Improving the accuracy of long‐term prognostic estimates in hepatitis C virus infection

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Abstract
Summary. Obtaining unbiased estimates of HCV prognosis is difficult because of potential biases associated with study design and calculation methods. We propose a new method for estimating fibrosis progression rates. A Markov model with fibrosis health states (F0–F4) was created. The maximum likelihood method was used to estimate stage‐specific progression rates. We compared the standard method to the new method using two well‐known cohort studies. The known stage distribution at the end of follow‐up was compared with stage predicted by the Markov model using both methods of calculating transition rates. We also compared rates obtained using both methods to known fibrosis rates in a series of Monte Carlo simulations. For Kenny‐Walsh's study (1999), transition rates between F0–F1, F1–F2, F2–F3, and F3–F4 were 0.042, 0.045, 0.097 and 0.070 fibrosis units/year (new method) and 0.045 units/year (standard method). The new method predicted fibrosis stage and known transition rates in Monte Carlo simulations more accurately. The standard method underestimates 30‐year cirrhosis rates by up to 40%. The new (Markov maximum likelihood or MML) method allows accurate estimation of stage‐specific transition probabilities from the many studies in which only a single biopsy is available. Application of the method supports the hypothesis that rates of fibrosis vary between stages.