare outcomes in databases, where the ability to analyze larger sample sizes can improve the power of the study. © 2005 Essebag et al; licensee BioMed Central Ltd.

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