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 Experiment Videos

SAS macro program for non-homogeneous Markov process in modeling multi-state disease progression.

Wu Hui-Min1, Yen Ming-Fang, Tony Hsiu-Hsi Chen

  • 1College of Public Health, Institute of Epidemiology, National Taiwan University, Taipei, Taiwan.

Computer Methods and Programs in Biomedicine
|June 24, 2004
PubMed
Summary

This study introduces a flexible SAS macro program for modeling multi-state disease processes in cancer and chronic diseases. The program efficiently estimates transition parameters, aiding complex health outcome predictions.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Cost-Effectiveness of Fecal Immunochemical Testing Alone vs Co-Testing With Helicobacter pylori Stool Antigen.

JAMA·2026
Same author

Early alcohol initiation is associated with higher lifetime Parkinson's disease risk after accounting for exposure latency.

Journal of Parkinson's disease·2026
Same author

Towards hepatitis C elimination with integrated risk-based screening and decentralized care in Taiwan.

Nature communications·2026
Same author

Fecal Hemoglobin-Guided Precision Postpolypectomy Surveillance Within the Current Framework: A Four-Million-Participant Fecal Immunochemical Test-Based Screening Study.

Gastroenterology·2026
Same author

Longitudinal Clinical Outcomes of Tissue-Level Dental Implants in a Periodontal Practice: A Retrospective Study.

Clinical implant dentistry and related research·2026
Same author

Imaging biomarkers are key to recognizing fatal breast cancers arising in the major ducts.

European journal of radiology·2025

Area of Science:

  • Biostatistics
  • Computational Biology
  • Health Informatics

Background:

  • Modeling multi-state disease progression, such as in cancer or chronic conditions, is complex and resource-intensive.
  • Existing computational methods for disease process modeling can be time-consuming and lack flexibility.

Purpose of the Study:

  • To develop a versatile SAS macro program for estimating transition parameters in multi-state disease models.
  • To provide a user-friendly tool for analyzing disease progression using Markov models and incorporating covariates.

Main Methods:

  • Developed a SAS macro program utilizing SAS IML for parameter estimation.
  • Implemented capabilities for homogeneous and non-homogeneous Markov models (e.g., Weibull, log-logistic).
  • Integrated covariate incorporation via proportional hazards and maximum likelihood estimation (MLE).

Related Experiment Videos

Main Results:

  • The SAS macro program successfully estimated transition parameters for a three-state colorectal cancer progression model.
  • Demonstrated flexibility in specifying model types and incorporating covariates.
  • The program calculates transition probabilities, likelihood functions, MLE, and 95% confidence intervals.

Conclusions:

  • The developed SAS macro program offers a flexible and efficient solution for modeling complex multi-state disease processes.
  • This tool can be generalized to various k-state models with multiple covariates, applicable to diverse chronic diseases and cancers.
  • Facilitates advanced statistical analysis in health research and clinical studies.