Illustrating idiographic methods for translation research: moderation effects, natural clinical experiments, and complex treatment-by-subgroup interactions

Abstract

A critical juncture in translation research involves the preliminary studies of intervention tools, provider training programs, policies, and other mechanisms used to leverage knowledge garnered at one translation stage into another stage. Potentially useful for such studies are rigorous techniques for conducting within-subject clinical trials, which have advanced incrementally over the last decade. However, these methods have largely not been utilized within prevention or translation contexts. The purpose of this manuscript is to demonstrate the flexibility, wide applicability, and rigor of idiographic clinical trials for preliminary testing of intervention mechanisms. Specifically demonstrated are novel uses of state-space modeling for testing intervention mechanisms of short-term outcomes, identifying heterogeneity in and moderation of within-person treatment mechanisms, a horizontal line plot to refine sampling design during the course of a clinic-based experimental study, and the need to test a treatment's efficacy as treatment is administered along with (e.g., traditional 12-month outcomes).

Randomized controlled trial of emotionally focused couple therapy compared to treatment as usual for depression: Outcomes and mechanisms of change

“…Conceptually, USEM is a form of state-space modeling having some overlap with structural equation modeling (Chow, Ho, Hamaker, & Dolan, 2010). USEM is a useful method for examining mechanisms of treatment using many assessments over time (Ridenour et al, 2016). It tests treatment mechanisms by comparing how associations among mechanisms and outcomes differ over time, and can be used in studies with small sample sizes.…”

“…For this study, USEM was used as a novel approach to model session-to-session changes within persons, while accounting for autocorrelation, or correlation among a person's assessments over time. Few studies to date have used USEM within clinical trial designs (Kim, Zhu, Chang, Bentler, & Ernst, 2007;Gates, Molenaar, Hillary, & Slobounov, 2011;Molenaar & Nesselroade, 2009;Ram, Brose, & Molenaar, 2013;Ridenour et al, 2016;Zheng, Wiebe, Cleveland, Molenaar, & Harris, 2013). This study used a special case of USEM based on Granger causality conditions (Granger, 1980;Zheng et al, 2013) to test the sequencing of changes in relationship satisfaction leading to changes in depression or vice versa within an individual (Figure 2).…”

A Case Study Examining Fixed Versus Randomized Criteria for Treating a Child With Conduct Problems and Callous-Unemotional Traits

“…First, as apparent from the figures, there was considerable heterogeneity across the course of treatment for all outcomes. Statistically sophisticated yet easy to use methods for understanding this heterogeneity are sorely needed, and there are promising recent developments toward this goal (Ridenour, Pineo, Maldonado Molina, & Hassmiller Lich, 2013; Ridenour, Wittenborn, Raiff, Benedict, & Kane-Gill, 2016). Second, it would be interesting to extend the methods used in this study to examine whether children with CPCU differentially respond to fixed versus random punishment.…”

Biometrics and Policing: A Protocol for Multichannel Sensor Data Collection and Exploratory Analysis of Contextualized Psychophysiological Response During Law Enforcement Operations

“…Mixed-model trajectory analysis (MMTA), derived from hierarchical linear modeling, will quantify each individual officer’s time series data at level 1, while aggregates of officer data across individuals are analyzed at level 2 [45-48]. Hence, observations are clustered within individuals.…”

“…Maximum likelihood estimation and fit statistics test which model components provide (or do not provide) improved fit to the observed data. Compared with traditional hierarchical linear modeling, several adjustments are needed to counter the potential for biased estimates in small sample sizes [45-48]. First, during the model fitting process, the Kenward-Roger correction is used to reduce the probability of a type 1 error [49-51].…”