Data-integration with pseudoweights and survey-calibration: application to developing US-representative lung cancer risk models for use in screening.

Accurate cancer risk estimation is crucial to clinical decision-making, such as identifying high-risk people for screening. However, most existing cancer risk models incorporate data from epidemiologic studies, which usually cannot represent the target population. While population-based health surveys are ideal for making inference to the target population, they typically do not collect time-to-cancer incidence data.

Evaluating the Use of Environmental and Polygenic Risk Scores to Inform Colorectal Cancer Risk-Based Surveillance Intervals.

Introduction: United States Multi-Society Task Force colonoscopy surveillance intervals are based solely on adenoma characteristics, without accounting for other risk factors. We investigated whether a risk model including demographic, environmental, and genetic risk factors could individualize surveillance intervals under an "equal management of equal risks" framework.

Methods: Using 14,069 individuals from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial who had a diagnostic colonoscopy following an abnormal flexible sigmoidoscopy, we modeled the risk of colorectal cancer, considering the diagnostic colonoscopy finding, baseline risk factors (e.

Evaluating the Use of Environmental and Polygenic Risk Scores to Inform Colorectal Cancer Risk-Based Surveillance Intervals.

Introduction: United States Multi-Society Task Force colonoscopy surveillance intervals are based solely on adenoma characteristics, without accounting for other risk factors. We investigated whether a risk model including demographic, environmental, and genetic risk factors could individualize surveillance intervals under an "equal management of equal risks" framework.

Methods: Using 14,069 individuals from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial who had a diagnostic colonoscopy following an abnormal flexible sigmoidoscopy, we modeled the risk of colorectal cancer, considering the diagnostic colonoscopy finding, baseline risk factors (e.

Design of randomized controlled trials to estimate cancer-mortality reductions from multicancer detection screening.

Background: Determining whether screening with multicancer detection (MCD) tests saves lives requires randomized controlled trials (RCTs). To inform RCT design, we estimated cancer-mortality outcomes from hypothetical MCD RCTs.

Methods: We used the Hu-Zelen model, previously used to plan the NLST, to estimate mortality reductions, sample-size, and power for 9 cancers for different RCT design parameters and MCD test parameters.