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Related Concept Videos

Therapeutic Drug Monitoring: Affecting Factors01:29

Therapeutic Drug Monitoring: Affecting Factors

414
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.
414
Therapeutic Drug Monitoring: Overview and Classification01:16

Therapeutic Drug Monitoring: Overview and Classification

614
Therapeutic Drug Monitoring (TDM) is a clinical practice that measures specific drug levels in a patient's blood at designated intervals to ensure the drug concentration stays within a therapeutic range. This monitoring is crucial for optimizing individual dosage regimens, enhancing therapeutic efficacy, and minimizing drug-related toxicity. TDM is vital for drugs with narrow therapeutic windows, significant variability in pharmacokinetics, and a clear correlation between plasma levels and...
614
Dosage Regimens: Designs and Approaches01:28

Dosage Regimens: Designs and Approaches

589
Designing a dosage regimen, which refers to the manner of drug administration, is a complex process involving the selection of drug dose, route, and frequency. This process is underpinned by pharmacokinetic parameters derived from tests and population averages. These parameters are then tailored to patient-specific variables such as diagnosis, demographics, and allergy status. Once therapy commences, therapeutic response monitoring is critical and achieved through clinical and physical...
589
Dosage Regimen: Individualization01:24

Dosage Regimen: Individualization

384
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...
384
Therapeutic Drug Monitoring: Drug Analysis Methods01:26

Therapeutic Drug Monitoring: Drug Analysis Methods

336
Therapeutic Drug Monitoring (TDM) is a clinical practice that measures specific drug levels in a patient's blood or body tissues to tailor drug therapy effectively. This monitoring is critical for managing drugs with narrow therapeutic indices like digoxin and phenytoin, ensuring they are both safe and effective. For instance, monitoring theophylline levels in asthma patients involves precision and sensitivity to adjust doses according to individual responses to therapy, ensuring efficacy and...
336
Transdermal Drug Delivery Systems01:18

Transdermal Drug Delivery Systems

213
Transdermal drug delivery systems (TDDS) enable the controlled release of drugs across the skin into systemic circulation. They are particularly advantageous for drugs with short half-lives or narrow therapeutic indices, as they maintain consistent plasma concentrations and reduce the risk of subtherapeutic or toxic levels.TDDS are categorized into monolithic, reservoir, and mixed systems. Monolithic systems embed the drug in a polymer matrix, where diffusion governs release. Reservoir systems...
213

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Dynamic treatment regimes: technical challenges and applications.

Eric B Laber1, Daniel J Lizotte2, Min Qian3

  • 1North Carolina State University, Raleigh, NC 27696-8203.

Electronic Journal of Statistics
|October 31, 2014
PubMed
Summary
This summary is machine-generated.

Dynamic treatment regimes personalize care through sequential decision rules. This study introduces an Adaptive Confidence Interval (ACI) to improve inference for optimal treatment strategies, addressing challenges in personalized medicine.

Keywords:
Adaptive confidence intervalsData-driven decision makingNonregular inferencePersonalized medicine

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Area of Science:

  • Clinical sciences
  • Biostatistics
  • Personalized medicine

Background:

  • Dynamic treatment regimes are crucial for sequential personalized clinical decision-making.
  • These regimes consist of decision rules mapping patient data to treatments at each intervention stage.
  • Existing methods for constructing these rules and associated inferential challenges are reviewed.

Purpose of the Study:

  • To address the inferential challenge of nonregularity in optimal dynamic treatment regimes.
  • To propose and evaluate a locally consistent Adaptive Confidence Interval (ACI) for parameter inference.
  • To provide a robust method for estimating optimal treatment strategies.

Main Methods:

  • Review of approaches for data-driven construction of dynamic treatment regimes.
  • Identification and discussion of inferential challenges, particularly nonregularity.
  • Development and evaluation of a novel Adaptive Confidence Interval (ACI).

Main Results:

  • The proposed Adaptive Confidence Interval (ACI) is locally consistent for optimal dynamic treatment regime parameters.
  • The ACI addresses asymptotic distribution sensitivity to local perturbations.
  • Illustrative data from the ADHD trial demonstrates the method's application.

Conclusions:

  • The Adaptive Confidence Interval (ACI) offers a statistically sound approach for inference in dynamic treatment regimes.
  • This method enhances the reliability of personalized treatment recommendations.
  • Further theoretical research is needed to address emerging problems in this field.