Abstract
Algorithms and technologies are essential tools that pervade all aspects of our daily lives. In the last decades, health care research benefited from new computer-based recruiting methods, the use of federated architectures for data storage, the introduction of innovative analyses of datasets, and so on. Nevertheless, health care datasets can still be affected by data bias. Due to data bias, they provide a distorted view of reality, leading to wrong analysis results and, consequently, decisions. For example, in a clinical trial that studied the risk of cardiovascular diseases, predictions were wrong due to the lack of data on ethnic minorities. It is, therefore, of paramount importance for researchers to acknowledge data bias that may be present in the datasets they use, eventually adopt techniques to mitigate them and control if and how analyses results are impacted.
This paper proposes a method to address bias in datasets that: (i) defines the types of data bias that may be present in the dataset, (ii) characterizes and quantifies data bias with adequate metrics, (iii) provides guidelines to identify, measure, and mitigate data bias for different data sources. The method we propose is applicable both for prospective and retrospective clinical trials. We evaluate our proposal both through theoretical considerations and through interviews with researchers in the health care environment.
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Acknowledgment
This work has been partially supported by the Health Big Data Project (CCR-2018-23669122), funded by the Italian Ministry of Economy and Finance and coordinated by the Italian Ministry of Health and the network Alleanza Contro il Cancro.
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Criscuolo, C., Dolci, T., Salnitri, M. (2022). Towards Assessing Data Bias in Clinical Trials. In: Rezig, E.K., et al. Heterogeneous Data Management, Polystores, and Analytics for Healthcare. DMAH Poly 2022 2022. Lecture Notes in Computer Science, vol 13814. Springer, Cham. https://doi.org/10.1007/978-3-031-23905-2_5
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