Observational birth cohorts for causal and predictive inference: The example of childhood asthma and allergic diseases

Abstract

Prospective birth cohort studies have identified important factors associated with the development and occurrence of early life conditions and facilitated exploration of causal mechanisms. We discuss the strengths, importance, and biases of birth cohort data for causal inference and predictive modeling, using childhood asthma and allergic disease research as an illustrative example. State-of-the-art study design and statistical methodologies are considered and recommended to mitigate bias and infer causality, as well as using cohort assembly for increased power, sample size, and generalizability. These include effective control for confounding, limiting loss to follow-up, and leveraging risk factors for precision. While logistical and methodologic challenges exist for establishing, maintaining, and analyzing birth cohorts and their respective data, this prospective study design offers numerous benefits for inferring causality over other observational designs, and it is often the only alternative for assessing critical research questions. With long-term follow-up and extensive data collection, birth cohort studies represent powerful tools for studying disease etiology and have been integral to developing effective treatment and prevention strategies.

Keywords: Birth cohort; allergic diseases; asthma; causal inference; observational; predictive modeling; prospective studies.