Donald School Journal of Ultrasound in Obstetrics and Gynecology

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VOLUME 15 , ISSUE 4 ( October-December, 2021 ) > List of Articles


Best Practices in the Analysis of Ultrasonographic Research Data: Ancora Imparo

Zuber D Mulla

Keywords : Collider-stratification bias, Confounding, Directed acyclic graphs, Generalized linear models, Longitudinal data analysis, Overadjustment bias, Quantile regression, Ultrasound, Women\'s health

Citation Information : Mulla ZD. Best Practices in the Analysis of Ultrasonographic Research Data: Ancora Imparo. Donald School J Ultrasound Obstet Gynecol 2021; 15 (4):340-346.

DOI: 10.5005/jp-journals-10009-1800

License: CC BY-NC 4.0

Published Online: 31-12-2021

Copyright Statement:  Copyright © 2021; The Author(s).


Aim: To briefly review several regression models and epidemiological research strategies that are of interest to women healthcare professionals who are engaged in scholarship involving data from ultrasound imaging studies. Background: Advances in statistical methods in epidemiology in the past two decades can aid clinician investigators. However, these recent developments in research methods may not be well-known outside the disciplines of biostatistics and epidemiology. Review results: Several types of regression models are discussed including log-binomial regression and quantile regression. Modern methods for the analysis of repeated measures data including generalized estimating equations are reviewed. Finally, the utility of directed acyclic graphs (DAGs), a type of causal diagram, is introduced. Directed acyclic graphs are useful in identifying confounders and avoiding a variety of biases such as overadjustment bias and collider-stratification bias. Conclusion: Data arising from ultrasound imaging studies provide a wealth of scholarly opportunities for clinicians. The application of sound, modern statistical techniques will ensure the design and conduct of high-quality research investigations. Clinical significance: Physicians using ultrasound may encounter variables with a skewed distribution such as nuchal translucency or a dataset in which the dependent variable, such as an umbilical artery Doppler index, is measured multiple times. Special methods are required to analyze such datasets properly. Clinician researchers, especially early-career faculty, should consider collaborating with biostatisticians and epidemiologists.

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