Life Expectancy Predicted by Decision-Analytic Models Evaluating Screening for Prostate, Lung, Breast, and Colorectal Cancer: A Systematic Review Focusing on Competing Mortality Risks

Background It is still a matter of debate whether a reduction in cancer-specific mortality due to cancer screening fully translates into a reduction in all-cause mortality and thus into a gain in life expectancy. Nevertheless, decision-analytic models simulating the health consequences of screening compared with no screening predict substantial gains in life expectancy. Purpose The aim of this review was to systematically assess methodological competing mortality risk features that affect the translation of cancer-specific mortality reductions into gains in life expectancy in decision-analytic screening models for prostate, lung, breast, and colorectal cancer. Data Sources Literature databases were systematically searched for clinical and economic decision-analytic models evaluating the effect of screening for prostate, lung, breast, and colorectal cancer compared with no screening. Study Selection Forty-two clinical and economic decision-analytic models were included for narrative synthesis. Data Extraction Basic information and specific methodological features of the included decision-analytic models were extracted using a standardized approach. Data Synthesis Characteristics and methodological features of the identified studies were summarized in evidence tables. Limitations The review focused on models that reported undiscounted outcomes of life-years gained for standard screening strategies. Conclusions This review highlights key modeling features related to competing mortality risks that should be considered in decision-analytic models assessing the effects of cancer screening. All included models predicted gains in life expectancy with screening, although the magnitude of these gains varied both within and across cancer types. Models that considered competing mortality risks tended to predict smaller lifetime gains from screening interventions. Future studies should prioritize the use of advanced modeling approaches that account for competing mortality risks to improve the accuracy of benefit–harm assessments in cancer screening. Highlights This is the first systematic assessment of methodological competing mortality risk features of decision-analytic screening models across 4 cancer types. Models vary greatly regarding predicted gains in life expectancy, natural history assumptions (onset and progression rates), methodological model features, and screening strategies. Models that considered competing mortality risks or adjusted life expectancy for comorbidities predicted smaller lifetime gains for screening compared with no screening.