Abstract
Psychological research is confronted with ever-increasing demands to save resources such as time and money while assuring high ethical standards. In medical and pharmaceutical research, group sequential designs have fundamentally changed traditional statistical testing approaches featuring only one analysis at the end of a single-stage study. They enable early stopping at an interim stage, after a group of observations, for efficacy or futility in case of an overwhelmingly large or small effect, respectively. Otherwise, the trial is continued to the next stage. On average over many studies time and money are saved and more ethical trials are facilitated by diminishing the risk of patients' exposure to inferior treatments. We provide an easy-to-use tutorial for psychological research replete with easily understandable figures highlighting the core idea of different group sequential designs, a workflow chart, an empirical real-world data set, and the annotated R code. Finally, we demonstrate the application of early stopping for efficacy.
Highlights
We provide an easy-to-use tutorial for psychological research replete with understandable figures highlighting the core idea of different group sequential designs, a workflow chart, an empirical real-world data set, and the annotated R code
We provide a tutorial based on (i) understandable figures and accompanying explanations of the core idea of group sequential designs, (ii) a workflow chart featuring all of the important steps for a concise application, (iii) the briefly annotated R code for further usage (Weigl & Ponocny, 2020), (vi) a demonstration of how to apply sample size estimation based on the sample size inflation factor (IF), (v) an elucidation of early stopping for efficacy on an illustrative example for a two groups comparison, and (vi) a real-world data set from psychological research for teaching purposes (Weigl & Ponocny, 2020)
We provide an understandable figure of the core idea behind group sequential designs, a work‐ flow chart, the annotated R code, and supply a real-world data set from psychological re‐ search
Summary
Klemens Weigl abc , Ivo Ponocny d [a] Department of Psychology, Catholic University of Eichstätt-Ingolstadt, Eichstätt, Germany. [b] Human-Computer Interaction Group, Technische Hochschule Ingolstadt, Ingolstadt, Germany. [c] Institute for Health Services Research and Clinical Epidemiology, School of Medicine, Philipps-Universitaet Marburg, Marburg, Germany. [d] Department for Sustainability, Governance, and Methods, MODUL University Vienna, Vienna, Austria
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