Data-integration with pseudoweights and survey-calibration: application to developing US-representative lung cancer risk models for use in screening
提出一种校准伪加权方法，整合队列、调查和癌症登记数据，利用调查中的癌症死亡率信息推算发病率，开发出能代表美国人群的肺癌风险模型，用于筛查决策。
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. Instead, time-to-cancer specific mortality is often readily available on surveys via linkage to vital statistics. We develop calibrated pseudoweighting methods that integrate individual-level data from a cohort and a survey, and summary statistics of cancer incidence from national cancer registries. By leveraging individual-level cancer mortality data in the survey, the proposed methods impute time-to-cancer incidence for survey sample individuals and use survey calibration with auxiliary variables of influence functions generated from Cox regression to improve robustness and efficiency of the inverse-propensity pseudoweighting method in estimating pure risks. We develop a lung cancer incidence pure risk model from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial using our proposed methods by integrating data from the National Health Interview Survey and cancer registries.