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Tutorial for $DESIGN in NONMEM: Clinical trial evaluation and optimization.

ABSTRACT: This NONMEM tutorial shows how to evaluate and optimize clinical trial designs, using algorithms developed in design software, such as PopED and PFIM 4.0. Parameter precision and model parameter estimability is obtained by assessing the Fisher Information Matrix (FIM), providing expected model parameter uncertainty. Model parameter identifiability may be uncovered by very large standard errors or inability to invert an FIM. Because evaluation of FIM is more efficient than clinical trial simulation, more designs can be investigated, and the design of a clinical trial can be optimized. This tutorial provides simple and complex pharmacokinetic/pharmacodynamic examples on obtaining optimal sample times, doses, or best division of subjects among design groups. Robust design techniques accounting for likely variability among subjects are also shown. A design evaluator and optimizer within NONMEM allows any control stream first developed for trial design exploration to be subsequently used for estimation of parameters of simulated or clinical data, without transferring the model to another software. Conversely, a model developed in NONMEM could be used for design optimization. In addition, the $DESIGN feature can be used on any model file and dataset combination to retrospectively evaluate the model parameter uncertainty one would expect given that the model generated the data, particularly if outliers of the actual data prevent a reasonable assessment of the variance-covariance. The NONMEM trial design feature is suitable for standard continuous data, whereas more elaborate trial designs or with noncontinuous data-types can still be accomplished in optimal design dedicated software like PopED and PFIM.

SUBMITTER: Bauer RJ

PROVIDER: S-EPMC8674001 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Conventional dissemination of clinical trial results has inconsistent impact on physician practices. A more comprehensive plan to influence determinants of prescribing practices is warranted.To report the response from the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial to the National Heart, Lung, and Blood Institute's requirement for dissemination and evaluation of trials with potential immediate public health applicability.ALLHAT's dissemination plan had two-components: (1) a traditional approach of media coverage, scientific presentation, and publication; and (2) a theory-based approach targeting determinants of clinician behavior. Strategies included: (1) academic detailing, in which physicians approach colleagues regarding blood pressure management, (2) direct patient messages to stimulate communication with physicians regarding blood pressure control, (3) approaches to formulary systems to use educational and economic incentives for evidence-based prescription, and (4) direct professional organization appeals to clinicians.One hundred and forty-seven Investigator Educators reported 1698 presentations to 18,524 clinicians in 41 states and the District of Columbia. The pre- and post-test responses of 1709 clinicians in the face-to-face meetings indicated significant changes in expectations for positive patient outcomes and intention to prescribe diuretics. Information was mailed to 55 individuals representing 20 professional organizations and to eight formulary systems. Direct-to-patient messages were provided to 14 sites that host patient newsletters and Web sites such as health plans and insurance companies, 62 print mass media outlets, and 12 broadcast media sites.It was not within the scope of the project to conduct a randomized trial of the impact of the dissemination. However, impact evaluation using quasi-experimental designs is ongoing.A large multi-method dissemination of clinical trial results is feasible. Planning for dissemination efforts, including evaluation research, should be considered as a part of the funding and design of the clinical trial and should begin early in trial planning.

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Tutorial for $DESIGN in NONMEM: Clinical trial evaluation and optimization.

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