Choice of Baselines in Clinical Trials: A Simulation Study from Statistical Power Perspective

Abstract

Multiple assessments of an efficacy variable are often conducted prior to the initiation of randomized treatments in clinical trials as baseline information. Two goals are investigated in this article, where the first goal is to investigate the choice of these baselines in the analysis of covariance (ANCOVA) to increase the statistical power, and the second to investigate the magnitude of power loss when a continuous efficacy variable is dichotomized to categorical variable as commonly reported the biomedical literature. A statistical power analysis is developed with extensive simulations based on data from clinical trials in study participants with end stage renal disease (ESRD). It is found that the baseline choices primarily depend on the correlations among the baselines and the efficacy variable, with substantial gains for correlations greater than 0.6 and negligible for less than 0.2. Continuous efficacy variables always give higher statistical power in the ANCOVA modeling and dichotomizing the efficacy variable generally decreases the statistical power by 25%, which is an important practicum in designing clinical trials for study sample size and realistically budget. These findings can be easily applied in and extended to other clinical trials with similar design.

Keywords:

• ANCOVA
• Baselines
• Clinical trials
• Power analysis

Mathematics Subject Classification:

• 62J10
• 62J07

Acknowledgments

The authors would like to thank Professor Balakrishnan and the anonymous referee for their suggestions and comments which significantly improved this manuscript.

Notes

N = “No baseline;” B1 = “First baseline only;” B2 = “Second baseline only;” A = “Average of both baselines.”