Matching algorithm for homogeneous base generation for case-control study
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1293Keywords:
Big Data, Case Control Study, Matching algorithmAbstract
Objective: generate balanced control bases in relation to the case base for a case-control study (CCS) and, therefore, minimize selection bias. Method: implementation and evaluation of a matching algorithm that generates homogeneous control bases, based on the characteristics of the case base. Results: the algorithm is capable of producing a homogeneous control file, with the same characteristics as the control base through three different real data sets. Furthermore, the algorithm is generic and easy to use and can be adopted for any case-control study. Conclusion: this algorithm has the potential to greatly help the scientific community, increasing the reliability of research carried out through case-control studies, generating a homogeneous database, helping to avoid selection bias.
References
Tenny, S., Kerndt, C., and Hoffman, M. (2023). Case control studies. StatPearls.
Haley, K. E. and Huber, K. E. (2023). Chapter 38 - case-control study. In Eltorai, A. E., Bakal, J. A., Kim, D. W., and Wazer, D. E., editors, Translational Radiation Oncology, Handbook for Designing and Conducting Clinical and Translational Research, pages 223–229. Academic Press
Rose, S. and Laan, M. J. (2009). Why match? investigating matched case-control study
designs with causal effect estimation. The international journal of biostatistics, 5(1)
Pinto, R., Polmann, H., Massignan, C., Stefani, C. M., and de L. Canto, G. (2021). Tipos
de vieses em estudos observacionais
Dash, S., S. S. S. M. e. a. (2019). Big data in healthcare: management, analysis and future prospects. J Big Data, 6(54).
Stanfill, B., Reehl, S., Bramer, L., Nakayasu, E. S., Rich, S. S., Metz, T. O., Rewers, M.,
Webb-Robertson, B. J., and Group, T. S. (2019). Extending classification algorithms
to case-control studies. Biomedical engineering and computational biology, 10
Mendes, G. P., Pôrto, L. C. M. S., Lima, C., Santiago, H., Almeida, S., and Sena, A. C.
(2023). Análise da prevalência de alelos hla em pacientes com covid-19. Journal of Health Informatics, 15(Especial)
Epi Info. Epi Info. Accessed: 2022-04-02
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