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Updated: Jul 20, 2026

Multiplex Therapeutic Drug Monitoring by Isotope-dilution HPLC-MS/MS of Antibiotics in Critical Illnesses
11:17

Multiplex Therapeutic Drug Monitoring by Isotope-dilution HPLC-MS/MS of Antibiotics in Critical Illnesses

Published on: August 30, 2018

Antimicrobial resistance and patient outcomes: the hazards of adjustment

Mitchell J Schwaber1, Yehuda Carmeli

  • 1Division of Epidemiology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel. mitchells@tasmc.health.gov.il

Critical Care (London, England)
|September 20, 2006
PubMed

Insights

Studies on resistant bacterial infections may misinterpret results by not properly distinguishing intermediate variables from confounders. This can lead to biased analyses, impacting our understanding of treatment effectiveness.

Area of Science:

  • Epidemiology
  • Biostatistics
  • Infectious Diseases

Background:

  • Outcomes studies analyzing infections with resistant bacteria frequently face analytical challenges.
  • A common issue is the inappropriate control for intermediate variables when adjusting for confounders.

Purpose of the Study:

  • To highlight the critical distinction between confounders and intermediate variables in outcomes research.
  • To explain how failing to differentiate these can introduce bias into analyses of resistant bacterial infections.

Main Methods:

  • Conceptual discussion and explanation of causal inference principles.
  • Illustrative examples of how intermediate variables can be mistaken for confounders.

Main Results:

  • Failure to distinguish confounders from intermediate variables leads to biased effect estimates.
  • Proper identification is crucial for accurate assessment of exposure-outcome relationships.

Conclusions:

  • Emphasizes the need for careful consideration of intermediate variables in the causal pathway.
  • Recommends methodological approaches to correctly address confounding and intermediate variables in infection outcomes studies.

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