A Motivational Example of An Emr-Based Research Question
Suppose a research group is interested in conducting a study on whether the number of occupied beds in an intensive care unit (ICU) is related to risk for infection (Goldstein et al., 2017).
The researchers hypothesize that the higher the ICU’s occupancy rate, the more likely it is for basic hygiene practices to break down, thus leading to increase.
Abstract
Epidemiology, biostatistics, and data science are broad disciplines that incorporate a variety of substantive areas.
Common among them is a focus on quantitative approaches for solving intricate problems.
When the substantive area is health and health care, the overlap is further cemented.
Researchers in these disciplines are fluent in statistics, .
Bringing Data Science to Health Research: More Than Just Machine Learning
Data scientists employ a variety of sophisticated methods that noncomputational researchers may not be aware of.
Machine learning and artificial intelligence algorithms, one of the many methodological tools of the data scientist, are becoming increasingly utilized in a variety of fields and have advanced causal inference approaches used by epidemio.
Disclosure Statement
Research reported in this publication was supported by the National Institute Of Allergy And Infectious Diseases of the National Institutes of Health under Award Number K01AI143356 (to NDG).
The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Discussion: What Does The Future Hold?
Data literacy underscores our themes in this article.
Data are inextricably embedded in everything we do as researchers; we all struggle with issues of data quality, measurement error, bias, and missing data.
Training students to understand the possibilities, and more importantly, the limitations of data is paramount.
As was argued in the first iss.
Introduction: A Confluence of Concepts
The fields of epidemiology, biostatistics, and data science, while very distinct in their focus on training, share much in common in that they all rely upon an intersection of various and overlapping concepts.
These concepts include statistical methods, research design, and substantive expertise.
Rigorous analysis of quantitative data is the common.
Opportunities For Training: Brick and Mortar Barriers to Collaboration
Given the importance of a collaborative model in health research, the question as to whether students are afforded an opportunity to cross-train arises.
To assess the current state of formalized training in epidemiology, biostatistics, and data science, we undertook a review of curricula as of the Fall 2019 academic year at the top 20–ranked U.S.
N.
References
Angrist, J., & Pischke, J.
S. (2008).
Mostly harmless econometrics.
Princeton University Press.
Blakely, T., Lynch, J., Simons, K., Bentley, R., & Rose, S. (2019).
Reflection on modern methods: When worlds collide-prediction, machine learning and causal inference.
International Journal of Epidemiology, 49(6), 2058–2064. https://doi.org/10.1093/ije/.
What are the main branches of Biostatistics?
Biostatistics is a branch of statistics applied to biological or medical sciences.
Biostatistics covers applications and contributions not only from health, medicines and, nutrition but also from fields such as:
genetics biology epidemiology and many others.[ 1 ] . 