Browsing by Subject "iterative proportional fitting"

The increasing demand for comprehensive datasets to address complex diseases has resulted in a widespread popularity of biobank-based research. However, the collection of biobank-level data may be susceptible to biases when fundamental aspects of study design, such as sampling approach, are overlooked. FinnGen is a large-scale cohort study aiming to improve diagnoses and prevent diseases through genetic research by combining biobank data with registry data.However, FinnGen’s hospital-based recruitment strategy makes FinnGen suffer from selection bias and thus epidemiologically less representative of its sampling population. In this study, we examine the profound impact of selection bias in FinnGen. We use well-established epidemiological methods and leverage representative data on the Finnish population to try and correct for the bias. By comparing key demographic characteristics and association statistics of interest between FinnGen and a comprehensive registry-based study, FinRegistry, we highlight the extent to which selection bias within FinnGen results in distorted association estimates and a dataset that is highly non - representative of its underlying population. In response to these findings, we estimate Iterative Proportional Fitting (IPF) weights to estimate association statistics that are representative of the true sampling population of FinnGen and unaffected by selection bias. By comparing weighted associations estimated in the FinnGen with associations estimated using FinRegistry data, we infer that the use of our IPF weights mitigates volunteer bias in FinnGen.