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

Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

222
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...
222
Ordinal Level of Measurement00:55

Ordinal Level of Measurement

24.8K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks...
24.8K
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

154
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.
154
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

277
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...
277
Cancer Survival Analysis01:21

Cancer Survival Analysis

383
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...
383
Survival Tree01:19

Survival Tree

109
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...
109

You might also read

Related Articles

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

Sort by
Same author

Escalating Threat of Wheat Stripe Rust Under Climate Change: Pathogen Evolution, Resistance Durability, and Future Management.

Plants (Basel, Switzerland)·2026
Same author

Identification and Biological Control of Fungal Pathogens Associated with Cactus Pear Diseases in Morocco.

Plants (Basel, Switzerland)·2026
Same author

Comparative Meta-Analysis of Chemical and Biological Strategies for the Management of Wheat Stripe Rust (<i>Puccinia striiformis</i> f. sp. <i>tritici</i>) Under Global Agro-Ecological Conditions.

Plants (Basel, Switzerland)·2026
Same author

Assessing <i>Venturia inaequalis</i> Response to Common Fungicides in Morocco.

Journal of fungi (Basel, Switzerland)·2025
Same author

Scab Intensity in Pecan Trees in Relation to Hedge-Pruning Methods.

Plant disease·2024
Same author

Advancing Methodologies for Investigating PM<sub>2.5</sub> Removal Using Green Wall System.

Plants (Basel, Switzerland)·2024
Same journal

Biometric, Physiological, Biochemical and Molecular Responses of Grapevine to Flavescence Dorée Phytoplasma Infection: A Comprehensive Meta-Analysis.

Phytopathology·2026
Same journal

The Invasive Fungal Pathogen <i>Neopestalotiopsis</i> in North Carolina: Molecular Characterization, Virulence, and Host Susceptibility.

Phytopathology·2026
Same journal

Phloem Sucrose Osmoregulation and Vector Competence in the Asian Citrus Psyllid, the Vector of Huanglongbing.

Phytopathology·2026
Same journal

Compartment-Specific Bacterial Communities in Turmeric and Their Association with Suppression of <i>Ralstonia pseudosolanacearum</i>.

Phytopathology·2026
Same journal

Population Structure of <i>Alternaria brassicicola</i> Suggests Genetic Diversity in Organic Broccoli Farms in Connecticut Is Driven by Multiple Introductions.

Phytopathology·2026
Same journal

Plant Hydathodes Detect Microbial Patterns to Close Hydathode Pores and Restrict Leaf Entry.

Phytopathology·2026
See all related articles

Related Experiment Video

Updated: Jul 18, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.2K

Survival Analysis as a Basis for Testing Hypotheses when Using Quantitative Ordinal Scale Disease Severity Data.

K S Chiang1, Y M Chang2, H I Liu3

  • 1Division of Biometrics, Department of Agronomy, National Chung Hsing University, Taichung, Taiwan.

Phytopathology
|August 22, 2023
PubMed
Summary
This summary is machine-generated.

Survival analysis (SA) offers a more powerful method than traditional midpoint conversion for analyzing plant disease severity data from quantitative ordinal scales (QOS). SA improves hypothesis testing, potentially reducing sample size needs in plant pathology research.

Keywords:
data scienceepidemiologymodeling

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.1K
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

302

Related Experiment Videos

Last Updated: Jul 18, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.2K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.1K
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

302

Area of Science:

  • Plant Pathology
  • Statistical Methods in Biology
  • Quantitative Ordinal Scales (QOS)

Background:

  • Disease severity in plant pathology is commonly measured using quantitative ordinal scales (QOS).
  • Traditional analysis of QOS data often involves midpoint conversion, which can lack precision.
  • QOS data can be considered interval-censored, introducing uncertainty in estimations.

Purpose of the Study:

  • To compare the effectiveness of survival analysis (SA) against midpoint conversion for analyzing QOS data.
  • To evaluate the impact of non-normally distributed data on the performance of these analytical methods.

Main Methods:

  • A simulation study was conducted using data from three plant pathosystems.
  • Quantitative ordinal scale (QOS) data were analyzed using both midpoint conversion and survival analysis (SA).
  • Statistical power was compared between the two methods, particularly under non-normal data conditions.

Main Results:

  • Survival analysis (SA) consistently outperformed midpoint conversion in terms of statistical power.
  • Midpoint conversion sometimes required up to a 400% larger sample size to achieve similar power to SA.
  • The need for increased sample size decreased as mean disease severity increased for both methods.

Conclusions:

  • Survival analysis (SA) is a valuable statistical tool for enhancing hypothesis testing with quantitative ordinal scale (QOS) severity data in plant pathology.
  • SA effectively accounts for interval-censored data, offering greater precision than midpoint conversion.
  • Further exploration of SA techniques in plant pathology is recommended to leverage its analytical advantages.