CDOoDocuments.StdDocumentDescDocuments.DocumentDescContainers.ViewDescViews.ViewDescStores.StoreDescDocuments.ModelDescContainers.ModelDescModels.ModelDescStores.ElemDescV NTextViews.StdViewDescTextViews.ViewDescTextModels.StdModelDescTextModels.ModelDesc{saTextModels.AttributesDesc'*G ?ArialG>>< <(Times New RomanPS*uTTextRulers.StdRulerDescTextRulers.RulerDescTextRulers.StdStyleDescTextRulers.StyleDescZTextRulers.AttributesDesc$ ZGo*uTsY?$|| ZGo/*uTsY?$ ZGo*uTsY?$ ZGo*uTsY?$ ZGoU*uTw]C( ZGo*uTsY?$ ZGol*uTY?$|| ZGo WinBUGS document - DEPRESSION STUDY (Paul Miller's depression data) Binary effectiveness measure Analyse efficacy and cost data assuming that both are normally distributed. Assume non-informative prior. A. The model model { for(j in 1:N1) { X1[ j, 1:2 ] ~ dmnorm( alpha1[], tau1[,] ) } for(j in 1:N2) { X2[ j, 1:2 ] ~ dmnorm( alpha2[], tau2[,] ) } alpha1[ 1:2 ] ~ dmnorm( m1[], w1[,] ) alpha2[1:2] ~ dmnorm( m21[], w21[,] ) for(t in 1:2) { alpha1dif[t] <- alpha1[t] - m1[t] } for(k in 1:2) { m21[k] <- m2[k] + inprod( c[k,], alpha1dif[] ) } tau1[ 1:2, 1:2 ] ~ dwish( A1[,], f1 ) tau2[ 1:2, 1:2 ] ~ dwish( A2[,], f2 ) for(k in 1:NK) { Q[k] <- step( (alpha1[2] - alpha2[2]) - K[k] * (alpha1[1] - alpha2[1]) ) } effic <- step(alpha2[1] - alpha1[1]) cheap <- step(alpha1[2] - alpha2[2]) dominant <- effic * cheap } B. Efficacy measure is Good Response; Costs are the total depression related costs. Outcome e1 = 1 if GLOBAL2 score is one, otherwise e1 = 0. list( N1=39, N2=42) X1[,1] X1[,2] 1 135 0 225 0 600.92 0 305 0 457.96 0 1130.48 1 510 0 326.48 0 270 1 120 0 230 0 379.57 0 605.73 0 252.64 0 221.15 0 367.14 0 435.26 1 211.48 0 636.3 0 301.48 1 120 0 222.96 0 1482.36 0 223.01 0 457.79 0 111.54 0 150 1 307.96 1 565.65 1 723.41 0 327.54 0 150 1 120 1 356.73 1 519.14 1 165 0 328.5 1 286.48 0 0 X2[,1] X2[,2] 0 211.75 1 558.91 0 1352.01 0 892.16 0 132.2 0 362.01 1 223.73 0 612.14 0 569.94 1 114.39 0 445.92 1 265.76 0 166.62 1 52.77 1 148.27 1 356.17 0 546.55 0 2651.09 0 1492.61 0 121.22 0 285.85 0 275.39 1 83.84 0 300.39 0 312.7 1 31.15 0 128.08 1 372.52 1 376.39 1 291.73 0 406.7 1 19.39 1 133.39 1 116.4 1 399.19 1 68.6 1 123.88 1 338.84 1 265.3 1 75 0 45 0 204.39 C. The prior. Weak prior information. list( f1=2, f2=2,  m1 = c(0.333,367.68), m2 = c(0.500, 379.29), w1 = structure( .Data=c(0.0000001, 0, 0, 0.000000001), .Dim=c(2,2) ), w21 = structure( .Data=c(0.0000001, 0, 0, 0.000000001), .Dim=c(2,2) ), c = structure( .Data=c(0, 0, 0, 0), .Dim=c(2,2) ), A1 = structure( .Data=c(0.5, 0, 0, 1000), .Dim=c(2,2) ), A2 = structure( .Data=c(0.5, 0, 0, 1000), .Dim=c(2,2) )  ) D. Specifying the C/E acceptability curve  list( NK = 35 K = c(1, 3, 10, 30, 100, 200, 300, 500, 700, 1000, 1500, 2000, 2500, 3000, 4000, 5000, 7500, 10000, 15000, 20000, 25000, 30000, 40000, 50000, 75000, 100000, 150000, 200000, 250000, 300000, 400000, 500000, 750000, 1000000, 5000000) ) E. Starting values list( alpha1 = c(0.333, 367.68), alpha2 = c(0.500, 379.29),  tau1 = structure(  .Data=c(0.5, 0, 0, 1000), .Dim=c(2,2) ), tau2 = structure( .Data=c(0.5, 0, 0, 1000), .Dim=c(2,2) )  )TextControllers.StdCtrlDescTextControllers.ControllerDescContainers.ControllerDescControllers.ControllerDesc aY?$ ZGo *G ,[ @Documents.ControllerDesc Ws8 [h