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Related Concept Videos

Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
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,...
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
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...
Survival Curves01:18

Survival Curves

Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
Effect of Hepatic Disease on Pharmacokinetics: Pathophysiologic Assessment and Liver Function Test01:22

Effect of Hepatic Disease on Pharmacokinetics: Pathophysiologic Assessment and Liver Function Test

In clinical practice, the direct measurement of hepatic blood flow to evaluate liver function presents significant challenges due to the intricate and specialized nature of the necessary techniques. Consequently, healthcare professionals often rely on empirical estimates derived from thorough patient examinations and liver function tests to gauge liver health. Among the tools at their disposal, the Child–Pugh and MELD scoring systems stand out for their ability to categorize and assess the...

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Related Experiment Video

Updated: May 31, 2026

Evaluating the Effectiveness of Cancer Drug Sensitization In Vitro and In Vivo
09:19

Evaluating the Effectiveness of Cancer Drug Sensitization In Vitro and In Vivo

Published on: February 6, 2015

Identifying differences between biochemical failure and cure: incidence rates and predictors.

Frank A Vicini1, Chirag Shah, Larry Kestin

  • 1Department of Radiation Oncology, William Beaumont Hospital, Royal Oak, MI 48073, USA. fvicini@beaumont.edu

International Journal of Radiation Oncology, Biology, Physics
|July 19, 2011
PubMed
Summary

Long-term follow-up is crucial for prostate cancer patients treated with radiation therapy, as biochemical failure rates don't stabilize until after 10 years. Prostate-specific antigen nadir and time to nadir are key indicators of treatment success.

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Last Updated: May 31, 2026

Evaluating the Effectiveness of Cancer Drug Sensitization In Vitro and In Vivo
09:19

Evaluating the Effectiveness of Cancer Drug Sensitization In Vitro and In Vivo

Published on: February 6, 2015

Area of Science:

  • Oncology
  • Radiation Oncology
  • Urology

Background:

  • Prostate cancer treatment efficacy requires understanding long-term outcomes.
  • Evaluating biochemical cure (BC) after radiation therapy (RT) is essential for patient management.

Purpose of the Study:

  • To estimate the time to achieve biochemical cure (BC) post-radiation therapy (RT) for prostate cancer.
  • To identify variables associated with long-term treatment efficacy and biochemical failure (BF).

Main Methods:

  • Analysis of 2,100 patients with localized prostate carcinoma treated with RT (external-beam, brachytherapy, or combined).
  • Biochemical failure (BF) defined using the Phoenix criteria.
  • Median follow-up of 8.6 years.

Main Results:

  • 32.6% of patients experienced biochemical failure (BF).
  • Median time to BF varied by risk group (low: 6.0 years, intermediate: 5.6 years, high: 4.5 years).
  • Prostate-specific antigen nadir and time to nadir were significantly associated with 10-year BC.

Conclusions:

  • Biochemical failure (BF) rates in prostate cancer patients treated with RT do not plateau until after 10 years.
  • Extended patient follow-up is necessary to accurately assess long-term treatment outcomes.
  • Prostate-specific antigen nadir and time to nadir are the strongest predictors of long-term biochemical cure (BC).