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Title:
Derivation and assessment of risk prediction models using case-cohort data | BMC Medical Research Methodology
Description:
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.
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Keywords {🔍}

casecohort, risk, cohort, data, full, nri, sampling, subcohort, article, model, methods, prediction, weighted, study, measures, estimates, figure, google, scholar, bias, pubmed, results, cindex, cases, weighting, absolute, predictive, standard, designs, models, ability, fractions, predictions, design, studies, incidence, events, fraction, measure, prentice, hazard, time, disease, individuals, additional, discrimination, continuous, empirical, analytical, applied,

Topics {✒️}

uk/ceu/research/erfc/stata open access article 𝑥 ip 𝑇 pre-publication history nested case–control design article download pdf nested case–control designs case-cohort data deal pool study-specific estimates long-term risk prediction case-cohort sampling design cardiovascular disease prediction 𝑧 𝑖 𝐶 unstratified case-cohort designs case-cohort weighted estimates weighted case-cohort estimates strangeways research laboratory author information authors clinical prediction models case-cohort weighted methods google scholar case-cohort weighted estimate case-cohort weighting methods cardiovascular registry maastricht main content log coronary heart disease log hazard ratio lisa pennells category-based nri shows case-cohort methods compared 𝑥 𝑖 2 cardiometabolic risk factors full size image lipid-related markers dr stephen kaptoge privacy choices/manage cookies influence care decisions disease prevention trials low sampling fractions shows good agreement 10-year risk predictions net reclassification improvement risk model derivation full cohort size log hazard ratios year absolute risk case-case comparisons case-cohort sampling case–control comparisons sampling fractions ranging

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Jonsdottir LS, Sigfusson N, Gudnason V, Sigvaldason H, Thorgeirsson G: Do lipids, blood pressure, diabetes, and smoking confer equal risk of myocardial infarction in women as in men?

Schema {🗺️}

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      description: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. 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. 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. Case-cohort designs can be used to provide unbiased estimates of the C-index, D measure and NRI.
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         Medicine
         Health Sciences
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