Power analysis for idiographic (within-subject) clinical trials: Implications for treatments of rare conditions and precision medicine

• Published in: Behavior research methods Vol. 55; no. 8; pp. 4175 - 4199
• Main Authors: Tueller, Stephen, Ramirez, Derek, Cance, Jessica D., Ye, Ai, Wheeler, Anne C., Fan, Zheng, Hornik, Christoph, Ridenour, Ty A.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2023; Springer Nature B.V
• Subjects: Behavioral Science and Psychology; Clinical trials; Cognitive Psychology; Humans; Mathematical models; Medical research; Models, Statistical; Monte Carlo Method; Monte Carlo simulation; Precision Medicine; Psychology; Rare Diseases; Sample Size; Statistical analysis; Rare diseases; Precision medicine; Within-subject clinical trials; Effect size; Idiographic; Hierarchical modeling; Piecewise regression; Statistical power; Monte Carlo simulation
• ISSN: 1554-3528; 1554-351X; 1554-3528
• DOI: 10.3758/s13428-022-02012-1

Power analysis informs a priori planning of behavioral and medical research, including for randomized clinical trials that are nomothetic (i.e., studies designed to infer results to the general population based on inter individual variabilities). Far fewer investigations and resources are available for power analysis of clinical trials that follow an idiographic approach, which emphasizes intra individual variabilities between baseline (control) phase versus one or more treatment phases. We tested the impact on statistical power to detect treatment outcomes of four idiographic trial design factors that are under researchers’ control, assuming a multiple baseline design: sample size, number of observations per participant, proportion of observations in the baseline phase, and competing statistical models (i.e., hierarchical modeling versus piecewise regression). We also tested the impact of four factors that are largely outside of researchers’ control: population size, proportion of intraindividual variability due to residual error, treatment effect size, and form of outcomes during the treatment phase (phase jump versus gradual change). Monte Carlo simulations using all combinations of the factors were sampled with replacement from finite populations of 200, 1750, and 3500 participants. Analyses characterized the unique relative impact of each factor individually and all two-factor combinations, holding all others constant. Each factor impacted power, with the greatest impact being from larger treatment effect sizes, followed respectively by more observations per participant, larger samples, less residual variance, and the unexpected improvement in power associated with assigning closer to 50% of observations to the baseline phase. This study’s techniques and R package better enable a priori rigorous design of idiographic clinical trials for rare diseases, precision medicine, and other small-sample studies.