<p>Absolute risk prediction models are important tools for disease prevention and early detection. They are best studied in prospective cohorts. However, when the disease incidence rate is low, such as that of lung cancer in never smokers, synthesizing data and information from multiple sources is an important strategy. A recent study 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 on the basis of age-matched case–control studies and age-specific lung cancer incidence rates among this population. The latter were obtained from the Taiwan Cancer Registry, Cause of Death Database, age-specific female population size and smoking rates, life tables, and others. The risk factors considered in the logistic regression model included age, body mass index, chronic obstructive pulmonary disease, educational level, and genetic variants. With additional information on the age-specific population distribution of these risk factors, in this paper, we established an asymptotic theory, used it to construct confidence intervals, examined its numerical performance via simulation studies, and applied it to estimate the numbers and confidence intervals of Taiwanese never-smoking females whose lung cancer risk was higher than the thresholds discussed in the literature regarding low-dose computed tomography lung cancer screening. This information is useful for health policy decision making.</p>