The International Journal of Biostatistics
Ed. by Chambaz, Antoine / Hubbard, Alan E. / van der Laan, Mark J.
2 Issues per year
IMPACT FACTOR 2014: 0.741
5-year IMPACT FACTOR: 1.475
SCImago Journal Rank (SJR) 2014: 1.247
Source Normalized Impact per Paper (SNIP) 2014: 1.078
Impact per Publication (IPP) 2014: 1.206
Mathematical Citation Quotient (MCQ) 2014: 0.07
Volume 11 (2015)
Volume 7 (2011)
Volume 5 (2009)
Volume 4 (2008)
Volume 3 (2007)
Volume 2 (2006)
Volume 1 (2005)
Most Downloaded Articles
- An Introduction to Causal Inference by Pearl, Judea
- Sample Size Estimation for Repeated Measures Analysis in Randomized Clinical Trials with Missing Data by Lu, Kaifeng/ Luo, Xiaohui and Chen, Pei-Yun
- Survival Models in Health Economic Evaluations: Balancing Fit and Parsimony to Improve Prediction by Jackson, Christopher H/ Sharples, Linda D and Thompson, Simon G
- Survival Curve Estimation with Dependent Left Truncated Data Using Cox's Model by Mackenzie, Todd
- Evaluating treatment effectiveness in patient subgroups: a comparison of propensity score methods with an automated matching approach by Radice, Rosalba/ Ramsahai, Roland/ Grieve, Richard/ Kreif, Noemi/ Sadique, Zia and Sekhon, Jasjeet S.
Non-Markov Multistate Modeling Using Time-Varying Covariates, with Application to Progression of Liver Fibrosis due to Hepatitis C Following Liver Transplant
1University of California, San Francisco
2University of California, San Francisco
3University of California, San Francisco
4University of California, San Francisco
5Hospital Universitario La Fe
Citation Information: The International Journal of Biostatistics. Volume 6, Issue 1, ISSN (Online) 1557-4679, DOI: 10.2202/1557-4679.1213, February 2010
- Published Online:
Multistate modeling methods are well-suited for analysis of some chronic diseases that move through distinct stages. The memoryless or Markov assumptions typically made, however, may be suspect for some diseases, such as hepatitis C, where there is interest in whether prognosis depends on history. This paper describes methods for multistate modeling where transition risk can depend on any property of past progression history, including time spent in the current stage and the time taken to reach the current stage. Analysis of 901 measurements of fibrosis in 401 patients following liver transplantation found decreasing risk of progression as time in the current stage increased, even when controlled for several fixed covariates. Longer time to reach the current stage did not appear associated with lower progression risk. Analysis of simulation scenarios based on the transplant study showed that greater misclassification of fibrosis produced more technical difficulties in fitting the models and poorer estimation of covariate effects than did less misclassification or error-free fibrosis measurement. The higher risk of progression when less time has been spent in the current stage could be due to varying disease activity over time, with recent progression indicating an "active" period and consequent higher risk of further progression.
Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.