
Methods: Data from the 57 epidemiologic studies (liver clinic series n=33, posttransfusion cohorts n=5, blood donor series n=10, and communitybased cohorts n=9) from a published systematic review were used, including fibrosis distribution data (n=5) and cirrhosis data (n=52).
Liver fibrosis states F0F4 were modeled using a continuoustime Markov tunnel model, with F4 denoting the absorbing cirrhosis state. Estimates of timedependent transition probability, expressed as functions of annual transition rates, were derived using Kolmogorov's forward equation.
These estimates were used in a metaanalysis model incorporating both the multinomialdistributed fibrosis data and binomialdistributed cirrhosis data. Both fixed effects and random effects models (i.e., studyspecific F0F1 rates) without and with covariates (i.e., age, cohort type) were used. The fibrosis model was linked with the metaanalysis model using an MPES approach which explicitly reflected both the heterogeneity and uncertainty in the input data.
Vague priors were used for model parameters. Analytical solutions were obtained using WinBugs (2 chains n=21,000 with 1,000 burnins). Convergence was assessed via inspection of MCMC trace and autocorrelation plots and the GelmanRubin ratio. Standardized residuals were used for evidence consistency checking. Longterm predictions of fibrosis distribution were derived from the fitted model.
Results: Annual transition rates were F0F1: 0.06 (95% CI: 0.04, 0.09); F1F2: 0.10 (0.09, 0.103); F2F3: 0.18 (0.16, 0.19), and F3F4: 0.20 (0.17, 0.22). Large standardized residuals were observed for multinomial data relative to those from binomial data.
The cumulative probability of cirrhosis after 20 years was estimated to be 19% (95% CI: 1425%) and varied by metaanalytic models. Patients from community clinics had better prognosis compared to those from liver clinics, rate ratio 0.08 (0.001, 0.18). Estimates of random effects model were generally consistent with or without baseline adjustment.
Conclusion: The MPES approach integrated a decision model with different metaanalytic models thus allowed the synthesis of the evidence base from vastly different study designs. For applications in HCV prognosis, this approach allowed for correction of bias due to study design and adjustment for clinical covariates.
See more of Concurrent Abstracts H: Simulation and Modeling
See more of The 28th Annual Meeting of the Society for Medical Decision Making (October 1518, 2006)