Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

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 until a...
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...
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.
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...
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 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 survival tree begins...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Outcomes in chronic lymphocytic leukemia patients on novel agents in the US Veterans Health Administration System.

Leukemia & lymphoma·2021
Same author

Comparative Validity of the American Speech-Language-Hearing Association's National Outcomes Measurement System, Functional Oral Intake Scale, and G-Codes to Mann Assessment of Swallowing Ability Scores for Dysphagia.

American journal of speech-language pathology·2019
Same author

Plinabulin, an inhibitor of tubulin polymerization, targets KRAS signaling through disruption of endosomal recycling.

Biomedical reports·2019
Same author

PACS2 is required for ox-LDL-induced endothelial cell apoptosis by regulating mitochondria-associated ER membrane formation and mitochondrial Ca<sup>2+</sup> elevation.

Experimental cell research·2019
Same author

A Feature Extraction Method Based on Differential Entropy and Linear Discriminant Analysis for Emotion Recognition.

Sensors (Basel, Switzerland)·2019
Same author

Inhibition of STAT3 activation mediated by toll-like receptor 4 attenuates angiotensin II-induced renal fibrosis and dysfunction.

British journal of pharmacology·2019
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
See all related articles

Related Experiment Video

Updated: Jun 24, 2026

Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy
07:02

Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy

Published on: January 19, 2019

A spatial scan statistic for survival data.

Lan Huang1, Martin Kulldorff, David Gregorio

  • 1Statistical Research and Applications Branch, Division of Cancer Control and Population Sciences, National Cancer Institute (Contractor), 6116 Executive Boulevard, Rockville, Maryland 20852, USA. huangla@mail.nih.gov

Biometrics
|April 24, 2007
PubMed
Summary
This summary is machine-generated.

This study introduces a new spatial scan statistic using an exponential model for continuous survival data, improving disease surveillance and cluster detection for outcomes like cancer survival. The method effectively analyzes both uncensored and censored data, offering enhanced geographical health insights.

More Related Videos

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

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

Related Experiment Videos

Last Updated: Jun 24, 2026

Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy
07:02

Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy

Published on: January 19, 2019

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

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

Area of Science:

  • Epidemiology
  • Biostatistics
  • Geographic Information Systems (GIS)

Background:

  • Traditional spatial scan statistics use Bernoulli and Poisson models, suitable for count data but not continuous outcomes.
  • Existing methods lack the capacity to analyze geographical disease patterns with continuous survival data effectively.

Purpose of the Study:

  • To propose and evaluate a novel spatial scan statistic for geographical disease surveillance using continuous survival data.
  • To extend cluster detection capabilities to handle both uncensored and censored continuous outcomes.

Main Methods:

  • Developed a spatial scan statistic employing an exponential model for continuous survival data.
  • Investigated the power and sensitivity of the proposed model through intensive simulations.
  • Incorporated covariate adjustment for enhanced analytical precision.

Main Results:

  • The exponential model-based spatial scan statistic demonstrated good performance across various survival distributions (exponential, gamma, log-normal).
  • The method proved effective for analyzing both uncensored and censored continuous survival data.
  • Covariate adjustment improved the analysis of spatial disease patterns.

Conclusions:

  • The proposed spatial scan statistic offers a robust method for geographical disease surveillance with continuous survival data.
  • This approach enhances the detection of disease clusters, particularly in epidemiological studies involving survival outcomes.
  • The method is applicable to real-world health data, as demonstrated with prostate cancer survival data.