A nested case–control (NCC) study is a type of case–control study drawn from a well-defined cohort, where exposure data is measured only for cases and selected controls. This design is more efficient and cost-effective than a full cohort study, especially when the exposure is difficult or expensive to measure, such as gene expression profiling. The NCC study is particularly useful when the outcome is rare or when biological samples are limited, reducing unnecessary resource use. By leveraging existing cohort data, it avoids the need for new data collection, enabling the use of specialized analysis methods that handle missing covariates effectively.
Example
As an example, of the 91,523 women in the Nurses' Health Study who did not have cancer at baseline and who were followed for 14 years, 2,341 women had developed breast cancer by 1993. Several studies have used standard cohort analyses to study precursors to breast cancer, e.g. use of hormonal contraceptives,3 which is a covariate easily measured on all of the women in the cohort. However, note that in comparison to the cases, there are so many controls that each particular control contributes relatively little information to the analysis.
If, on the other hand, one is interested in the association between gene expression and breast cancer incidence, it would be very expensive and possibly wasteful of precious blood specimen to assay all 89,000 women without breast cancer. In this situation, one may choose to assay all of the cases, and also, for each case, select a certain number of women to assay from the risk set of participants who have not yet failed (i.e. those who have not developed breast cancer before the particular case in question has developed breast cancer). The risk set is often restricted to those participants who are matched to the case on variables such as age, which reduces the variability of effect estimates.
Efficiency of the NCC model
Commonly 1–4 controls are selected for each case. Since the covariate is not measured for all participants, the nested case–control model is both less expensive than a full cohort analysis and more efficient than taking a simple random sample from the full cohort. However, it has been shown that with 4 controls per case and/or stratified sampling of controls, relatively little efficiency may be lost, depending on the method of estimation used.45
Analysis of nested case–control studies
The analysis of a nested case–control model must take into account the way in which controls are sampled from the cohort. Failing to do so, such as by treating the cases and selected controls as the original cohort and performing a logistic regression, which is common, can result in biased estimates whose null distribution is different from what is assumed. Ways to account for the random sampling include conditional logistic regression,6 and using inverse probability weighting to adjust for missing covariates among those who are not selected into the study.7
Case–cohort study
A case–cohort study is a design in which cases and controls are drawn from within a prospective study. All cases who developed the outcome of interest during the follow-up are selected and compared with a random sample of the cohort. This randomly selected control sample could, by chance, include some cases. Exposure is defined prior to disease development based on data collected at baseline or on assays conducted in biological samples collected at baseline.
Porta, Miquel (2014). A Dictionary of Epidemiology. Oxford: Oxford University Press.
Further reading
- Keogh, Ruth H.; Cox, D. R. (2014). "Nested case–control studies". Case–Control Studies. Cambridge University Press. pp. 160–190. ISBN 978-1-107-01956-0.
References
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Cai, Tianxi; Zheng, Yingye (2012). "Evaluating prognostic accuracy of biomarkers in nested case–control studies". Biostatistics. 13 (1): 89–100. doi:10.1093/biostatistics/kxr021. PMC 3276269. PMID 21856652. /wiki/Tianxi_Cai ↩
Hankinson SE; Colditz GA; Manson JE; Willett WC; Hunter DJ; Stampfer MJ; et al. (1997). "A prospective study of oral contraceptive use and risk of breast cancer (Nurses' Health Study, United States)". Cancer Causes Control. 8 (1): 65–72. doi:10.1023/a:1018435205695. PMID 9051324. S2CID 24873830. https://pubmed.ncbi.nlm.nih.gov/9051324 ↩
Cai, Tianxi; Zheng, Yingye (2012). "Evaluating prognostic accuracy of biomarkers in nested case–control studies". Biostatistics. 13 (1): 89–100. doi:10.1093/biostatistics/kxr021. PMC 3276269. PMID 21856652. /wiki/Tianxi_Cai ↩
Goldstein, Larry; Zhang, Haimeng (2009). "Efficiency of the maximum partial likelihood estimator for nested case control sampling". Bernoulli. 15 (2): 569–597. arXiv:0809.0445. doi:10.3150/08-bej162. JSTOR 20680165. S2CID 16589954. /wiki/ArXiv_(identifier) ↩
Borgan, O.; Goldstein, L.; Langholz, B. (1995). "Methods for the Analysis of Sampled Cohort Data in the Cox Proportional Hazards Model" (PDF). Annals of Statistics. 23 (5): 1749–1778. doi:10.1214/aos/1176324322. JSTOR 2242544. https://www.duo.uio.no/bitstream/10852/47861/1/1992-7.pdf ↩
Cai, Tianxi; Zheng, Yingye (2012). "Evaluating prognostic accuracy of biomarkers in nested case–control studies". Biostatistics. 13 (1): 89–100. doi:10.1093/biostatistics/kxr021. PMC 3276269. PMID 21856652. /wiki/Tianxi_Cai ↩