Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The ONWARDS insulin icodec trials

A round-up of the ONWARDS phase 3 clinical trials evaluating the novel weekly insulin icodec in people with diabetes.
ADVANCED SEARCH
Log in
Top
BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2007

Open Access 01-12-2007 | Research article

Estimation of progression of multi-state chronic disease using the Markov model and prevalence pool concept

Authors: Hui-Chuan Shih, Pesus Chou, Chi-Ming Liu, Tao-Hsin Tung

Published in: BMC Medical Informatics and Decision Making | Issue 1/2007

Login to get access

Abstract

Background

We propose a simple new method for estimating progression of a chronic disease with multi-state properties by unifying the prevalence pool concept with the Markov process model.

Methods

Estimation of progression rates in the multi-state model is performed using the E-M algorithm. This approach is applied to data on Type 2 diabetes screening.

Results

Good convergence of estimations is demonstrated. In contrast to previous Markov models, the major advantage of our proposed method is that integrating the prevalence pool equation (that the numbers entering the prevalence pool is equal to the number leaving it) into the likelihood function not only simplifies the likelihood function but makes estimation of parameters stable.

Conclusion

This approach may be useful in quantifying the progression of a variety of chronic diseases.
Appendix
Available only for authorised users
Literature
1.
Chen HH, Duffy SW, Tabar L: A Markov chain method to estimate the tumour progression rate from preclinical to clinical phase, sensitivity and positive predictive value for mammography in breast cancer screening. The Statistician. 1996, 45: 307-317. 10.2307/2988469.CrossRef
2.
Sharples LD: Use the gibbs sampler to estimate transition rates between grades of coronary disease following cardiac transplantation. Statistics in Medicine. 1993, 12: 1155-1169.CrossRefPubMed
3.
Tabár L, Duffy SW, Vitak B, Chen HH, Prevost TC: The natural history of breast carcinoma: what have we learned from screening?". Cancer. 1999, 86: 449-462. 10.1002/(SICI)1097-0142(19990801)86:3<449::AID-CNCR13>3.0.CO;2-Q.CrossRefPubMed
4.
Chen HH, Duffy SW, Tabar L, Day NE: Markov chain models for progression of breast cancer Part I: tumour attributes and the preclinical screen-detectable phase. Journal of Epidemiology and Biostatistics. 1997, 2: 9-23.
5.
Day NE, Walter SD: Simplified models of screening for chronic disease: estimation procedures from mass screening programs. Biometrics. 1984, 40: 1-14. 10.2307/2530739.CrossRefPubMed
6.
Duffy SW, Chen HH, Tabar L, Day NE: Estimation of mean sojourn time in breast cancer screening using a Markov Chain Model of both entry to and exit from the preclinical detectable phase. Stat Med. 1995, 14: 1531-1543. 10.1002/sim.4780141404.CrossRefPubMed
7.
Kalbfleisch JD, Lawless JF: The analysis of panel data under a Markov assumption. J Am Stat Assoc. 1985, 80: 863-871. 10.2307/2288545.CrossRef
8.
Prevost TC, Launoy G, Duffy SW, Chen HH: Estimating sensitivity and sojourn time in screening for colorectal cancer a comparison of statistical approaches. Am J Epidemiol. 1998, 148: 609-619.CrossRefPubMed
9.
Rothman KJ, Greenland S: Modern Epidemiology. 1998, Philadelphia, Lippincott-Raven
10.
Dempster AP, Laird N, Rubin DB: Maximum likelihood from incomplete data via the E-M algorithm (with discussion). Journal of the Royal Statistical Society (B). 1977, 39: 1-38.
11.
van Oortmarssen GJ, Habbema JDF, Lubbe JTN, van der Maas PJ: A model-based analysis of the HIP-project for breast cancer screening. Int J Can. 1990, 46: 207-213. 10.1002/ijc.2910460211.CrossRef
12.
van Oortmarssen GJ, Boer R, Habbema JD: Modelling issues in cancer screening. Stat Methods Med Res. 1995, 4 (1): 33-54.CrossRefPubMed
13.
Cox DF, Miller HD: The theory of stochastic process. 1965, London, Methuen
14.
Brookmeyer R, Quinn TC: Estimation of current human immunodeficiency virus incidence rates from a cross-sectional survey using early diagnostic tests. Am J Epidemiol. 1995, 141: 166-172.PubMed
15.
Tanner MA: Tools for statistical inference–Methods for the exploration of posterior distribution and likelihood functions. 1996, U.S.A, Springer
16.
Longford NT, Ely M, Hardy R, Wadsworth MEJ: Handling missing data in diaries of alcohol consumption. J R Statist Soc A. 2000, 163: 381-402. 10.1111/1467-985X.00174.CrossRef
17.
Emili M: Mathematica 3.0 Standard Add-On Packages. 1996, London, Wolfram Research
18.
Chou P, Chen HH, Hsiao KJ: Community-based epidemiological study on diabetes in Pu-Li, Taiwan. Diabetes Care. 1992, 15: 81-89. 10.2337/diacare.15.1.81.CrossRefPubMed
19.
Welton NJ, Ades AE: Estimation of markov chain transition probabilities and rates from fully and partially observed data: Uncertainty propagation, evidence synthesis, and model calibration. Med Decis Making. 2005, 25: 633-645. 10.1177/0272989X05282637.CrossRefPubMed
20.
Chen KT, Chen CJ, Fuh MM, Narayan KM: Causes of death and associated factors among patients with non-insulin-dependent diabetes mellitus in Taipei, Taiwan. Diabetes Res Clin Prac. 1999, 43: 101-109. 10.1016/S0168-8227(98)00126-0.CrossRef
Metadata
Title
Estimation of progression of multi-state chronic disease using the Markov model and prevalence pool concept
Authors
Hui-Chuan Shih
Pesus Chou
Chi-Ming Liu
Tao-Hsin Tung
Publication date
01-12-2007
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2007
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/1472-6947-7-34

Other articles of this Issue 1/2007

BMC Medical Informatics and Decision Making 1/2007 Go to the issue

Research article

A simulation study comparing aberration detection algorithms for syndromic surveillance

Research article

Developing a web-based information resource for palliative care: an action-research inspired approach

Research article

Automated real time constant-specificity surveillance for disease outbreaks

Research article

Generating prior probabilities for classifiers of brain tumours using belief networks

Research article

A web-based laboratory information system to improve quality of care of tuberculosis patients in Peru: functional requirements, implementation and usage statistics

Research article

Can postponement of an adverse outcome be used to present risk reductions to a lay audience? A population survey