The clinical trials mosaic: Toward a range of clinical trials designs to optimize evidence-based treatment

Abstract

Objective: Dichotomizing clinical trials designs into nomothetic (e.g., randomized clinical trials or RCTs) versus idiographic (e.g., N-of-1 or case studies) precludes use of an array of hybrid designs and potential research questions between these extremes. This paper describes unique clinical evidence that can be garnered using idiographic clinical trials (ICTs) to complement RCT data. Proposed and illustrated herein is that innovative combinations of design features from RCTs and ICTs could provide clinicians with far more comprehensive information for testing treatments, conducting pragmatic trials, and making evidence-based clinical decisions. Method: Mixed model trajectory analysis and unified structural equations modeling were coupled with multiple baseline designs in (a) a true N-of-1 pilot study to improve severe autism-related communication deficits and (b) a small sample preliminary study of two complimentary interventions to relieve wheelchair discomfort. Results: Evidence supported certain mechanisms of treatment outcomes and ruled out others. Effect sizes included mean phase differences (i.e., effectiveness), trajectory slopes, and differences in path coefficients between study phases. Conclusions: ICTs can be analyzed with equivalent rigor as, and generate effect sizes comparable to, RCTs for the purpose of developing hybrid designs to augment RCTs for pilot testing innovative treatment, efficacy research on rare diseases or other small populations, quantifying within-person processes, and conducting clinical trials in many situations when RCTs are not feasible.

Precision Strategies as a Timely and Unifying Framework for Ongoing Prevention Science Advances

“…Prevention science has long relied on methods that are designed to test population-oriented hypotheses such as efficacy. More recently, methods have been evolving to intensively investigate intra-individual processes over time (e.g., in response to an intervention) concurrently with interindividual differences (Howe et al 2010;Ridenour et al 2017;Voelkle et al 2014). Such methodologies could permit prevention scientists to carefully identify the mechanisms whereby an intervention is able (or fails) to influence individuals toward desired outcomes and in turn refine the intervention to attain stronger impacts (Tryon 2018).…”

Sustaining Physical Activity Following Cardiac Rehabilitation Discharge

“…The within-subject study design and analyses are based on previous small sample clinical trials. [22][23][24][25] The outcomes tested for the intervention included Here is what one gentleman wanted to say to you. "Try to instill to them that the most important thing is them.…”

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

“…ICTs are uniquely attuned for rare diseases by using small samples, providing statistical rigor, typically costing far less than RCTs, yielding detailed analysis of outcomes heterogeneity, and offering the option to be overlaid onto usual care practices (Ridenour et al, 2016 ; Wittenborn et al, 2019 ). ICTs can quantify mechanisms of change during the administration of treatments , regardless of their length of time to administer (e.g., weeks to months of psychotherapy), and have used data from health records to thereby minimize burden on participants (Howe & Ridenour, 2019 ; Ridenour et al, 2016 ; Ridenour et al, 2017 ; Wittenborn et al, 2019 ). ICTs also can potentially incorporate patient preferences (Ridenour et al, 2013 ).…”

“…A mixed effects modeling framework provides a flexible, yet statistically powerful analytic approach for ICT data (Ferron et al, 2009 ; Ferron et al, 2010 ; Ridenour et al, 2013 ; Ridenour et al, 2017 ). Similar models are hierarchical linear models (HLM), multilevel models (MLM), mixed model trajectory analysis (MMTA), and latent growth curve models (LGM), which yield identical or equivalent special cases (i.e., estimates under one framework are identical or are simple transformations of estimates under another framework).…”

“…The intensive hierarchical model (Ferron et al, 2010 ; Ridenour et al, 2016 ; Ridenour et al, 2017 ) is a longitudinal mixed effects model specified as: where y ij is the outcome for patient i at time j . Here, Phase j = 0 during the baseline phase, and Phase j = 1 during the treatment phase.…”