Bayesian Analysis for Multiple-baseline Studies Where the Variance Differs across Cases in OpenBUGS.

Objective: There is a growing interest in the potential benefits of applying Bayesian estimation for multilevel models of SCED data. Methodological studies have shown that Bayesian estimation resolves convergence issues, can be adequate for the small sample, and can improve the accuracy of the variance components. Despite the potential benefits, the lack of accessibility to software codes makes it difficult for applied researchers to implement Bayesian estimation in their studies. The purpose of this article is to illustrate a feasible way to implement Bayesian estimation using OpenBUGS software to analyze a complex SCED model where within-participants variability and autocorrelation may differ across cases. Method: By using extracted data from a published study, step-by-step guidance in analyzing the data using OpenBUGS software is provided, including (1) model specification, (2) prior distributions, (3) data entering, (4) model estimation, (5) convergence criteria, and (6) posterior inferences and interpretations. Result: Full codes for the analysis are provided.

Single-case experimental designs for behavioral neuroscience.

Single-case experimental designs (SCEDs) are commonly used in behavior analytic research but rarely used in behavioral neuroscience research. The recent development of technologies that allow control of the timing of neurobiological events such as gene expression and neuronal firing enable the fruitful application of SCEDs for the study of brain-behavior relations. There are at least 3 benefits expected from applying SCEDs to study how neurobiological events affect behavior. First, SCEDs entail direct within- and across-subject assessments of reliability, likely increasing the probability of replication across studies and encouraging a search for the causes of replication failure when they occur. Second, SCEDs focus on behavior in individual organisms producing a body of knowledge that applies to individuals rather than population parameters. Finally, SCEDs require fewer animals, decreasing costs and effort and addressing the ethical obligation to reduce the number of animals used for research. Examples are provided using hypothetical data generated based on published research. Collaborations between behavior analysts and behavioral neuroscientists will bring the world within the skin under direct experimental control and broaden our understanding of the determinants of behavior.

Power of a randomization test in a single case multiple baseline AB design.

A randomization test can be used to statistically test hypotheses in multiple baseline designs to complement the commonly used visual inspection analysis. A crossed factor simulation study was performed to investigate the power of a randomization test in an multiple baseline design. The results show that the degree of autocorrelation of the observations, the number of participants, the effect size, the overlap of possible start moments of the intervention between participants, the ratio of the number of measurements in the baseline- and intervention phase, a gradually emerging effect, and the number of measurements had strong main effects on the power. The two-way interactions between number of participants and effect size, and between the number of measurements and the number of start moments of the intervention also had a large effect. An online tool was developed to calculate the power of a multiple baseline design given several design characteristics.

Assessing Consistency in Single-Case A-B-A-B Phase Designs.

Previous research has introduced several effect size measures (ESMs) to quantify data aspects of single-case experimental designs (SCEDs): level, trend, variability, overlap, and immediacy. In the current article, we extend the existing literature by introducing two methods for quantifying consistency in single-case A-B-A-B phase designs. The first method assesses the consistency of data patterns across phases implementing the same condition, called CONsistency of DAta Patterns (CONDAP). The second measure assesses the consistency of the five other data aspects when changing from baseline to experimental phase, called CONsistency of the EFFects (CONEFF). We illustrate the calculation of both measures for four A-B-A-B phase designs from published literature and demonstrate how CONDAP and CONEFF can supplement visual analysis of SCED data. Finally, we discuss directions for future research.

How Qualitative Case Study Methodology Informs Occupational Therapy Practice: A Scoping Review.

Qualitative case study methodology (QCSM) is a useful research approach that has grown in popularity within the social sciences; however, it has received less attention in the occupational therapy literature. The current scoping review aims to explore how studies utilizing a QCSM help inform occupational therapy knowledge and practice. Electronic searches were conducted in April 2017, and the resultant 388 publications were screened by three reviewers in Covidence. In all, 27 publications met the inclusion criteria and were included in the scoping review. Case studies in the occupational therapy literature have explored phenomena relating to the delivery of intervention, theoretical concepts, clinical reasoning, and education and research methods and were situated in a range of different practice areas and contexts. QCSM is a valuable approach in occupational therapy research that contributes to the body of knowledge and theory that informs occupational therapy practice.

Single-case Design Studies in Children with Cerebral Palsy: A Scoping Review.

Aim: To critically evaluate single-case design (SCD) studies performed within the population of children/adolescents with cerebral palsy (CP).Methods: A scoping review of SCD studies of children/adolescents with CP. Demographic, methodological, and statistical data were extracted. Articles were evaluated using the Risk of Bias in N-of-1 Trials (RoBiNT) Scale and the Consolidated Standards of Reporting Trials (CONSORT) extension for N-of-1 trials (CENT 2015). Comments regarding strengths and limitations were analyzed.Results: Studies investigated the effects of a wide range of interventions on various outcomes. Most SCD types were adopted in multiple studies. All studies used visual inspection rather than visual analysis, often complemented with basic statistical descriptives. Risk of bias was high, particularly concerning internal validity. Many CENT items were insufficiently reported. Several benefits and limitations of SCD were identified.Conclusions: The quality of evidence from results of SCD studies needs to be increased through risk of bias reduction.

Randomization procedures in single-case intervention research contexts: (Some of) "the rest of the story".

Following up on articles recently published in this journal, the present contribution tells (some of) "the rest of the story" about the value of randomization in single-case intervention research investigations. Invoking principles of internal, statistical-conclusion, and external validity, we begin by emphasizing the critical distinction between design randomization and analysis randomization, along with the necessary correspondence between the two. Four different types of single-case design-and-analysis randomization are then discussed. The persistent negative influence of serially dependent single-case outcome observations is highlighted, accompanied by examples of inappropriate applications of parametric and nonparametric tests that have appeared in the literature. We conclude by presenting valid applications of single-case randomization procedures in various single-case intervention contexts, with specific reference to a freely available Excel-based software package that can be accessed to incorporate the present randomization schemes into a wide variety of single-case intervention designs and analyses.

Applying mixed-effects modeling to single-subject designs: An introduction.

Behavior analysis and statistical inference have shared a conflicted relationship for over fifty years. However, a significant portion of this conflict is directed toward statistical tests (e.g., t-tests, ANOVA) that aggregate group and/or temporal variability into means and standard deviations and as a result remove much of the data important to behavior analysts. Mixed-effects modeling, a more recently developed statistical test, addresses many of the limitations of more basic tests by incorporating random effects. Random effects quantify individual subject variability without eliminating it from the model, hence producing a model that can predict both group and individual behavior. We present the results of a generalized linear mixed-effects model applied to single-subject data taken from Ackerlund Brandt, Dozier, Juanico, Laudont, & Mick, 2015, in which children chose from one of three reinforcers for completing a task. Results of the mixed-effects modeling are consistent with visual analyses and importantly provide a statistical framework to predict individual behavior without requiring aggregation. We conclude by discussing the implications of these results and provide recommendations for further integration of mixed-effects models in the analyses of single-subject designs.

Single-case experimental designs. Evaluating interventions in research and clinical practice.

Single-case designs refer to a methodological approach that can be used to investigate the effectiveness of treatment with the individual client. The designs permit scientifically valid inferences to be drawn about the effects of treatment and hence offer advantages over alternative strategies such as the uncontrolled case study or open study that are used with the individual case. The present article discusses the key features of the methodology, illustrates specific designs and how inferences are drawn, and discusses critical issues (feasibility, generality of results, ethical issues) in the use of the designs. Essential features of the design, including ongoing assessment and drawing on the underlying thinking and logic of the designs can improve the clinical care for individual clients, even when the rigors of experimentation are not feasible or desirable. Lamentably, single-case methods are rarely trained among researchers or practitioners in psychology or related mental health professions. The designs could play a special role by improving individual care and therapeutic change, apart from the strength of the methodology as a purely research tool.

The role of the individual in the coming era of process-based therapy.

For decades the development of evidence-based therapy has been based on experimental tests of protocols designed to impact psychiatric syndromes. As this paradigm weakens, a more process-based therapy approach is rising in its place, focused on how to best target and change core biopsychosocial processes in specific situations for given goals with given clients. This is an inherently more idiographic question than has normally been at issue in evidence-based therapy over the last few decades. In this article we explore methods of assessment and analysis that can integrate idiographic and nomothetic approaches in a process-based era.

Real-time monitoring technology in single-case experimental design research: Opportunities and challenges.

Single-case experimental design (SCED) is a rigorous method of studying behavior and behavior change. A key characteristic of SCED is repeated, systematic assessment of outcome variables, which is critical to achieving high internal validity, collecting a sufficient number of observations to conduct adequately powered statistical analyses, capturing dynamic and fine-grained changes in outcomes, and tailoring interventions at the individual level. Recent advances in real-time monitoring technology, such as digital ecological momentary assessment, passive smartphone-based behavioral tracking, and physiological assessment with wearable biosensors, are extremely well-suited to conducting these repeated, systematic measurements. Here, we discuss the rationale for incorporating real-time data collection technologies within SCED and highlight how recent studies have paired SCED with real-time monitoring. We also present original data illustrating how real-time digital monitoring can provide an idiographic and granular view of behavior (in this case, suicidal ideation). Last, we discuss the challenges of, and offer our recommendations for, using real-time monitoring technologies in SCED research.