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.

Fig. 1

Timeline of data collection. Note: s = baseline phase using sliding scale. M = intervention phase using manual pancreas

Fig. 2

Exemplar patient time series data for two patients. Panel a: Time series of blood glucose levels at 11:30am for Patient C. Panel b: Time series of blood glucose levels at 8:30pm for Patient D with the trend from results of mixed model trajectory analysis of intervention impact appearing in bold

Fig. 3

P-technique factor model for daily glucose level with Lag 1

Fig. 4

Panel a Closed Loop diagram of a healthy blood sugar metabolism system. Panel b closed loop diagramof common clinical strategies to manage type I diabetes. Panel c model boundaries chart for panel b diagram. Panel A presents a causal loop diagram illustrating the putative causal sequences that keep the blood sugar metabolism system balanced, heavily inspired by a similar diagram developed by Gaynor (1998). An arrow connecting two variables indicates that a change in the first causes a change in the second, all other things being equal. A “+” on the arrowhead indicates the variables move in the same direction, while a “−” indicates they move in opposite directions. Loops are formed when causal sequences circle back on themselves; balancing loops move a system into equilibrium. Panel B presents an expanded causal loop diagram of common clinical strategies to manage type 1 diabetes. Dashed lines suggest potential interventions