If you are dealing with clinical data (e.g., medical imaging, EMR, or physiologic monitoring data), you can follow Ramesh Sridharan (@rameshvs), Matteo Fumagalli (@mfumagalli), and José Ignacio Orlando (@ignaciorlando). They are great on that. Although you may be looking for something more broad in terms of data analysis, clinical data analysis has great fundaments for best practices, since it is a critical domain. Hence, you can try to look more into this field. Additionally, you can look at some literature. You have some interesting literature coming from this field [3, 4], however; if you want to take a look at medical data analysis for breast cancer you can also follow my (@FMCalisto) work [1, 2].

References

[1] Calisto, F. M., Santiago, C., Nunes, N., & Nascimento, J. C. (2022). BreastScreening-AI: Evaluating medical intelligent agents for human-AI interactions. Artificial Intelligence in Medicine, 127, 102285.

[2] Calisto, F. M., Nunes, N., & Nascimento, J. C. (2020, September). BreastScreening: on the use of multi-modality in medical imaging diagnosis. In Proceedings of the international conference on advanced visual interfaces (pp. 1-5).

[3] Knight, R., Vrbanac, A., Taylor, B. C., Aksenov, A., Callewaert, C., Debelius, J., ... & Dorrestein, P. C. (2018). Best practices for analysing microbiomes. Nature Reviews Microbiology, 16(7), 410-422.

[4] McGinnis, J. M., Olsen, L., Goolsby, W. A., & Grossmann, C. (Eds.). (2011). Clinical data as the basic staple of health learning: Creating and protecting a public good: Workshop summary. National Academies Press.