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Related Concept Videos

Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

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.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...

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Related Experiment Video

Updated: Jun 17, 2026

Pretargeted Radioimmunotherapy Based on the Inverse Electron Demand Diels-Alder Reaction
09:44

Pretargeted Radioimmunotherapy Based on the Inverse Electron Demand Diels-Alder Reaction

Published on: January 29, 2019

Analytical strategies for characterizing chemotherapy diffusion with patient-level population-based data.

Cami S Sima1, Katherine S Panageas, Glenn Heller

  • 1Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York 10021, USA. simac@mskcc.org

Applied Health Economics and Health Policy
|December 30, 2009
PubMed
Summary

This study introduces a new method to track how quickly new chemotherapy drugs reach cancer patients after FDA approval. It helps assess cancer care quality by analyzing drug adoption trends over time.

Related Experiment Videos

Last Updated: Jun 17, 2026

Pretargeted Radioimmunotherapy Based on the Inverse Electron Demand Diels-Alder Reaction
09:44

Pretargeted Radioimmunotherapy Based on the Inverse Electron Demand Diels-Alder Reaction

Published on: January 29, 2019

Area of Science:

  • Health Services Research
  • Pharmacoeconomics
  • Oncology

Background:

  • Assessing cancer care quality requires understanding the adoption of new chemotherapy drugs.
  • Existing economic and medical literature offers aggregate and disaggregate models for diffusion of innovations.
  • Patient-level data enables more robust disaggregate methods for diffusion analysis.

Purpose of the Study:

  • To describe analytical approaches for characterizing trends in chemotherapy drug diffusion post-US FDA approval.
  • To propose a novel method for assessing drug utilization trends using time-to-event techniques.
  • To evaluate the diffusion of specific chemotherapy drugs in cancer patient populations.

Main Methods:

  • Employed time-to-event analysis to model the probability of drug utilization post-cancer diagnosis.
  • Mapped utilization probability against patient diagnosis calendar time to assess diffusion trends.
  • Utilized Surveillance, Epidemiology, and End Results (SEER)-Medicare data, accounting for dependent censoring and patient clustering within physicians.

Main Results:

  • The proposed method effectively characterizes trends in chemotherapy drug diffusion.
  • Case studies on gemcitabine (pancreatic cancer) and irinotecan (colorectal cancer) illustrate the method's application.
  • Demonstrated the ability to assess the rate at which newly approved drugs become part of cancer treatment.

Conclusions:

  • The proposed time-to-event method provides a robust approach to analyzing chemotherapy drug diffusion trends.
  • This methodology can inform assessments of cancer care quality and the uptake of innovations.
  • Accurate tracking of drug diffusion is crucial for understanding treatment accessibility and patterns.