Single case designs for early phase behavioral translational research in health psychology

Abstract

Objective: The biomedical research community has long recognized that much of the basic research being conducted, whether in the biological, behavioral or social sciences, is not readily translated into clinical and public health applications. This translational gap is due in part to challenges inherent in moving research findings from basic or discovery research to applied research that addresses clinical or public health problems. In the behavioral and social sciences, research designs typically used in the early phases of translational research are small, underpowered "pilot" studies that may lack sufficient statistical power to test the research question of interest. While this approach is discouraged, these studies are often employed to estimate effect sizes before embarking on a larger trial with adequate statistical power to test the research hypothesis. The goal of this paper is to provide an alternative approach to early phase studies using single case designs (SCDs).

Method: Review basic principles of SCDs; provide a series of hypothetical SCD replication experiments to illustrate (1) how data from SCDs can be analyzed to test the effects of an intervention on behavioral and biological outcomes and (2) how sample sizes can be derived for larger randomized controlled trials (RCTs) based on clinically meaningful effects from SCDs; and review feedback between SCDs and RCTs.

Results: The paper illustrates the use of SCD reversal and multiple baseline designs for early phase translational research.

Conclusion: SCDs provide a flexible and efficient platform for the use of experimental methods in early phase translational research. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Conflict of interest statement

Declaration of Conflicting Interests

The views expressed here are those of the authors only and do not represent any official position of the National Cancer Institute or National Institutes of Health. Although the following activities/relationships do not create a conflict of interest pertaining to this manuscript, in the interest of full disclosure, Dr. Bickel would like to report the following: W. K. Bickel is a principal of HealthSim, LLC; BEAM Diagnostics, Inc.; and Red 5 Group, LLC. In addition, he serves on the scientific advisory board for Sober Grid, Inc. and is a consultant for Alkermes, Inc. and Sandoz Inc. The other authors have no conflict of interest.

Figures

Figure 1.

Hypothetical daily carbohydrate intake (left graphs) and daily average blood glucose values (right graphs) for 4 people with type 2 diabetes who were studied in ABAB conditions in which reliable changes in both carbohydrate intake and blood glucose values were observed.

Figure 2.

Hypothetical daily carbohydrate intake (left graphs) and daily average blood glucose values (right graphs) for 4 people with type 2 diabetes who were studied in ABAB conditions in which reliable changes in carbohydrate intake but not blood glucose values were observed.

Figure 3.

Hypothetical daily carbohydrate intake (left graphs) and daily average blood glucose values (right graphs) for 4 people with type 2 diabetes who were studied in ABAB conditions in which changes in carbohydrate intake were not observed but blood glucose values improved throughout the study.

Figure 4.

Hypothetical daily carbohydrate intake (left graphs) and daily average blood glucose values (right graphs) for 4 people with type 2 diabetes who were studied in ABAB conditions in which reliable changes in carbohydrate intake or blood glucose values were not observed.

Figure 5.

Hypothetical daily carbohydrate intake (left graphs) and daily average blood glucose values (right graphs) for 3 people with type 2 diabetes who were studied in a multiple baseline design in which reliable changes in carbohydrate intake and blood glucose values were observed.