title: The value of reusing prior nested case-control data in new studies with different outcome.
abstract: Many epidemiological studies use a nested case-control (NCC) design to reduce cost while maintaining study power. However, because of the incidence density sampling used, reusing data from NCC studies for analysis of secondary outcomes is not straightforward. Recent methodological developments have opened the possibility for prior NCC data to be used to complement controls in a current study, thereby improving study efficiency. However, practical guidelines on the effectiveness of prior data relative to newly sampled subjects and the potential power gains are still lacking. Using simulated cohorts, we show in this paper how the efficiency of NCC studies that use a mixture of prior and newly sampled subjects depends on the number of newly sampled controls and prior subjects as well as the overlap in the distributions of the matching variables. We explore the feasibility and efficiency of a current study that gathers no controls, relying instead on prior data. Using the concept of effective number of controls, we show how researchers can assess the potential power gains from reusing prior data. We apply the method to analyses of anorexia and contralateral breast cancer in the Swedish population and show how power calculations can be done using publicly available software. This work has important applications in genetic and molecular epidemiology to make optimal use of costly exposure measurements.
authors.affiliation: Saw Swee Hock School of Public Health, National University of Singapore, Singapore.
authors.forename: Agus
authors.initials: A
authors.lastname: Salim
authors.forename: Qian
authors.initials: Q
authors.lastname: Yang
authors.forename: Marie
authors.initials: M
authors.lastname: Reilly
mesh_terms: D000328:Adult; D000855:Anorexia / Q000453:epidemiology; D001943:Breast Neoplasms / Q000453:epidemiology; D016022:Case-Control Studies; D003198:Computer Simulation / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000453:epidemiology; D008297:Male; D015233:Models, Statistical; D012559:Schizophrenia / Q000453:epidemiology; D012984:Software / Q000706:statistics & numerical data; D013548:Sweden / Q000453:epidemiology