Purpose: Population-based routine service screening has gained popularity following an era of randomized controlled trials. The evaluation of these service screening programs is subject to study design, data availability, and the precise data analysis for adjusting bias. We developed a computer-aided system that allows the evaluation of population-based service screening to unify these aspects and facilitate and guide the program assessor to efficiently perform an evaluation. Methods: This system underpins two experimental designs: the posttest-only non-equivalent design and the one-group pretest-posttest design and demonstrates the type of data required at both the population and individual levels. Three major analyses were developed that included a cumulative mortality analysis, survival analysis with lead-time adjustment, and self-selection bias adjustment. We used SAS AF software to develop a graphic interface system with a pull-down menu style. Results: We demonstrate the application of this system with data obtained from a Swedish population-based service screen and a population-based randomized controlled trial for the screening of breast, colorectal, and prostate cancer, and one service screening program for cervical cancer with Pap smears. The system provided automated descriptive results based on the various sources of available data and cumulative mortality curves corresponding to the study designs. The comparison of cumulative survival between clinically and screen-detected cases without a lead-time adjustment are also demonstrated. The intention-to-treat and noncompliance analysis with self-selection bias adjustments are also shown to assess the effectiveness of the population-based service screening program. Model validation was composed of a comparison between our adjusted self-selection bias estimates and the empirical results on effectiveness reported in the literature. Conclusions: We demonstrate a computer-aided system allowing the evaluation of population-based service screening programs with an adjustment for self-selection and lead-time bias. This is achieved by providing a tutorial guide from the study design to the data analysis, with bias adjustment.
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