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Decision Making01:20

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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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MRI-guided dmPFC-rTMS as a Treatment for Treatment-resistant Major Depressive Disorder
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Development and evaluating multimarker models for guiding treatment decisions.

Parvin Tajik1, Mohammad Hadi Zafarmand2, Aeilko H Zwinderman3

  • 1Department of Pathology, Department of Clinical Epidemiology, Biostatistics & Bioinformatics, Department of Obstetrics & Gynaecology, Academic Medical Centre - University of Amsterdam, Room J1b-210, PO Box 22700, 1100, DE, Amsterdam, the Netherlands. p.tajik@amc.uva.nl.

BMC Medical Informatics and Decision Making
|June 30, 2018
PubMed
Summary
This summary is machine-generated.

A new strategy helps identify patient groups who benefit from specific treatments using multi-marker models. This approach analyzes existing trial data to predict treatment response and guide clinical decisions.

Keywords:
BiomarkerIndividualised medicineModel developmentPrediction modelsPrognosticRandomised controlled trialsStratified medicineSubgroup analysisTreatment selectionValidation

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Area of Science:

  • Biostatistics
  • Clinical Trial Analysis
  • Predictive Modeling

Background:

  • Developing markers to predict treatment response is crucial but lacks standardized methodology.
  • A step-by-step strategy is proposed to identify and evaluate multi-marker models for treatment selection.

Purpose of the Study:

  • To develop and present a methodology for identifying multi-marker models to predict treatment benefit.
  • To apply this strategy to existing randomized trial data for guiding treatment decisions.

Main Methods:

  • Formulated treatment selection problem and defined treatment threshold.
  • Developed a multivariable prediction model focusing on treatment benefit using marker-by-treatment interactions.
  • Applied the model to data from a randomized trial of cervical pessary in multiple pregnancies.

Main Results:

  • The developed logistic model identified 35% of participants as benefiting from pessary insertion.
  • Model-based selective pessary insertion could reduce adverse outcome risk from 13.5% to 8.1%.
  • External validation of the model using independent trial data is the next step.

Conclusions:

  • Revisiting existing trial data can reveal patient characteristics associated with treatment benefit.
  • This approach can lead to validated treatment selection tools for clinical practice.
  • The proposed strategy aids in optimizing treatment decisions for future patients.