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Related Concept Videos

Therapeutic Drug Monitoring: Affecting Factors01:29

Therapeutic Drug Monitoring: Affecting Factors

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Therapeutic Drug Monitoring (TDM) is the clinical practice of measuring specific drug levels in a patient's blood or body tissues to manage and optimize therapy. TDM is crucial for drugs with narrow therapeutic windows, like warfarin and phenytoin, where incorrect doses can lead to treatment failure or severe side effects. This monitoring ensures the dosage administered is within a safe and effective range. The factors affecting therapeutic drug monitoring include:Patient-Specific Factors:a.
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Evaluating Markers for Guiding Treatment.

Stuart G Baker1, Marco Bonetti2

  • 1Division of Cancer Prevention, National Cancer Institute, Bethesda, MD (SGB); Carlo F. Dondena Centre for Research on Social Dynamics and Public Policies and Bocconi University, Milan, Italy (MB) sb16i@nih.gov.

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Summary
This summary is machine-generated.

The subpopulation treatment effect pattern plot (STEPP) can now assess multiple markers for clinical trial subgroups. While initial breast cancer data showed no significant subgroups, hypothetical data with many markers identified promising patient groups.

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Translational Medicine

Background:

  • The subpopulation treatment effect pattern plot (STEPP) is valuable for identifying patient subgroups with differential treatment effects.
  • Original STEPP lacked decision-analytic justification and was limited to single predictive markers.

Purpose of the Study:

  • To derive a decision-analytic foundation for STEPP.
  • To extend STEPP for analyzing multiple predictive markers.

Main Methods:

  • Developed a decision-analytic framework to motivate STEPP.
  • Incorporated multiple predictive markers using risk difference, cadit, and responders-only benefit functions.

Main Results:

  • Application of STEPP to breast cancer trial data with multiple markers did not identify a promising subgroup.
  • Analysis of hypothetical data with 100 markers revealed promising subgroups identified by all three benefit functions.

Conclusions:

  • STEPP, with its decision-analytic properties and informative plots, is a valuable tool for randomized trials.
  • The method can identify subgroups with potentially large treatment effects based on multiple predictive markers.