Download Simulation for Designing Clinical Trials: A by Hui Kimko, Stephen B. Duffull PDF

By Hui Kimko, Stephen B. Duffull

Supplying greater than only a finished heritage, severe vocabulary, insightful compilation of motivations, and transparent rationalization of the cutting-edge of recent medical trial simulation, this e-book offers a rigorous framework for using simulation as an test, in line with a predefined simulation plan, that displays reliable simulation practices. The booklet discusses tips on how to medical trial designs based on their chance for fulfillment, options to outline distributions of digital topics' features, easy methods to make certain the sensitivity of the trial layout, and the inhabitants courting among dosing schedules and sufferer reaction.

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Read or Download Simulation for Designing Clinical Trials: A Pharmacokinetic-Pharmacodynamic Modeling Perspective (Drugs and the Pharmaceutical Sciences, Vol 127) PDF

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Additional info for Simulation for Designing Clinical Trials: A Pharmacokinetic-Pharmacodynamic Modeling Perspective (Drugs and the Pharmaceutical Sciences, Vol 127)

Example text

In general, resampling is done with replacement of individuals, so that the new data sets will have different distributions of covariates from the original data set. In clinical trials, there are usually several prognostic factors known or believed to influence the patient’s ability to respond to treatment. Therefore, the clinical trial must be designed so that treatment balance is simultaneously achieved across all such patient factors (9). In generating a virtual patient database using resampling, the criteria for selection of patients with such prognostic factors, such as a particular disease severity, may be governed either by chance or by expectations of enrollment in the actual trial.

Finally, for a random variable x, the geometric mean GM(x) ϭ exp(mean(log(x))). As above, ln(CLpop) is the mean of ln(CL); hence GM(CL) ϭ CLpop. In summary, CLpop is equal to the GM(CL), the median(CL), and the mode(CL), but CL pop is not equal to the mean(CL). To determine mean(CL), one must employ the formulas used above. A complete vector of covariates X′ ϭ (X′1, X′2, . . , X′n) can be created for each virtual patient by randomly sampling independently for covariate information from each univariate distribution.

Therefore, the clinical trial must be designed so that treatment balance is simultaneously achieved across all such patient factors (9). In generating a virtual patient database using resampling, the criteria for selection of patients with such prognostic factors, such as a particular disease severity, may be governed either by chance or by expectations of enrollment in the actual trial. For example, an early phase study in patients with mild to moderate disease might be expected to enroll a greater proportion of patients with moderate disease than mild disease.

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