Random control selection for conducting high-throughput adverse drug events screening using large-scale longitudinal health data.

ABSTRACT: Case-control design based high-throughput pharmacoinformatics study using large-scale longitudinal health data is able to detect new adverse drug event (ADEs) signals. Existing control selection approaches for case-control design included the dynamic/super control selection approach. The dynamic/super control selection approach requires all individuals to be evaluated at all ADE case index dates, as the individuals' eligibilities as control depend on ADE/enrollment history. Thus, using large-scale longitudinal health data, the dynamic/super control selection approach requires extraordinarily high computational time. We proposed a random control selection approach in which ADE case index dates were matched by randomly generated control index dates. The random control selection approach does

SUBMITTER: Chiang CW 

PROVIDER: S-EPMC8452297 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature