Validity of absolute risk estimates derived from matched case-control studies and population rates

Music City Center 

Absolute risk prediction models are important tools for disease prevention. They are best studied in prospective cohorts. However, when the disease incidence rate is low, synthesizing data and information from multiple sources is an important strategy. Previously, we exemplified this strategy by proposing a two-stage procedure to estimate a logistic regression model for predicting lung cancer occurrence among never-smoking females in Taiwan based on age-matched case-control studies and age-specific lung cancer incidence rates among never-smoking females in Taiwan. With additional information on the age-specific population distribution of the risk factors, we establishes in this presentation its asymptotic theory, uses it to construct confidence intervals, examines its numerical performance by simulation studies, and applies it to estimate the numbers and confidence interval of Taiwanese never-smoking women whose lung cancer risk is higher than the thresholds discussed in the literature regarding low-dose computed tomography lung cancer screening, which is useful in health policy decision making.

Absolute risk prediction model

Matched case-control studies

Data synthesis

Low-dose computed tomography lung cancer
screening 

Section on Statistics in Epidemiology