Empirical assessment of practices and bias across diverse research domains
Abstract/Contents
- Abstract
- Research today is expensive, time consuming, and low yield. Much work has been done to describe research practices and quantify biases that may contribute to this. However, new ways of conducting research are constantly being proposed. Potential biases may not be as well characterized for these new fields. I aimed to describe the prevalence of practices and potential for biases in three relatively new and fast-growing fields. Specifically, these included: comparative oncology trials in domestic dogs, E-values for sensitivity analysis in observational studies, and machine learning diagnostic tools. Primary research articles were systematically retrieved from literature databases. Data regarding research methodology, reporting, and study results were extracted. In addition to field-specific findings, conserved trends were observed across multiple fields, including a reluctance to report experimental details, a tendency for facile automation in methods and interpretation, and an opportunity to learn from adjacent fields. The present study identifies pitfalls that threaten the validity, generalizability, and clinical value of these three fields of research and provides recommendations for improvement
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2020; ©2020 |
| Publication date | 2020; 2020 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Tan, Yuan Jin |
|---|---|
| Degree supervisor | Ioannidis, John |
| Thesis advisor | Ioannidis, John |
| Thesis advisor | Goodman, Steven R |
| Thesis advisor | Grimes, Kevin |
| Thesis advisor | Sainani, Kristin |
| Degree committee member | Goodman, Steven R |
| Degree committee member | Grimes, Kevin |
| Degree committee member | Sainani, Kristin |
| Associated with | Stanford University, Department of Epidemiology and Population Health. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Yuan Jin Tan |
|---|---|
| Note | Submitted to the Department of Epidemiology and Population Health |
| Thesis | Thesis Ph.D. Stanford University 2020 |
| Location | electronic resource |
Access conditions
- Copyright
- © 2020 by Yuan Jin Tan
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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