Toward Rigorous Idiographic Research in Prevention Science: Comparison Between Three Analytic Strategies for Testing Preventive Intervention in Very Small Samples

Abstract

Psychosocial prevention research lacks evidence from intensive within-person lines of research to understand idiographic processes related to development and response to intervention. Such data could be used to fill gaps in the literature and expand the study design options for prevention researchers, including lower-cost yet rigorous studies (e.g., for program evaluations), pilot studies, designs to test programs for low prevalence outcomes, selective/indicated/ adaptive intervention research, and understanding of differential response to programs. This study compared three competing analytic strategies designed for this type of research: autoregressive moving average, mixed model trajectory analysis, and P-technique. Illustrative time series data were from a pilot study of an intervention for nursing home residents with diabetes (N=4) designed to improve control of blood glucose. A within-person, intermittent baseline design was used. Intervention effects were detected using each strategy for the aggregated sample and for individual patients. The P-technique model most closely replicated observed glucose levels. ARIMA and P-technique models were most similar in terms of estimated intervention effects and modeled glucose levels. However, ARIMA and P-technique also were more sensitive to missing data, outliers and number of observations. Statistical testing suggested that results generalize both to other persons as well as to idiographic, longitudinal processes. This study demonstrated the potential contributions of idiographic research in prevention science as well as the need for simulation studies to delineate the research circumstances when each analytic approach is optimal for deriving the correct parameter estimates.

Internet-based incentives increase blood glucose testing with a non-adherent, diverse sample of teens with type 1 diabetes mellitus: a randomized controlled Trial

“…Significant differences were based on α set to p < 0.05. Mixed model trajectory analysis (MMTA) [26][27][28] was used (in SAS 9.3) to test changes in daily SMBG associated with each intervention component with regard to mean change and slope over time. Additional planned analyses were conducted to determine whether age (13-15 vs 16-18) served as a moderator to treatment effects, based on previous research, suggesting that age is related to metabolic control [20,21]; thus, age was tested as a dichotomized variable (0 = younger, 1 = older).…”

“…Missing data were analyzed as missing and were not significantly different between groups when excluding non-responders (CS = 5 %, NS = 5.7 %, overall = 5.3 %). The Kenward-Roger adjustment [27] was used in all MMTA.…”

Personalizing Behavioral Interventions Through Single‐Patient (N‐of‐1) Trials

“…Although N-of-1 RCTs apply scientific rigor to an approach that most closely mimics clinical practice, remarkably little progress has been made in the development of the N-of-1 RCT method and its related statistical approaches during the last three decades (Barlow & Nock, 2009; Hamaker, 2012; Molenaar & Campbell, 2009; Ridenour, Pineo, Maldonado Molina, & Hassmiller Lich, 2013). In the health care industry, this is purported to be due to the research and development model of pharmaceutical companies, reimbursement practices, and governmental regulatory procedures (Aspinall & Hamermesh, 2007).…”

From ideas to efficacy: The ORBIT model for developing behavioral treatments for chronic diseases.

“…Observational techniques such as behavioral event modeling (Wansink, 2010) and use of ecological momentary assessment (Shiffman, Stone, & Hufford, 2008) permit the study of treatment effects in naturalistic settings, leading not only to the identification of intervention targets but also of critical time points for intervention delivery. Time series and ABA designs, involving multiple baseline and treatment observations in small numbers of individuals (Ridenour, Pineo, Molina, & Lich, 2013), allow researchers to identify early indications of potential benefit. New analytic techniques for use with small samples, including Bayesian analyses of data from N-of-1 trials, may be useful in Phase Ia studies (Zucker, Ruthazer, & Schmid, 2010).…”