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Receiver Operating Characteristic Analysis and Clinical Trial Simulation to Inform Dose Titration Decisions.

ABSTRACT: Optimal dose selection in clinical trials is problematic when efficacious and toxic concentrations are close. A novel quantitative approach follows for optimizing dose titration in clinical trials. A system of pharmacokinetics (PK), pharmacodynamics, efficacy, and toxicity was simulated for scenarios characterized by varying degrees of different types of variability. Receiver operating characteristic (ROC) and clinical trial simulation (CTS) were used to optimize drug titration by maximizing efficacy/safety. The scenarios included were a low-variability base scenario, and high residual (20%), interoccasion (20%), interindividual (40%), and residual plus interindividual variability scenarios, and finally a shallow toxicity slope scenario. The percentage of subjects having toxicity was reduced by 87.4% to 93.5%, and those having efficacy was increased by 52.7% to 243%. Interindividual PK variability may have less impact on optimal cutoff values than other sources of variability. ROC/CTS methods for optimizing dose titration offer an individualized approach that leverages exposure-response relationships.

SUBMITTER: Clements JD

PROVIDER: S-EPMC6263661 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Receiver Operating Characteristic Analysis and Clinical Trial Simulation to Inform Dose Titration Decisions.

Clements John David JD Perez Ruixo Juan Jose JJ Gibbs John P JP Doshi Sameer S Perez Ruixo Carlos C Melhem Murad M

CPT: pharmacometrics & systems pharmacology 20181015 11

Optimal dose selection in clinical trials is problematic when efficacious and toxic concentrations are close. A novel quantitative approach follows for optimizing dose titration in clinical trials. A system of pharmacokinetics (PK), pharmacodynamics, efficacy, and toxicity was simulated for scenarios characterized by varying degrees of different types of variability. Receiver operating characteristic (ROC) and clinical trial simulation (CTS) were used to optimize drug titration by maximizing eff ...[more]

PMID: 30246497

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Project description:BackgroundDisease prevalence is rarely explicitly considered in the early stages of the development of novel prognostic tests. Rather, researchers use the area under the receiver operating characteristic (AUROC) as the key metric to gauge and report predictive performance ability. Because this statistic does not account for disease prevalence, proposed tests may not appropriately address clinical requirements. This ultimately impedes the translation of prognostic tests into clinical practice.MethodsA method to express positive- and/or negative predictive value criteria (PPV, NPV) within the ROC space is presented. Equations are derived for so-called equi-PPV (and equi-NPV) lines. Herewith it is possible, for any given prevalence, to plot a series of sensitivity-specificity pairs which meet a specified PPV (or NPV) criterion onto the ROC space.This concept is introduced by firstly reviewing the well-established "mechanics", strengths and limitations of the ROC analysis in the context of developing prognostic models. Then, the use of PPV (and/or) NPV criteria to augment the ROC analysis is elaborated.Additionally, an interactive web tool was also created to enable people to explore the dynamics of lines of equi-predictive value in function of prevalence. The web tool also allows to gauge what ROC curve shapes best meet specific positive and/or negative predictive value criteria (http://d4ta.link/ppvnpv/).ResultsTo illustrate the merits and implications of this concept, an example on the prediction of pre-eclampsia risk in low-risk nulliparous pregnancies is elaborated.ConclusionsIn risk stratification, the clinical usefulness of a prognostic test can be expressed in positive- and negative predictive value criteria; the development of novel prognostic tests will be facilitated by the possibility to co-visualise such criteria together with ROC curves. To achieve clinically meaningful risk stratification, the development of separate tests to meet either a pre-specified positive value (rule-in) or a negative predictive value (rule-out) criteria should be considered: the characteristics of successful rule-in and rule-out tests may markedly differ.

Dataset Information

Receiver Operating Characteristic Analysis and Clinical Trial Simulation to Inform Dose Titration Decisions.

Publications

Receiver Operating Characteristic Analysis and Clinical Trial Simulation to Inform Dose Titration Decisions.

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