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

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
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...
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
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...

You might also read

Related Articles

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

Sort by
Same author

Explainable AI for Equitable Nurse Scheduling: Pragmatic Pre-Post Implementation Study.

JMIR nursing·2026
Same author

Association between pre-diagnostic prevalence of 30 common diseases and the subsequent risk of dementia: a population-based retrospective cohort study in Taiwan.

Frontiers in aging neuroscience·2026
Same author

Enhancing Model Generalizability in Medical Artificial Intelligence: Systematic Comparison of Categorical Encoding and Sampling Techniques for Imbalanced Data.

JMIR medical informatics·2026
Same author

Chinese Herbal Medicine Is Associated With Improved Survival in Patients With Advanced Lung Cancer Receiving Standard Treatment: A Nationwide Population-Based Propensity Score Matching Study.

Integrative cancer therapies·2026
Same author

Dose-response effect of statins on colorectal cancer risk in IBD: a nationwide cohort study.

BMC cancer·2026
Same author

Machine Learning Prediction of Progression to Dialysis in Patients With Polycystic Kidney Disease: Population-Based Retrospective Cohort Study.

JMIR medical informatics·2026

Related Experiment Video

Updated: Jun 16, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Semiparametric prognosis models in genomic studies.

Shuangge Ma1, Jian Huang, Mingyu Shi

  • 1School of Public Health, Yale University, USA. shuangge.ma@yale.edu

Briefings in Bioinformatics
|February 4, 2010
PubMed
Summary

Genomic studies for complex diseases like cancer require careful model selection. Choosing the wrong statistical model can lead to inaccurate gene identification and poor prediction performance.

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

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

Related Experiment Videos

Last Updated: Jun 16, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 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

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

Area of Science:

  • Genomics
  • Biostatistics
  • Cancer Prognosis

Background:

  • High-throughput technologies enable whole-genome surveys for disease markers.
  • Genomic studies aim to identify predictive markers for complex diseases (e.g., cancer, diabetes, obesity).
  • Existing statistical methods often prioritize marker selection over robust prognosis models.

Purpose of the Study:

  • To review and compare three common prognosis models: Cox, additive risk, and accelerated failure time.
  • To evaluate the impact of model misspecification on gene identification in genomic studies.
  • To assess model dependency in gene identification, prediction performance, and reproducibility using cancer prognosis data.

Main Methods:

  • Simulation studies to assess gene identification under model misspecification.
  • Analysis of three real-world cancer prognosis datasets.
  • Comparison of results across Cox, additive risk, and accelerated failure time models.

Main Results:

  • Gene identification can be unsatisfactory when the underlying statistical model is incorrect.
  • Results of gene identification, combined prediction performance, and gene reproducibility are dependent on the chosen prognosis model.
  • Significant variations observed in analyses of cancer prognosis studies based on the model used.

Conclusions:

  • Model assumption is critical in practical genomic data analysis for disease prognosis.
  • Considering multiple prognosis models is recommended to ensure reliable gene identification and prediction.
  • Careful selection and validation of statistical models are essential for accurate genomic interpretation in complex diseases.