Enhancing binary dose-response analysis in clinical and translational research: Leveraging grouped data techniques in R

Abstract

Background: In pharmacometric analyses, binary dose-response outcome data are used to understand drug potency through the pharmacologic parameter, Effective dose 50 (ED50). Optimal treatment strategies can be developed by characterizing a drug’s dose-response curve, which provides insights into the theoretical maximum effect and the steepness of the curve in response to changes in dose or exposure. Approaches for analyzing group-level response data have not been systematically described in pharmacometric literature, although they are commonly applied in the statistical literature. Aim: This study demonstrates the use of R to analyze grouped or ungrouped binary data, with a focus on pharmacometric applications. Methods: Simulated data were generated to represent a hypothetical Phase 2, dose-ranging, placebo-controlled, randomized clinical trial of drug X, consisting of 250 participants randomized into five distinct cohorts, including a single placebo arm. Linear and non-linear Emax models were fit to the simulated data. Results: Both grouped and ungrouped data approaches produced identical final parameter estimates in logistic regression using the linear and Emax models. The same ED50 value for drug X was obtained from both approaches in the Emax model. Conclusion: This study demonstrates the various methods by which summary- or subject-level binary data can be analyzed using R to model binary response data. Relevance for patients: This work helps bridge the gap between statistical and pharmacometric analysis techniques in the context of binary data analysis. This type of data may facilitate comparative assessments of drug potency and maximal effect using publicly available information from scientific publications or regulatory approval documents.

Keywords

Generalized linear models / Generalized non-linear models / Logistic regression / R software / Binary data / Dose-response analysis

Funding

None.

Conflict of interest

Jenny-Hoa Q. Nguyen, Fudan Zheng, Yuan Xiong, and Mahesh N. Samtani are employees and stockholders of Johnson & Johnson (JNJ). This work was conducted as part of fulfilling responsibilities within the company.

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