discrete-event simulation in clinical trials - Institut für Statistik ...

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Mixed model ‣ Linear mixed model Y i =X β + Z b + ε i i i i ‣ Fitted to data: • Parameter estimation • Parameter CI • Tests of goodness of fit 22
Types of simulations in function of “t”: Continous Discrete Discrete events. The models based on simulation of discreet events have sufficient flexibility and adequacy to the clinical reality to be used, on the other way round, the trees of decision and the models of Markov have been the methods used with major frequency in the evaluations farmacoeconomics, and very rarely in the simulation of clinical trials (Caro JJ. Pharmacoeconomics 23(4):323-332 2005 ). Time (t) General planning in discrete time events: INICIALIZATION t = 0; Initialize states of the system and the statistical counters; Initialize events list; PRINCIPAL PROGRAM If simulation time < time rule Do: counters; list; End; RESULTS Determine type of next event; Advance simulation time Up date condition system + statistical Generate future events + put in the event Compute + print interesting estimations; 23
Page 1 and 2: DRUGS CLINICAL TRIAL OPTIMIZATION U
Page 3 and 4: Schedule • Clinical trial framewo
Page 5 and 6: Randomization 5
Page 7 and 8: The bio-pharmacological basis of cl
Page 9 and 10: Dk k [ exp( −ket) − exp( −kat
Page 11 and 12: Simulation general framework
Page 13 and 14: Modelization/Simulation process Sta
Page 15 and 16: Sargent paradigm 15
Page 17 and 18: Statistic phases of the process ‣
Page 19 and 20: Operational validation: methodologi
Page 21: Mixed model: General form ‣ Linea
Page 25 and 26: SIMULATION METHODOLOGY: Statistical
Page 27 and 28: Realistic simulation by means of di
Page 29 and 30: STATISTICAL MODELLING 1-Disease pro
Page 31 and 32: N=144 Start of CT W(10) 0 months Ti
Page 33 and 34: Determination of the ending moment
Page 35 and 36: ‣Validation/Verification methodol
Page 37: Vielen dank für Ihre Aufmerksamkei

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