Assessment of individual risk of illness is an important activity in preventive medicine. Development of risk-assessment models has heretofore relied predominantly on studies involving follow-up of cohort-type populations, while case–control studies have generally been considered unfit for this purpose. To present a method for individualized assessment of absolute risk of an illness (as illustrated by lung cancer) based on data from a ‘non-nested’ case–control study. We used data from a case–control study conducted in Montreal, Canada in 1996–2001. Individuals diagnosed with lung cancer (n = 920) and age- and sex-matched lung-cancer-free subjects (n = 1288) completed questionnaires documenting life-time cigarette-smoking history and occupational, medical, and family history. Unweighted and weighted logistic models were fitted. Model overfitting was assessed using bootstrap-based cross-validation and ‘shrinkage.’ The discriminating ability was assessed by the c-statistic, and the risk-stratifying performance was assessed by examination of the variability in risk estimates over hypothetical risk-profiles. In the logistic models, the logarithm of incidence-density of lung cancer was expressed as a function of age, sex, cigarette-smoking history, history of respiratory conditions and exposure to occupational carcinogens, and family history of lung cancer. The models entailed a minimal degree of overfitting (‘shrinkage’ factor: 0.97 for both unweighted and weighted models) and moderately high discriminating ability (c-statistic: 0.82 for the unweighted model and 0.66 for the weighted model). The method’s risk-stratifying performance was quite high. The presented method allows for individualized assessment of risk of lung cancer and can be used for development of risk-assessment models for other illnesses.
Case–control study Etiologic research Prognostic research Prognostication Risk assessment Absolute risk Lung cancer Logistic regression
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The study was supported by an operating grant from the Canadian Cancer Society Research Institute (Grant No. 019243). The authors thank Romain Pasquet and Mathieu Johnson for their help with the design of the on-line version of the risk-assessment tool.
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