A. James O'Malley, PhD

Professor of Biostatistics

A. James O’Malley, PhD





  • Ph.D., in Statistics, University of Canterbury, 1999
  • M.S., Applied Statistics, Purdue University, 1999
  • B.Sc.(Hons), in Statistics, University of Canterbury, 1994

Curriculum Vitae

A. James O'Malley CV

Areas of Expertise

My methodological research interests have centered on the design and analysis of medical device clinical trials, multivariate-hierarchical modeling, causal inference and social network analysis. I have developed novel statistical methods, often involving novel use of Bayesian statistics, to solve important methodological and applied problems in health policy and health services research, including the evaluation of treatments and quality of care in multiple areas of medicine. I am continuing to look at problems from multiple lenses including statistical, health policy, medical, epidemiological, and sociological perspectives. I expect to continue working on methodological problems in causal inference (comparative effectiveness research), hierarchical-multivariate modeling, social network analysis, and Bayesian analysis with specific problems often at the intersection of two or more of these areas.


1992 Page Memorial Prize for Mathematics, University of Canterbury
1993 University of Canterbury Senior Scholarship, University of Canterbury
1993 Cook Memorial Prize for Mathematics, University of Canterbury
1994 University of Canterbury Doctoral Scholarship
1996 Second Place, Students' Paper Competition, New Zealand Statistical Association
1997 Charles Cook, Warwick House Memorial Scholarship, University of Canterbury
1999 L.J. Cote Award for Excellence in Statistics, Purdue University
2002 Young investigator travel award, 4th Scientific Forum on Quality of Care and
Outcomes Research in Cardiovascular Disease and Stroke
2011 Mid-Career Excellence Award, American Statistical Association Health Policy
Statistics Section
2012 Elected Fellow, American Statistical Association for “novel use of Bayesian statistics, multivariate-hierarchical modeling, causal inference and social network analysis to solve problems in health policy and health services research, for improving evaluation of treatments and quality of health care, and for leadership in health policy statistics.”

Professional Achievements:

Published Chapter: O’Malley AJ, Neelon BH. "Using structural equation, latent factor and latent class models to accommodate heterogeneity." In: Anthony J. Culyer (ed.), Encyclopedia of Health Economics, Vol 2. San Diego: Elsevier; 2014. pp. 131-140.


Culture Change and Nursing Home Quality of Care
The Gerontologist, February 2014;54 (Suppl 1): S35-S45. doi: 10.1093/geront/gnt143
Application of models for multivariate mixed outcomes to medical device trails: coronary artery stenting
Statistics in Medicine, Dec. 2002;22(2): 313-36
Likelihood methods for treatment noncompliance and subsequent nonresponse in randomized trials.
Biometrics, June 2005;61(2): 325-34
Exploratory Factor Analyses of the CAHPS® Hospital Pilot Survey Responses across and within Medical, Surgical, and Obstetric Services
Health Services Research, Dec. 2005;40(6 Pt 2)2078–2095: 2078–95
Variance-Covariance Functions for Domain Means of Ordinal Survey Items
Survey Methodology,, December 2005;31(2): 169-182
The Analysis of Social Networks
Health Services and Outcomes Research Methodology, Dec. 2008;Scholarships (2) 8(4): 222–269
Domain-Level Covariance Analysis for Multilevel Survey Data With Structured Nonresponse
Journal of the American Statistical Association, 2008;103(484): 1405-18
Use Of Telemedicine Can Reduce Hospitalizations Of Nursing Home Residents And Generate Savings For Medicare
Health Affairs, February 2014;33(2): 244-50
A Bayesian model for repeated measures zero-inflated count data with application to outpatient psychiatric service use.
Stat Modeling, December 2010;10(4): 421-439
A Bayesian Two-Part Latent Class Model for Longitudinal Medical Expenditure Data: Assessing the Impact of Mental Health and Substance Abuse Parity
Biometrics, March 2011;67(1): 280-9

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