相关实验视频

Updated: Feb 12, 2026

Survivable Stereotaxic Surgery in Rodents
00:08

Survivable Stereotaxic Surgery in Rodents

Published on: October 6, 2008

70.2K

在无进展生存益处的背景下评估后进展生存:对CodeBreaK200设计的重新审视

Zhoujingpeng Wei1, Yuanyuan Han2, Luoying Yang3

  • 1Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.

Therapeutic innovation & regulatory science
|February 10, 2026
PubMed
概括

在瘤学试验中无进展生存 (PFS) 的改善可能并不总是转化为整体生存 (OS) 的好处. 诸如交叉和后续疗法的因素可能会掩盖OS的收益,影响药物批准. 谨慎的试验设计至关重要.

关键词:
交叉式车辆交叉式车辆有关信息的审查信息审查.瘤学试验的试验总体生存率 总体生存率进展后的生存率.没有进展的生存率.随后的疗法后续治疗.

更多相关视频

Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy07:02

Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy

Published on: January 19, 2019

6.9K
A Quick Phenotypic Neurological Scoring System for Evaluating Disease Progression in the SOD1-G93A Mouse Model of ALS06:49

A Quick Phenotypic Neurological Scoring System for Evaluating Disease Progression in the SOD1-G93A Mouse Model of ALS

Published on: October 6, 2015

21.0K

相关概念视频

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...
732
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
436
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

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...
830
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...
617
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
631
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
433