Dynamic p-technique for modeling patterns of data: applications to pediatric psychology research

Grouping of mood symptoms by time series dynamics

BACKGROUND

Understanding how symptoms of mood disorders vary over time in relation to each other is potentially valuable for diagnosis and predicting episodes of illness. In this paper, we characterize the degree of similarity of time series of different mood disorder symptoms.

METHODS

We collected 32,215 mood disorder symptom questionnaires, administered twice-daily over 18 months to (n = 19) subjects with rapidly cycling bipolar disorder and (n = 20) healthy control subjects, using visual analog scales to rate 11 sets of symptom severity ratings plus a control item. We used Dynamic Time Warping to calculate similarity ratings between all within-subject pairs of severity ratings followed by Exploratory Factor Analysis (EFA) to identify latent factors of symptom time series across all subjects.

RESULTS

Two latent factors were identified: one with depression and anxiety; and a second, with concentration, energy, irritability, fatigue, appetite, euphoria/elation and overall mood. Restlessness, racing thoughts, and the control item (daily hours of daylight) did not cluster with any of the others.

LIMITATIONS

Limited sample size dictated that we pool bipolar and healthy patients and use an iterative EFA procedure.

CONCLUSION

This analysis suggests that, in a pooled sample of individuals with bipolar disorder and in healthy controls, severity ratings of overall depression and overall anxiety vary jointly as one dynamic factor, while some but not all other DSM mood symptoms vary jointly along with overall mood rating as a second dynamic factor. Further investigation may determine if these findings can simplify subjective symptom reporting in mood-monitoring studies.

New directions for the study of within-individual variability in development: the power of "N = 1"

This article provides an introduction to the idiographic approach ("N = 1" research) in developmental psychology and an overview of methodological and statistical techniques employed to address the study of within-individual variability in development. Through a popularization of the idiographic approach and associated statistical techniques, but also through technical advances in the apparatus used to produce single-case intensive longitudinal data, the "power" of "N = 1" is becoming increasingly tangible and may challenge, or supplement, established paradigms in nomothetic (group-level) developmental psychology.

Aggregated N-of-1 randomized controlled trials: modern data analytics applied to a clinically valid method of intervention effectiveness

OBJECTIVE

Aggregated N-of-1 randomized controlled trials (RCTs) combined with multilevel modeling represent a methodological advancement that may help bridge science and practice in pediatric psychology. The purpose of this article is to offer a primer for pediatric psychologists interested in conducting aggregated N-of-1 RCTs.

METHODS

An overview of N-of-1 RCT methodology is provided and 2 simulated data sets are analyzed to demonstrate the clinical and research potential of the methodology.

RESULTS

The simulated data example demonstrates the utility of aggregated N-of-1 RCTs for understanding the clinical impact of an intervention for a given individual and the modeling of covariates to explain why an intervention worked for one patient and not another.

CONCLUSIONS

Aggregated N-of-1 RCTs hold potential for improving the science and practice of pediatric psychology.

Translational bioinformatics in psychoneuroimmunology: methods and applications

Translational bioinformatics plays an indispensable role in transforming psychoneuroimmunology (PNI) into personalized medicine. It provides a powerful method to bridge the gaps between various knowledge domains in PNI and systems biology. Translational bioinformatics methods at various systems levels can facilitate pattern recognition, and expedite and validate the discovery of systemic biomarkers to allow their incorporation into clinical trials and outcome assessments. Analysis of the correlations between genotypes and phenotypes including the behavioral-based profiles will contribute to the transition from the disease-based medicine to human-centered medicine. Translational bioinformatics would also enable the establishment of predictive models for patient responses to diseases, vaccines, and drugs. In PNI research, the development of systems biology models such as those of the neurons would play a critical role. Methods based on data integration, data mining, and knowledge representation are essential elements in building health information systems such as electronic health records and computerized decision support systems. Data integration of genes, pathophysiology, and behaviors are needed for a broad range of PNI studies. Knowledge discovery approaches such as network-based systems biology methods are valuable in studying the cross-talks among pathways in various brain regions involved in disorders such as Alzheimer's disease.