相关实验视频

Updated: Jun 26, 2025

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

256

在标准化治疗后,Nomograms预测SCLC患者的PFS和OS:现实世界的研究

Ke Li1,2, Lupeng Qiu1,2, Yang Zhao3

  • 1Medical School of Chinese PLA, Beijing, 100853, People's Republic of China.

International journal of general medicine
|May 13, 2024
PubMed
概括

这项研究开发并验证了名谱模型,以预测小细胞肺癌 (SCLC) 患者的无进展生存期 (PFS) 和整体生存期 (OS). 这些模型根据治疗反应准确评估患者的预后.

关键词:
诺莫格拉姆预后模型预测模型患者预后 患者预后小细胞肺癌是一种小细胞肺癌.治疗对治疗的反应反应.

更多相关视频

Establishing a Competing Risk Regression Nomogram Model for Survival Data04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.2K
Predictive Immune Modeling of Solid Tumors08:50

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

6.9K

相关概念视频

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...
345
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...
177
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,...
133