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Related Concept Videos

Clinical Trials01:16

Clinical Trials

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Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
There are four phases in a clinical trial. A phase one...
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Clinical Trials: Overview01:11

Clinical Trials: Overview

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Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...
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Hazard Ratio01:12

Hazard Ratio

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The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
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Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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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...
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Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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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.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
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Introduction To Survival Analysis01:18

Introduction To Survival Analysis

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Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Sizing clinical trials when comparing bivariate time-to-event outcomes.

Tomoyuki Sugimoto1, Toshimitsu Hamasaki2, Scott R Evans3

  • 1Department of Mathematics and Computer Science, Kagoshima University Graduate School of Science and Technology, Kagoshima, Japan.

Statistics in Medicine
|January 26, 2017
PubMed
Summary

This study provides methods for calculating sample size and power in clinical trials with two time-to-event outcomes. It addresses various censoring and competing risk scenarios for superiority trials.

Keywords:
dependent censoringlog-rank testmultiple endpointssemi-competing risktime-dependent association

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Survival Analysis

Background:

  • Clinical trials frequently involve multiple primary time-to-event outcomes, posing challenges for power and sample size calculations.
  • Accurate statistical methods are crucial for designing robust trials with multiple endpoints, especially for time-to-event data.

Purpose of the Study:

  • To present methods for calculating statistical power and sample size for superiority clinical trials featuring two correlated time-to-event outcomes.
  • To address independent and dependent censoring across three distinct censoring scenarios: non-fatal, semi-competing risk, and competing risk events.

Main Methods:

  • Derivation of the bivariate log-rank test for all three censoring scenarios.
  • Investigation of statistical power and required sample sizes under different conditions.
  • Evaluation for two inferential goals: superiority on all co-primary endpoints or at least one primary endpoint.

Main Results:

  • The study provides a framework for calculating power and sample size for complex time-to-event endpoints.
  • The derived bivariate log-rank test's behavior and its impact on sample size requirements are analyzed across scenarios.

Conclusions:

  • The presented methods facilitate more accurate sample size and power calculations in clinical trials with multiple time-to-event outcomes.
  • This work supports improved trial design, particularly in the presence of competing risks and complex censoring patterns.