Longitudinal Trial Design: A Software – Assisted Approach to Power and Sample Size Optimization in Pharmacological Studies

Yashpreet Malhotra

University of New Haven, New Haven, Connecticut 06516, USA

yashmalhotra9323@gmail.com

DOI: 10.46793/BISEC25.345M

ABSTRACT: Ensuring adequate statistical power is paramount in longitudinal clinical trials evaluating pharmaceutical interventions. Underpowered studies can lead to unreliable conclusions regarding drug efficacy. This paper introduces a computational framework, implemented as an R package and a user-friendly web application, to facilitate robust sample size and power calculations specifically for longitudinal data arising in pharmacological research. The methodology encompasses various statistical models commonly employed in analyzing repeated measures in treatment versus control settings. Utilizing illustrative examples relevant to pharmaceutical outcomes, such as disease progression in neurodegenerative conditions and changes in physiological markers under drug administration, we demonstrate the utility of this software in optimizing study design parameters. Further- more, the application allows researchers to incorporate pilot data, potentially derived from large-scale initiatives like the Alzheimer’s Disease Neuroimaging Initiative (ADNI), to enhance the precision of these crucial computations, thereby improving the rigor and ethical conduct of pharmaceutical trials.

KEYWORDS: Longitudinal data analysis, Sample size determination, Power analysis, Linear mixed-effects models (LMM), Mixed models for repeated measures (MMRM), Generalized estimating equations (GEE), Attrition adjustment, Variance–covariance structures, Clinical trial design, Statistical efficiency, Alzheimer’s Disease Neuroimaging Initiative (ADNI), R statistical software, Shiny web application.

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