Handling survivor bias in case-control data with prevalent cases and age-matched controls

Music City Center 

Case-control study designs are commonly used in biomedical studies because they require less time and expense than a prospective cohort study to conduct satisfactory analyses. Such designs are applicable for finding risk factors of a disease when study subjects can be classified as cases vs. controls according to the status of being diseased or disease-free. When adopting such a sampling design, survivor bias could form a serious problem which may lead to biased analysis results. Existing approaches typically treated cases and controls in binary form without specifying age at incidence of disease, and assumed the prevalent cases are sampled with length bias in stationary models. Instead of binary disease outcome, we consider age-specific risk outcome and propose a composite likelihood approach which handles survival bias in either stationary or non-stationary models. A data analysis is presented using data from the Alzheimer Biomarkers Consortium - Down Syndrome (ABC-DS) Study to illustrate the applicability of the proposed methods.