Biography

Joshua Wiley is a health researcher and senior analyst at Elkhart Group Ltd. Trained as a health psychologist, his research focuses on understanding the complex interplays of psychological, social, and physiological processes as they pertain to psychological and physical health. His passion to integrate across mind and body has lead to involvement in a number of interdisciplinary collaborations with published articles in psychological, psychiatric, and medical journals. Melding his statistical and research background, Joshua is particularly interested in research projects that move beyond testing simple associations to testing more comprehensive models of health as well as research that investigates health trajectories and their predictors.

As an analyst and statistical consultant, Joshua focuses on biostatistics, and is interested in reproducible research and graphical displays of data and statistical models. Through his work at Elkhart Group Ltd. and former work at UCLA Statistical Consulting Group, he has supported a wide array of clients ranging from graduate students, to experienced researchers, and biotechnology companies.

Curriculum Vitae

Research

Psychosocial Resources & Coping

I am fascinated by the individual differences in how people react to and cope with stressful events. This has led me to explore the roles of psychosocial resources: psychological and social factors that are innately valuable or that individuals can draw on to promote good mental or physical health. Specific resources I have studied include social support, self-efficacy, and positive outcome expectancies. Given the degree of measurement overlap of many resources, another line of my research has been examining measurement and factor analytic models of constellations of psychosocial resources. In four samples analyzed so far, a two-factor model with a latent psychological and social resource factor has fit well.

Coping strategies people employ in response to stressors also play a role in how well they adjust. Coping is particularly important in the face of major life stressors such as cancer or chronic disease. In collaboration with the UCLA Stanton Stress and Coping lab, I have studied the effects of coping in response to genetic testing, breast cancer diagnosis, and an experimental study of receipt of negative health information.

Biomarkers

To understand how psychological and physiological processes interact, I have worked on studies measuring biomarkers such as salivary cortisol and alpha-amylase to assess HPA axis and sympathetic nervous system reactivity to stress, and whether psychosocial resources predict physiological baseline or reactivity to acute stress. I have also collaborated with researchers at the University of Melbourne and Monash University to study the roles of cognitive vulnerabilities in both self- and actigraphy-assessed sleep.

As the number of biomarkers available has grown, researchers are increasingly faced with how to choose which to collect or if many are collected how to model them. Given my work exploring measurement models of psychosocial resources, I am interested in applying similar approaches to biomarkers. I am particularly interested in practical analytical models researchers can use not only to reduce the number of dimensions of biomarkers for analysis, but also to find theoretically meaningful dimensions. Related questions include how best to utilize repeated measures of biomarkers, issues of non-normality or non-linearity, and adjusting for potential biasing effects of medications and other measurement error.

Other

As part of larger teams, I have also engaged in some smaller lines of research. In the area of health services, I have engaged in research probing the frequency and cost of providing futile care in critical care units. We also investigated predictors of providing care assessed as futile: what characteristics of patients or providers increases the probability that a patient will receive futile care? A separate line of work has involved analysis of behavioral and EEG patterns that are signatures of ADHD, and research that attempts to move beyond looking at ADHD differences on a single measure and examine ADHD effects on whole networks.

Publications Google Scholar

Publications

  1. Ortiz Parada, M., Wiley, J. F., & Chiang, J. J. (In Press). How stress gets under the skin o Como el estr├ęs psicol├│gico se mete bajo tu piel. Revista Medica de Chile.
  2. Wiley, J. F., & Stanton, A. L. (in press). Cancer and mental Health Encyclopedia of Mental Health.
  3. Hale, T. S., Kane, A. M., Kaminsky, O., Tung, K. L., Wiley, J. F., McGough, J. J., Loo, S. K., & Kaplan, J. T. (2013). Visual Network Asymmetry and Default Mode Network Function in ADHD: An fMRI Study. Frontiers in Psychiatry, 5(81). doi: 10.3389/fpsyt.2014.00081.
  4. Wiley, J. F. (2014). Growth Curve Analysis and Visualization Using R. Journal of Statistical Software, Book Reviews, 58(2). URL: http://www.jstatsoft.org/v58/b02
  5. Brown, L. A., Wiley, J. F., Wolitzky-Taylor, K., Roy-Byrne, P., Sherbourne, C., Stein, M. B., . . . Craske, M. G. (In Press). Changes in self-efficacy and outcome expectancy as predictors of anxiety outcomes from the CALM study. Depression and Anxiety. doi: 10.1002/da.22256
  6. Dour, H. J., Wiley, J. F., Roy-Byrne, P., Stein, M. B., Sullivan, G., Sherbourne, C. D., . . . Craske, M. G. (2014). Perceived social support mediates anxiety and depressive symptom changes following primary care intervention. Depression and Anxiety, 31(5), 436-442. doi: 10.1002/da.22216
  7. Huynh, T. N., Kleerup, E. C., Wiley, J. F., Savitsky, T. D., Guse, D., Garber, B. J., & Wenger, N. S. (2013). The frequency and cost of treatment perceived to be futile in critical care. JAMA Intern Med, 173(20), 1887-1894. doi: 10.1001/jamainternmed.2013.10261
  8. Langley, A. K., Falk, A., Peris, T., Wiley, J. F., Kendall, P. C., Ginsburg, G., . . . Piacentini, J. (2013). The Child Anxiety Impact Scale: Examining Parent- and Child-Reported Impairment in Child Anxiety Disorders. Journal of Clinical Child & Adolescent Psychology, 1-13. doi: 10.1080/15374416.2013.817311
  9. Wiley, J. F., Laird, K., Beran, T., McCannel, T. A., & Stanton, A. L. (2013). Quality of life and cancer-related needs in patients with choroidal melanoma. British Journal of Ophthalmology, 97(11), 1471 - 1474. doi: 10.1136/bjophthalmol-2013-303635
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Statistics & Analytics

Measurement Models

Deriving unbiased, reliable measures is a common problem in psychology where core constructs such as depression and personality are unobservable. I integrate psychometrics and item response theory into my work to create latent variables of focal constructs and to explore their accuracy and reliability of these measures. I have used confirmatory factor models to understand the latent factor structure underlying psychosocial resources (Wiley, ..., Stanton, under review) and bi-factor models to separate variability associated with anxiety from multiple reporters (parent and child) in categorical response data. Although most of work employs maximum likelihood methods, I am also interested in Bayesian methods. In one study, I used Bayesian factor analysis to extract multiple draws of factor scores from the posterior distribution of the latent variable to meta-analyze several samples where the latent construct was assumed similar, but the specific indicators varied (Wiley, ..., Stanton, under review). I am also exploring the use of Bayesian factor analysis with strong priors to allow small covariances among residuals. I use a variety of analytical software including the commercial Mplus as well as open source variants such as OpenMx and lavaan. For Bayesian models, I have used both Mplus and coded my own factor models in Stan. I have also written a small number of utility functions related to measurement models such as getting maximum likelihood estimates of descriptive statistics using all available data in the R package semutils available on github, and I am a co-developer of the R package MplusAutomation to link R and Mplus, also available on github. MplusAutomation has been cited by over 15 publications (see list here and is in the top 15% of most downloaded R packages.

Mixed Effects Models

Another focus of my statistics work is on mixed and random effects models (e.g., patients sampled within physicians; longitudinal observational studies). One project I worked on involved observations crossed between patients (repeated measures) and physicians and the outcome was ordinal (Huynh, Kleerup, Wiley, ..., Wenger, 2013). I analyzed the data in a Bayesian framework and presented the average marginal change in the probability of falling into each level of the outcome as an intuitive way for clinicians to understand the results and effect of each variable. To automate generating average marginal predictions following an ordinal, cross-classified Bayesian mixed effects model, I wrote the R package, postMCMCglmm, available on github and presented a poster on the work at Modern Modeling Methods 2013 meeting.

Missing Data

I have expertise using multiple imputation and maximum likelihood approaches to more efficiently use data in the case they are missing completely at random (MCAR) or at least missing at random (MAR). However, in longitudinal studies, attrition is ubiquitous and assuming this is random is often untenable. I have done work using joint models or so-called shared parameter models where the random intercept and slope parameters from a growth model are used as predictors of the probability of still being in the study (Stanton, Wiley, ..., in preparation). I have also used joint models in work with clients where the outcome was one of the decision criteria for whether participants could remain in the study (i.e., non random as participants who worsened too quickly were triaged out of the study).

Current and Future Directions

Areas of current interest include models and tests for multilevel mediation. In my work and consultation, I have used a non-parametric two-stage re-sampling bootstrap approach as well as a fully Bayesian model derive confidence intervals around the indirect effect. I have conducted some small simulations examining the properties of two-stage re-sampling for multilevel models and code to make use of multiple processors or clusters to speed non parametric bootstrapping of multilevel models in R. Easy to use, fast, tools for applied research using multilevel mediation models is an active area of interest. I am also interested in techniques to estimate the distribution of indirect effects when one of the pathways is a random effect and in how this information can be presented to be useful to researchers, rather than marginalizing out individual differences.

I am currently working on methods for modeling, visualizing, and utilizing variability in repeated measures data.

Contact

To get in touch with me about my research or a research collaboration please contact me here. If you have questions about statistical analysis or would like to hire me as a statistical analyst or consultant on your project, please go to Elkhart Group Ltd.

Copyright © 2009 - 2014 by Joshua Wiley. All rights reserved. Brought to you by Elkhart Group Limited.