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Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...

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Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities
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Published on: October 27, 2023

When does combining markers improve classification performance and what are implications for practice?

Aasthaa Bansal1, Margaret Sullivan Pepe

  • 1Department of Biostatistics, University of Washington, Campus Mail Stop 359461, Seattle, WA 98195, USA. abansal@uw.edu

Statistics in Medicine
|January 26, 2013
PubMed
Summary
This summary is machine-generated.

Combining new and standard markers can improve classification accuracy. A new marker

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Area of Science:

  • Biostatistics
  • Biomarker Discovery
  • Machine Learning in Medicine

Background:

  • Standard markers often lack sufficient classification accuracy alone.
  • New markers are sought to improve diagnostic or prognostic performance.
  • Current strategies prioritize markers with high individual performance and low correlation.

Purpose of the Study:

  • To investigate performance gains from combining novel continuous markers with standard continuous markers.
  • To explore various biologically motivated models for joint marker distributions.
  • To challenge conventional marker selection strategies.

Main Methods:

  • Investigated linear combinations of novel and standard continuous markers.
  • Utilized biologically motivated models for joint distributions.
  • Compared a broadened marker selection strategy (joint performance) with the standard strategy (marginal performance) using simulated and real datasets.

Main Results:

  • Uncorrelated novel markers with moderate individual performance offer minimal improvement.
  • Significant performance gains are achieved with novel markers that are highly correlated with the standard, even if their individual performance is poor.
  • High correlation within specific class categories can also lead to substantial improvements.

Conclusions:

  • Current marker selection strategies may be suboptimal.
  • A broadened strategy focusing on joint performance with existing markers can be more fruitful.
  • Broader strategies require studies with larger sample sizes for validation.