Abstract
Cohorts are instrumental for epidemiologically oriented observational studies. Cohort studies usually observe large groups of individuals for a specific period of time to identify the contributing factors to a specific outcome (for instance an illness) and create associations between risk factors and the outcome under study. In collaborative projects, federated data facilities are meta-database systems that are distributed across multiple locations that permit to analyze, combine, or harmonize data from different sources making them suitable for mega- and meta-analyses. The harmonization of data can increase the statistical power of studies through maximization of sample size, allowing for additional refined statistical analyses, which ultimately lead to answer research questions that could not be addressed while using a single study. Indeed, harmonized data can be analyzed through mega-analysis of raw data or fixed effects meta-analysis. Other types of data might be analyzed by e.g., random-effects meta-analyses or Bayesian evidence synthesis. In this article, we describe some methodological aspects related to the construction of a federated facility to optimize analyses of multiple datasets, the impact of missing data, and some methods for handling missing data in cohort studies.
Highlights
Cohort studies are widely used in epidemiology to measure how the exposure to certain factors influences the risk of a specific disease
We aim to suggest a few examples of cohort studies and a data collection procedure for a cohort study, and, secondly, to offer approaches of harmonization and integrative data analysis over cohorts
We present different methods for handling missing data, such as complete case-analysis and multiple imputations
Summary
Cohort studies are widely used in epidemiology to measure how the exposure to certain factors influences the risk of a specific disease. The role of large cohort studies is increasing with the development of multi-omics approaches and with the search of methods for the translation of omics findings, especially those that are derived from genome-wide association studies (GWAS) in clinical settings [1]. Many research efforts have been made to link vast amounts of phenotypic data across diverse centers. This procedure concerns molecular information, as well as data regarding environmental factors, such as those recorded in and obtained from health-care databases and epidemiological registers [2]. Cohort studies can be prospective (forward-looking) or retrospective (backward-looking).
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