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

Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

587
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
587
The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

1.3K
The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of...
1.3K
Relative Risk01:12

Relative Risk

2.6K
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
2.6K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

729
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...
729
Two-Way ANOVA01:17

Two-Way ANOVA

3.8K
The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the...
3.8K
Wilcoxon Signed-Ranks Test for Median of Single Population01:14

Wilcoxon Signed-Ranks Test for Median of Single Population

555
The Wilcoxon signed-rank test for the median of a single population is a nonparametric test used to evaluate whether the median of a population differs from a specified value. Unlike parametric tests, it does not require data to follow a normal distribution, making it suitable for non-normal or small samples. The test begins by calculating the difference (d) between each observation and the hypothesized median. The absolute values of these differences are ranked in ascending order, with ties...
555

You might also read

Related Articles

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

Sort by
Same author

The role of smoking as a risk indicator for apical periodontitis and endodontic status: a cross-sectional study of a portuguese adult sample.

Odontology·2026
Same author

Botulinum Toxin Effects on Biochemical Biomarkers Related to Inflammation-Associated Head and Neck Chronic Conditions: A Systematic Review of Preclinical Research.

Toxins·2025
Same author

Impact of chronic alcohol consumption on inflammatory response and periapical bone resorption in induced apical periodontitis: a systematic review of animal studies.

Odontology·2025
Same author

Botulinum toxin effects on biochemical biomarkers related to inflammation-associated head and neck chronic conditions: a systematic review of clinical research.

Journal of neural transmission (Vienna, Austria : 1996)·2025
Same author

Exploring Musculoskeletal Complaints in a Needle Manufacturing Industry: A Cross-Sectional Study.

International journal of environmental research and public health·2024
Same author

Osteoporosis and Apical Periodontitis Prevalence: A Systematic Review.

Dentistry journal·2024

Related Experiment Video

Updated: Apr 20, 2026

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

2.8K

CRIB conditional on gender: nonparametric ROC curve.

Maria Filipa Mourão, Ana Cristina Braga, Pedro Nuno Oliveira

    International Journal of Health Care Quality Assurance
    |November 25, 2014
    PubMed
    Summary

    Baby gender significantly impacts survival risk predictions using the Clinical Risk Index for Babies (CRIB) scale. Smoothed receiver operating characteristic (ROC) curves show higher predictive accuracy for female infants, highlighting gender as a crucial factor in neonatal outcomes.

    More Related Videos

    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

    11.1K
    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

    1.1K

    Related Experiment Videos

    Last Updated: Apr 20, 2026

    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

    2.8K
    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

    11.1K
    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

    1.1K

    Area of Science:

    • Neonatal care
    • Biostatistics
    • Medical informatics

    Background:

    • The Clinical Risk Index for Babies (CRIB) scale is a vital tool for assessing neonatal mortality risk.
    • Evaluating the influence of covariates like gender on the CRIB scale's predictive accuracy is crucial for refining neonatal risk stratification.

    Purpose of the Study:

    • To apply the kernel smoothing method for generating a smoothed receiver operating characteristic (ROC) curve.
    • To investigate the impact of newborn gender on the predictive performance of the CRIB scale regarding survival risks.

    Main Methods:

    • Employed direct smoothing within the ROC curve framework to model covariate effects.
    • Sampled 160 Portuguese newborns for analysis.
    • Calculated Area Under the Curve (AUC) for gender-stratified CRIB scale predictions.

    Main Results:

    • Kernel smoothing with a bandwidth of h=0.1 did not alter the fundamental shape of the ROC curves.
    • Newborn gender was identified as a significant covariate influencing the prediction of infant mortality.
    • The AUC was notably higher when the CRIB scale predictions were conditioned on female infants, indicating improved accuracy.

    Conclusions:

    • Gender serves as a significant predictor, discriminating between deceased and surviving infants within the CRIB scale's assessment.
    • Smoothed ROC curve analysis provides a robust method for evaluating covariate effects on risk prediction models.
    • The findings underscore the importance of considering gender in neonatal risk assessment tools like the CRIB scale.