list(N = 60, # Number of cycles
K = 12, # Number of age-sex strata
S = 5, # Number of states in Markov model
M = 3, # Number of studies in evidence synthesis
rho = 0.04, # re-revision rate
lambda.op = 0.01, # post-operative mortality rate
# age-sex specific mean revision hazard for Charnley:
logh0 = c(-6.119, -6.119, -6.119, -6.438, -6.438, -6.438, -6.377, -6.377, -6.377, -6.725, -6.725, -6.725),
tau = 25,
C0 = c(4052, 4402), # set-up costs of primary operation
c = structure(.Data = c(0, 5290, 5290, 0, 0,
0, 5640, 5640, 0, 0), .Dim=c(2,5)), # additional costs associated with each state and prothesis
# (zero except for revision states 2 and 3)
bl = c(1,0,1,1,0), # life-expectancy benefits associated with each state (one except for death states 2 and 5)
bq = c(0.938, -0.622, -0.3387, 0.938, 0), # QALYs associated with each state
delta.c = 0.06, # cost discount
delta.b = 0.06, # health discount
# delta.b = 0.015, # alternative health discount (for sensitivity analysis)
# probablilty of hip replacement by age and sex
p.strata = c(0.02, 0.03, 0.07, 0.13, 0.10, 0.00, 0.02, 0.04, 0.10, 0.22, 0.26, 0.01),
lambda = structure(.Data = c(0.0017, 0.0017, 0.0017, 0.0017, 0.0017, 0.0044, 0.0044,
   0.0044, 0.0044, 0.0044, 0.0044, 0.0044, 0.0044, 0.0044, 0.0044, 0.0138,
   0.0138, 0.0138, 0.0138, 0.0138, 0.0138, 0.0138, 0.0138, 0.0138, 0.0138,
   0.0379, 0.0379, 0.0379, 0.0379, 0.0379, 0.0379, 0.0379, 0.0379, 0.0379,
   0.0379, 0.0912, 0.0912, 0.0912, 0.0912, 0.0912, 0.0912, 0.0912, 0.0912,
   0.0912, 0.0912, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958,
   0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.0044,
   0.0044, 0.0044, 0.0044, 0.0044, 0.0138, 0.0138, 0.0138, 0.0138, 0.0138,
   0.0138, 0.0138, 0.0138, 0.0138, 0.0138, 0.0379, 0.0379, 0.0379, 0.0379,
   0.0379, 0.0379, 0.0379, 0.0379, 0.0379, 0.0379, 0.0912, 0.0912, 0.0912,
   0.0912, 0.0912, 0.0912, 0.0912, 0.0912, 0.0912, 0.0912, 0.1958, 0.1958,
   0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958,
   0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958,
   0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.0138, 0.0138, 0.0138, 0.0138,
   0.0138, 0.0379, 0.0379, 0.0379, 0.0379, 0.0379, 0.0379, 0.0379, 0.0379,
   0.0379, 0.0379, 0.0912, 0.0912, 0.0912, 0.0912, 0.0912, 0.0912, 0.0912,
   0.0912, 0.0912, 0.0912, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958,
   0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958,
   0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958,
   0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958,
   0.1958, 0.1958, 0.0379, 0.0379, 0.0379, 0.0379, 0.0379, 0.0912, 0.0912,
   0.0912, 0.0912, 0.0912, 0.0912, 0.0912, 0.0912, 0.0912, 0.0912, 0.1958,
   0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958,
   0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958,
   0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958,
   0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958,
   0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.0912,
   0.0912, 0.0912, 0.0912, 0.0912, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958,
   0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958,
   0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958,
   0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958,
   0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958,
   0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958,
   0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958,
   0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958,
   0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958,
   0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958,
   0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958,
   0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958,
   0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958, 0.1958,
   0.1958, 0.1958, 0.0011, 0.0011, 0.0011, 0.0011, 0.0011, 0.0028, 0.0028,
   0.0028, 0.0028, 0.0028, 0.0028, 0.0028, 0.0028, 0.0028, 0.0028, 0.0081,
   0.0081, 0.0081, 0.0081, 0.0081, 0.0081, 0.0081, 0.0081, 0.0081, 0.0081,
   0.022, 0.022, 0.022, 0.022, 0.022, 0.022, 0.022, 0.022, 0.022, 0.022,
   0.0578, 0.0578, 0.0578, 0.0578, 0.0578, 0.0578, 0.0578, 0.0578, 0.0578,
   0.0578, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503,
   0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.0028, 0.0028,
   0.0028, 0.0028, 0.0028, 0.0081, 0.0081, 0.0081, 0.0081, 0.0081, 0.0081,
   0.0081, 0.0081, 0.0081, 0.0081, 0.022, 0.022, 0.022, 0.022, 0.022,
   0.022, 0.022, 0.022, 0.022, 0.022, 0.0578, 0.0578, 0.0578, 0.0578,
   0.0578, 0.0578, 0.0578, 0.0578, 0.0578, 0.0578, 0.1503, 0.1503, 0.1503,
   0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503,
   0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503,
   0.1503, 0.1503, 0.1503, 0.1503, 0.0081, 0.0081, 0.0081, 0.0081, 0.0081,
   0.022, 0.022, 0.022, 0.022, 0.022, 0.022, 0.022, 0.022, 0.022, 0.022,
   0.0578, 0.0578, 0.0578, 0.0578, 0.0578, 0.0578, 0.0578, 0.0578, 0.0578,
   0.0578, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503,
   0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503,
   0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503,
   0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503,
   0.022, 0.022, 0.022, 0.022, 0.022, 0.0578, 0.0578, 0.0578, 0.0578,
   0.0578, 0.0578, 0.0578, 0.0578, 0.0578, 0.0578, 0.1503, 0.1503, 0.1503,
   0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503,
   0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503,
   0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503,
   0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503,
   0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.0578, 0.0578, 0.0578,
   0.0578, 0.0578, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503,
   0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503,
   0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503,
   0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503,
   0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503,
   0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503,
   0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503,
   0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503,
   0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503,
   0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503,
   0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503,
   0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503,
   0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503, 0.1503
   ), .Dim=c(12,60)),
   # Amount health care provider is willing to pay for each additional QALY
   KK = c(200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000, 2200, 2400, 2600, 2800, 3000, 3200, 3400, 3600, 3800, 4000,
4200, 4400, 4600, 4800, 5000, 5200, 5400, 5600, 5800, 6000, 6200, 6400, 6600, 6800, 7000, 7200, 7400, 7600, 7800, 8000,
8200, 8400, 8600, 8800, 9000, 9200, 9400, 9600, 9800, 10000, 10200, 10400, 10600, 10800, 11000,
11200, 11400, 11600, 11800, 12000, 12200, 12400, 12600, 12800, 13000, 13200, 13400, 13600, 13800, 14000,
14200, 14400, 14600, 14800, 15000, 15200, 15400, 15600, 15800, 16000, 16200, 16400, 16600, 16800, 17000,
17200, 17400, 17600, 17800, 18000, 18200, 18400, 18600, 18800, 19000, 19200, 19400, 19600, 19800, 20000),
# Evidence
rC = c(1683, 7, 33), # number of revisions for each study (Charnley)
nC = c(28525, 200, 208), # number of operations for each study (Charnley)
rS = c(28, 9, 69), # number of revisions for each study (Stanmore)
nS = c(865, 213, 982), # number of operations for each study (Stanmore)
# Quality weights for each study
qualweights = c(0.5, 1, 0.2)
# qualweights = c(0.1, 1, 0.05) # alternative quality weights for sensitivity analysis
)