Below are some example studies using the PersonAlytics technology. Click on the title in this list to jump to a specific study. You can then click on the title of any study to download the full PDF article.
- Potential Utility of Idiographic Clinical Trials in Drug Development
- The Clinical Trials Mosaic: Toward a Range of Clinical Trials Designs to Optimize Evidence-Based Treatment
- Biometrics and Policing: A Protocol for Multichannel Sensor Data Collection and Exploratory Analysis of Contextualized Psychophysiological Response During Law Enforcement Operations
- Internet-based incentives increase blood glucose testing with a non-adherent, diverse sample of teens with type 1 diabetes mellitus: a randomized controlled Trial
- Illustrating idiographic methods for translation research: moderation effects, natural clinical experiments, and complex treatment-by-subgroup interactions
- Toward Rigorous Idiographic Research in Prevention Science: Comparison Between Three Analytic Strategies for Testing Preventive Intervention in Very Small Samples
- Application of person-centered medicine in addiction
- Synthesizing single-case studies: A Monte Carlo examination of a three-level meta-analytic model
- Estimating individual treatment effects from multiple-baseline data: A Monte Carlo study of multilevel-modeling approaches
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Ty A. Ridenour, PhD & Donald Stull, PhD
Publication Date: March/April 2018
Introduction: An innovative methodology — idiographic clinical trials (ICTs) — is introduced as a way to inform randomized controlled trials (RCTs) in terms of RCT planning (eg, sample size, effect size), use in research scenarios when RCTs are not feasible (eg, rare diseases with small populations), or use in applied settings such as clinical practice, where RCT parameters cannot be followed. ICTs can be conducted generally for lower cost with faster completion time than RCTs. ICTs should not be seen as replacements for RCTs, but as a way to help inform RCTs or provide insights for early product development without allocating the resources for an RCT for early evaluation of an asset. The term idiographic clinical trials comes from its emphasis on within-individual processes over time. Compared to RCTs, this approach is adept for early phase clinical trials, pilot studies, and testing whether efficacy from an RCT can be replicated in a specific clinical setting or subpopulation (eg, patients with RCT exclusion criteria). ICTs couple two well-known methodologies to yield rigorous results from small samples: subject-as-own-control experimental designs (eg, crossover designs) with hierarchical linear modeling (or multi-level modeling) refined specifically for small samples.
Ty A. Ridenour & Szu-Han K. Chen & Hsin-Yi Liu & Georgiy V. Bobashev & Katherine Hill & Rory Cooper
Online Publication Date: 01 November 2017
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.
Robert D Furberg, PhD, MBA & Travis Taniguchi, PhD & Brian Aagaard, MA & Alexa M Ortiz, RN, MSN & Meghan Hegarty-Craver, PhD & Kristin H Gilchrist, PhD & Ty A Ridenour, PhD
Publication Date: March 2017
Background: Stress experienced by law enforcement officers is often extreme and is in many ways unique among professions. Although past research on officer stress is informative, it is limited, and most studies measure stress using self-report questionnaires or observational studies that have limited generalizability. We know of no research studies that have attempted to track direct physiological stress responses in high fidelity, especially within an operational police setting. The outcome of this project will have an impact on both practitioners and policing researchers. To do so, we will establish a capacity to obtain complex, multisensor data; process complex datasets; and establish the methods needed to conduct idiopathic clinical trials on behavioral interventions in similar contexts.
Objective: The objective of this pilot study is to demonstrate the practicality and utility of wrist-worn biometric sensor-based research in a law enforcement agency.
Methods: We will use nonprobability convenience-based sampling to recruit 2-3 participants from the police department in Durham, North Carolina, USA.
Results: Data collection was conducted in 2016. We will analyze data in early 2017 and disseminate our results via peer reviewed publications in late 2017.
Conclusions: We developed the Biometrics & Policing Demonstration project to provide a proof of concept on collecting biometric data in a law enforcement setting. This effort will enable us to (1) address the regulatory approvals needed to collect data, including human participant considerations, (2) demonstrate the ability to use biometric tracking technology in a policing setting, (3) link biometric data to law enforcement data, and (4) explore project results for law enforcement policy and training.
Bethany R. Raiff, PhD & Victoria B. Barrry, PsyD & Ty A. Ridenour, PhD & Natinee Jitnarin, PhD
Publication Date: June 2016
Abstract: Non-adherence with self-monitoring blood glucose (SMBG) among teenagers with type 1 diabetes can be a problem. The purpose of this study was to investigate the feasibility, acceptability, and preliminary efficacy of using Internet-based incentives to improve adherence with SMBG in non-adherent teenagers. Participants were randomly assigned to contingent (CS; N= 23), where they had to meet web camera-verified SMBG goals to earn incentives, or non-contingent (NS) groups (N=18),where they earned incentives independent of adherence. Brief motivational interviewing (MI) was given prior to the intervention. Attrition was 15% in the CS group. Participants and parents endorsed the intervention on all intervention dimensions. Daily SMBG increased after one MI session, and further increased when incentives were added, but significantly more for so for older participants. SMBG declined slowly over time, but only returned to baseline levels for younger NS participants. Internet-based incentive interventions are feasible, acceptable, and show promise for improving adherence with SMBG.
Ty A Ridenour, PhD & Andrea K Wittenborn, PhD & Bethany R Raiff, PhD & Neal Benedict, PhD & Sandra Kane-Gill
Publication Date: March 2016
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).
Ty A. Ridenour & Thomas Z. Pineo & Mildred M. Maldonado Molina & Kristen Hassmiller Lich
Online Publication Date: 10 January 2013
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 (N04) 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 Ptechnique 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.
Ralph E. Tarter, PhD & Levent Kirisci, PhD & Ty Ridenour, PhD & and Debra Bogen, MD
Publication Date: June 2012
Abstract: This article discusses human individuality within a lifespan developmental perspective. The practical application of an individual differences framework for diagnosis, prevention and treatment of addiction is described. A brief overview of the methods conducive to knowledge development within the rubric of person-centered medicine that are available to practitioners working in office and clinic settings concludes the discussion.
Corina M. Owens & John M. Ferron
Online Publication Date: 18 December 2011
Abstract: Numerous ways to meta-analyze single-case data have been proposed in the literature; however, consensus has not been reached on the most appropriate method. One method that has been proposed involves multilevel modeling. For this study, we used Monte Carlo methods to examine the appropriateness of Van den Noortgate and Onghena’s (2008) raw-data multilevel modeling approach for the meta-analysis of single-case data. Specifically, we examined the fixed effects (e.g., the overall average treatment effect) and the variance components (e.g., the between-person within-study variance in the treatment effect) in a three-level multilevel model (repeated observations nested within individuals, nested within studies). More specifically, bias of the point estimates, confidence interval coverage rates, and interval widths were examined as a function of the number of primary studies per meta-analysis, the modal number of participants per primary study, the modal series length per primary study, the level of autocorrelation, and the variances of the error terms. The degree to which the findings of this study are supportive of using Van den Noortgate and Onghena’s (2008) raw-data multilevel modeling approach to meta-analyzing single-case data depends on the particular parameter of interest. Estimates of the average treatment effect tended to be unbiased and produced confidence intervals that tended to overcover, but did come close to the nominal level as Level-3 sample size increased. Conversely, estimates of the variance in the treatment effect tended to be biased, and the confidence intervals for those estimates were inaccurate.
John M. Ferron & Jennie L. Farmer & Corina M. Owens
Publication Date: November 2010
Abstract: While conducting intervention research, researchers and practitioners are often interested in how the intervention functions not only at the group level, but also at the individual level. One way to examine individual treatment effects is through multiple-baseline studies analyzed with multilevel modeling. This analysis allows for the construction of confidence intervals, which are strongly recommended in the reporting guidelines of the American Psychological Association. The purpose of this study was to examine the accuracy of confidence intervals of individual treatment effects obtained from multilevel modeling of multiple-baseline data. Monte Carlo methods were used to examine performance across conditions varying in the number of participants, the number of observations per participant, and the dependency of errors. The accuracy of the confidence intervals depended on the method used, with the greatest accuracy being obtained when multilevel modeling was coupled with the Kenward—Roger method of estimating degrees of freedom.
Ty A. Ridenour & Deanne L. Hall & James E. Bost
Online Publication Date: 01 July 2009
Background/Objectives: To date, research on substance abuse prevention relied extensively on large sample randomized clinical trials to evaluate intervention programs. These designs are appropriate for certain types of randomized prevention trials (e.g., efficacy or effectiveness for broad populations) but are unfeasible for other prevention science scenarios (e.g., rare pathologies, pilot studies, or replication tests at specific locales).
Methods: An alternative randomized clinical trial is described that relies on much smaller samples, less resources than the large sample designs, randomization, N-of-1 designs for the intervention group, and mixed model analysis.
Results: This methodology is illustrated using a small sample prevention study, which demonstrates its statistical power, flexibility, and sophistication for experimental testing of prevention-oriented research questions. Scientific
Significance: This methodology can be applied to many existing prevention datasets to facilitate secondary analyses of existing datasets as well as novel studies. It is hoped that such efforts will include further development of the small sample design in substance abuse prevention contexts.