Applying Quantitative Bias Analysis to Epidemiologic Data

By T. Lash, M. Fox, A. Fink. Statistics for Biology and Health, 2009

522 citations recorded by the source corpus.

Subfields: Health Information Management, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/07ba8514b3c34d0181c2fb0283f91c51ad947e14

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Health Information Management

1. Tammy Jiang
2. J. Gradus
3. A. Rosellini

In Statistics and Probability

1. Tammy Jiang
2. J. Gradus
3. A. Rosellini
4. Mark S. Gilthorpe
5. G. Ellison
6. M. Fox
7. P. Tennant
8. Johannes Textor

Citations in the corpus

• Supervised machine learning: A brief primer (2020, methodology): “Measurement error arises when information is not correctly captured in the study database due to faulty instruments, respondents provide inaccurate information due to poor recall or sensitive issues, or mistakes are made in coding data (Lash et al., 2009).”
• Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations (2020, background): “Where possible, bias analyses should be performed to quantify the impact of unobserved confounders and obtain more accurate causal effect estimates.(35)”