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1.  Influence of Cannabis Use on Severity of Hepatitis C Disease 
Background
Complications of hepatitis C virus (HCV) infection are primarily related to the development of advanced fibrosis.
Methods
Baseline data from a prospective community-based cohort study of 204 persons with chronic hepatitis C virus (HCV) infection were used for analysis. The outcome was fibrosis score on biopsy and the primary predictor evaluated was daily cannabis use.
Results
The median age of the cohort was 46.8 years, 69.1% were male, 49.0% were Caucasian, and the presumed route of infection was injection drug use in 70.1%. The median lifetime duration and average daily use of alcohol were 29.1 years and 1.94 drink equivalents per day. Cannabis use frequency (within prior 12 months) was daily in 13.7%, occasional in 45.1%, and never in 41.2%. Fibrosis stage, assessed by Ishak method, was F0, F1–2 and F3–6 in 27.5%, 55.4% and 17.2% of subjects, respectively. Daily compared to non-daily cannabis use was significantly associated with moderate to severe fibrosis (F3–6 versus F1–2) in univariate [OR = 3.21 (95% CI, 1.20–8.56), p = 0.020] and multivariate analyses (OR = 6.78, (1.89–24.31), p=0.003). Other independent predictors of F3–6 were ≥11 portal tracts (compared to <5, OR = 6.92 (1.34–35.7), p=0.021] and lifetime duration of moderate and heavy alcohol use [OR per decade = 1.72 (1.02–2.90), p=0.044].
Conclusion
We conclude that daily cannabis use is strongly associated with moderate to severe fibrosis and that HCV-infected individuals should be counseled to reduce or abstain from cannabis use.
doi:10.1016/j.cgh.2007.10.021
PMCID: PMC3184401  PMID: 18166478
fibrosis; alcohol; viral load; marijuana; cirrhosis
2.  Progression of Biopsy-Measured Liver Fibrosis in Untreated Patients with Hepatitis C Infection: Non-Markov Multistate Model Analysis 
PLoS ONE  2011;6(5):e20104.
Background
Fibrosis stages from liver biopsies reflect liver damage from hepatitis C infection, but analysis is challenging due to their ordered but non-numeric nature, infrequent measurement, misclassification, and unknown infection times.
Methods
We used a non-Markov multistate model, accounting for misclassification, with multiple imputation of unknown infection times, applied to 1062 participants of whom 159 had multiple biopsies. Odds ratios (OR) quantified the estimated effects of covariates on progression risk at any given time.
Results
Models estimated that progression risk decreased the more time participants had already spent in the current stage, African American race was protective (OR 0.75, 95% confidence interval 0.60 to 0.95, p = 0.018), and older current age increased risk (OR 1.33 per decade, 95% confidence interval 1.15 to 1.54, p = 0.0002). When controlled for current age, older age at infection did not appear to increase risk (OR 0.92 per decade, 95% confidence interval 0.47 to 1.79, p = 0.80). There was a suggestion that co-infection with human immunodeficiency virus increased risk of progression in the era of highly active antiretroviral treatment beginning in 1996 (OR 2.1, 95% confidence interval 0.97 to 4.4, p = 0.059). Other examined risk factors may influence progression risk, but evidence for or against this was weak due to wide confidence intervals. The main results were essentially unchanged using different assumed misclassification rates or imputation of age of infection.
Discussion
The analysis avoided problems inherent in simpler methods, supported the previously suspected protective effect of African American race, and suggested that current age rather than age of infection increases risk. Decreasing risk of progression with longer time already spent in a stage was also previously found for post-transplant progression. This could reflect varying disease activity, with recent progression indicating active disease and high risk, while longer time already spent in a stage indicates quiescent disease and low risk.
doi:10.1371/journal.pone.0020104
PMCID: PMC3103523  PMID: 21637766
3.  Non-Markov Multistate Modeling Using Time-Varying Covariates, with Application to Progression of Liver Fibrosis due to Hepatitis C Following Liver Transplant* 
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.
doi:10.2202/1557-4679.1213
PMCID: PMC2836212  PMID: 20305705
fibrosis; hepatitis C; liver transplant; memoryless assumptions; multistate modeling

Results 1-3 (3)