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Related Concept Videos

Combination Therapies and Personalized Medicine02:50

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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
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Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

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Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
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Dosage Regimen: Individualization01:24

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Individualization in dosing regimens is the customization of medication doses for individual patients. Its necessity arises from the goal of maximizing therapeutic benefits while minimizing risks. This approach is pivotal because human responses to drugs can vary widely; what is effective for one person may be inadequate or excessive for another. Interpatient (intersubject) variability refers to differences in drug responses between individuals, while intrapatient (intrasubject) variability...
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Strategies for Assessing and Addressing Confounding01:25

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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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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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Factors Affecting Drug Response: Overview01:21

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When it comes to infants and young children, they are typically administered smaller doses of medication in comparison to adults. This is primarily because their organ functions still need to fully develop, meaning their bodies are not as efficient at metabolizing or eliminating drugs. Additionally, their blood-brain barrier is more permeable than in adults. As a result, high concentrations of drugs can easily penetrate the central nervous system (CNS), potentially leading to neurological...
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Related Experiment Video

Updated: Jan 2, 2026

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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Optimising treatment decision rules through generated effect modifiers: a precision medicine tutorial.

Eva Petkova1, Hyung Park2, Adam Ciarleglio3

  • 1Professor, Departments of Population Health and Child and Adolescent Psychiatry, New York University School of Medicine and Nathan S. Kline Institute for Psychiatric Research, USA.

Bjpsych Open
|December 4, 2019
PubMed
Summary
This summary is machine-generated.

This tutorial presents new methods for precision medicine to create personalized treatment plans. These approaches use interpretable

Keywords:
Treatment effect modifierspersonalised treatment assignmentsingle index modelstreatment decision rule (TDR)value of TDR

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Area of Science:

  • Biomedical Informatics
  • Translational Medicine
  • Statistical Genetics

Background:

  • Precision medicine aims to tailor treatments to individual patients.
  • Estimating optimal treatment decision rules is crucial for personalized healthcare.
  • Current machine learning methods can be complex and lack interpretability.

Purpose of the Study:

  • Introduce recent developments in precision medicine for treatment decision rules.
  • Advance personalized healthcare by identifying optimal treatments based on patient characteristics.
  • Provide interpretable methods for generating 'biosignatures' of treatment response.

Main Methods:

  • Define composite variables from patient measures, termed 'generated effect modifiers'.
  • Utilize linear and non-linear regression models for biosignature derivation.
  • Illustrate methods with examples from randomized clinical trials.

Main Results:

  • Generated effect modifiers serve as interpretable biosignatures for differential treatment response.
  • Regression-based biosignatures offer advantages in visualization and interpretation compared to complex machine learning models.
  • Demonstrated applicability in real-world clinical trial data.

Conclusions:

  • The presented methods offer a more interpretable approach to precision medicine.
  • Generated effect modifiers can enhance the identification of optimal treatment strategies.
  • These developments contribute to advancing personalized healthcare through data-driven insights.