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3 Aug 2022: t in 1:N) { Lambda[t,1,1] - 1 - gamma[t] - lambda[t] Lambda[t,1,2] - gamma[t] lambda.op Lambda[t,1,3] - gamma[t] (1-lambda.op) ... t] Lambda[t,4,1] - 0 Lambda[t,4,2] - rho lambda.op Lambda[t,4,3] - rho (1- lambda.op) Lambda[t,4,4] - 1 - rho - lambda[t]

3 Aug 2022: op Lambda[k,t,1,3] - gamma[k,t] (1-lambda.op) Lambda[k,t,1,4] - 0 Lambda[k,t,1,5] - lambda[k,t] Lambda[k,t,2,1] - ... t,4,1] - 0 Lambda[k,t,4,2] - rho lambda.op Lambda[k,t,4,3] - rho (1- lambda.op) Lambda[k,t,4,4] - 1 - rho - lambda[k,t]

3 Aug 2022: n,t,1,2] - gamma[n,t] lambda.op Lambda[n,t,1,3] - gamma[n,t] (1-lambda.op) Lambda[n,t,1,4] - 0 Lambda[n,t,1,5] - ... 5] - lambda[t] Lambda[n,t,4,1] - 0 Lambda[n,t,4,2] - rho lambda.op Lambda[n,t,4,3] - rho (1- lambda.op) Lambda[n,t,4,4] -

3 Aug 2022: k,t] Lambda[k,t,1,2] - gamma[k,t] lambda.op Lambda[k,t,1,3] - gamma[k,t] (1-lambda.op) Lambda[k,t,1,4] - 0 ... t] Lambda[k,t,3,5] - lambda[k,t] Lambda[k,t,4,1] - 0 Lambda[k,t,4,2] - rho lambda.op Lambda[k,t,4,3] - rho (1-

3 Aug 2022: t] lambda.op Lambda[k,t,1,3] - gamma[k,t] (1-lambda.op) Lambda[k,t,1,4] - 0 Lambda[k,t,1,5] - lambda[k,t] ... Lambda[k,t,4,1] - 0 Lambda[k,t,4,2] - rho lambda.op Lambda[k,t,4,3] - rho (1- lambda.op) Lambda[k,t,4,4] - 1 - rho -

3 Aug 2022: op = 0.01, # post-operative mortality rate # age-sex specific revision hazard: tau = 25, # sd of 1/sqrt(25) =. 2 on log-scale corresponds to 95% interval including /- 50% hazard uncertainty

3 Aug 2022: 0.04, # re-revision rate lambda.op = 0.01, # post-operative mortality rate # age-sex specific revision hazard: # age-sex specific revision hazard: tau = 25, # sd of 1/sqrt(25) =. 2

3 Aug 2022: lambda.op)Lambda[k, t, 1, 4] <- 0Lambda[k, t, 1, 5] <- lambda[k, t]. ... 3] <- 0 ;pi[k,1, 4] <- 0 pi[k,1, 5] <- lambda.op. #

3 Aug 2022: Lambda[n,k,t,1,2] - gamma[n,k,t] lambda.op Lambda[n,k,t,1,3] - gamma[n,k,t] (1-lambda.op) Lambda[n,k,t,1,4] - ... n,k,t,4,2] - rho lambda.op Lambda[n,k,t,4,3] - rho (1- lambda.op) Lambda[n,k,t,4,4] - 1 - rho - lambda[k,t]

3 Aug 2022: Bayesian cost-effectiveness modelling 7. Post-op survival after revision THR. (state 3).

3 Aug 2022: UNPHASEDVersion 3.0.7. User guide. by. Frank DudbridgeMRC Biostatistics Unit. Cambridge, UK. March 2007. ContentsContents. 2. Introduction. 4 Running UNPHASED. 5. Installation. 5 Starting UNPHASED. 5 Input files. 7. Pedigree file. 7 Data file. 8

3 Aug 2022: WinBUGS Cumulative Patch No. 3 (Version 1.4.3), August 6, 2007 1. Start your copy of WinBUGS 1.4 and open this file from within it. 2. Select "Decode" from the "Tools" menu -- the "Decode" dialogue box should appear. 3. Click on the "Decode All"