Increasing efficiency in case-cohort designs for recurrent events analysis

Music City Center 

In many large cohort epidemiological studies, the case-cohort design has been employed to combat the issue of rare events and to save time, cost, and valuable biological samples. In this work, we will develop case-cohort methodology for multiple endpoints and specifically the recurrent events setting, such as the survival of multiple hip replacements for the same patient. To better handle analysis in recurrent event settings, we will examine two general designs for conducting sampling: "pooled" sampling, or randomly sampling records from all available records, and "event-specific" sampling, or oversampling records from different event occurrences. We will derive results to compare the efficiency of these sampling designs as well as the utility of event-specific sampling in the presence of a lack of events or under-represented subgroups. Such issues will affect statistical power, especially in later event occurrences. Additionally, we will extend sample size and power calculation from the univariate case-cohort setting and derive the allocation of an optimal ratio of cases to controls to maximize power when restricted by resources.

survival analysis

efficient sampling

case-cohort design

recurrent events

correlated data

marginal methods 

Lifetime Data Science Section