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- Title
Derivation and assessment of risk prediction models using case-cohort data.
- Authors
Sanderson, Jean; Thompson, Simon G.; White, Ian R.; Aspelund, Thor; Pennells, Lisa
- Abstract
Background: Case-cohort studies are increasingly used to quantify the association of novel factors with disease risk. Conventional measures of predictive ability need modification for this design. We show how Harrell's C-index, Royston's D, and the category-based and continuous versions of the net reclassification index (NRI) can be adapted. Methods: We simulated full cohort and case-cohort data, with sampling fractions ranging from 1% to 90%, using covariates from a cohort study of coronary heart disease, and two incidence rates. We then compared the accuracy and precision of the proposed risk prediction metrics. Results: The C-index and D must be weighted in order to obtain unbiased results. The NRI does not need modification, provided that the relevant non-subcohort cases are excluded from the calculation. The empirical standard errors across simulations were consistent with analytical standard errors for the C-index and D but not for the NRI. Good relative efficiency of the prediction metrics was observed in our examples, provided the sampling fraction was above 40% for the C-index, 60% for D, or 30% for the NRI. Stata code is made available. Conclusions: Case-cohort designs can be used to provide unbiased estimates of the C-index, D measure and NRI.
- Subjects
CORONARY disease; HEART diseases; TYPE A behavior; ACUTE coronary syndrome; ANGINA pectoris
- Publication
BMC Medical Research Methodology, 2013, Vol 13, Issue 1, p1
- ISSN
1471-2288
- Publication type
Article
- DOI
10.1186/1471-2288-13-113