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. Author manuscript; available in PMC: 2018 Oct 30.
Published in final edited form as: Am J Transplant. 2018 Aug 30;18(10):2483–2495. doi: 10.1111/ajt.15040

TABLE 1.

Transition probabilities and model inputs for the Monte Carlo simulation evaluating the cost‐effectiveness of HCV treatment timing among kidney transplant candidates

Probability name Probability Range Source
Demographics
 Age, mean (SD) 56 (9) 35−70 Shelton 2018)10
 Male, % 73.6 60−80
 HCV Metavir stage at
  baseline, median (IQR)
2 (1−3)
Regimen 1 (glecaprevir/pibrentasvir)
 Deatha 0.00001 0.00001−0.01 Gane (2017)1
 Withdrawal 0.04 0.02−0.08 Gane (2017)1
 Toxicity 0.01 0.003−0.03 Gane (2017)1
 SVR 0.98 0.90−1.00 Gane (2017)1
Regimen 2 grazoprevir/elbasvir)
 Deatha 0.00001 0.00001−0.01
 Withdrawal 0.05 0.02−0.08 Roth (2015)2
 Toxicity 0.01 0.003−0.03 Roth (2015)2
 SVR 0.99 0.90−1.00 Roth (2015)
Kohli 2016)2,38
No treatment
 Delisting 0.003 0.006−0.01 Hart (2018)4
 Death due to cirrhosis, rate
 per 100 PY
1.39 0.96−1.85 Bruno (2009)24
Treated
 Transplant OPO‐specific SRTR PSR
 Delisting 0.003 0.006−0.01 Hart (2018)4
 Death on waitlist, annual 0.06 0.03−0.24 USRDS 2017)29

ESRD, end‐stage renal disease; HCV, hepatitis C; IQR, interquartile range; PSR, program specific report; PY, person‐years; SRTR, Scientific Registry of Transplant Recipients; SVR, sustained viral response; USRDS, United States Renal Data System.

a

When disaggregated from background mortality, ESRD‐specific mortality, and HCV‐based mortality, the probability of death was less than zero.