- Home
- People
- Chief Investigators
- Louise Ryan
Professor Louise Ryan
University of Technology Sydney
After completing her undergraduate degree in statistics and mathematics at Macquarie University, Louise Ryan left Australia in 1979 to pursue her PhD in Statistics at Harvard University in the United States. In 1983, Louise then took up a postdoctoral fellowship in Biostatistics, jointly between Dana-Farber Cancer Institute and the Harvard School of Public Health. She was promoted to Assistant Professor in 1985, eventually becoming the Henry Pickering Walcott Professor and Chair of the Department of Biostatistics at Harvard. Louise returned to Australia in early 2009 to take up the role as Chief of CSIRO’s Division of Mathematics, Informatics and Statistics. In 2012, she joined UTS as a Distinguished Professor of Statistics in the School of Mathematical and Physical Sciences.
Dr. Ryan is well known for her methodological contributions to statistical methods for cancer and environmental health research. She loves the challenge and satisfaction of multi-disciplinary collaboration. Equal to her scholarly efforts are her efforts to promote the careers of women and other groups traditionally under-represented in the profession. She has championed many causes to this end at many levels, including being a founder of the long-standing Fostering Diversity in Biostatistics Workshop at Eastern North American Region of the International Biometric Society (ENAR).
She has received a number of prestigious awards and honors, most recently her 2012 election to the Australian Academy of Science, a 2015 honorary doctorate from Ghent University, Harvard’s 2015 Centennial Medal and her new role as President of the International Biometrics Society. She is an Editor in Chief of Statistics in Medicine.
Research Interests:
Applied statistics
Biostatistics
epidemiological methods
Meta-Analysis
Quantative risk assessment
Qualifications:
Ph.D., 1983, Harvard University
BA (Hons), 1978, Macquarie University, Sydney
Projects
Publications
Invited talks, refereed proceedings and other conference outputs
Ryan, L. M.
(2018). Trajectory modelling for child growth data.
South African Statistical Association.
Ryan, L. M.
(2018). Statistical strategies for big data.
Brazilian Regional Meetings of the International Biometric Society.
Ryan, L. M.
(2018). Presidents Invited Address at International Society of Clinical Biostatistics.
International Society for Clinical Biostatistics.
Ryan, L. M.
(2018). Statistics and computers.
Meeting of the Eastern Mediterranean Region of the International Biometric Society.
Ryan, L. M.
(2017). Statistical Strategies for Big Data.
Meeting of the Eastern Mediterranean Region of the International Biometric Society.
Ryan, L. M.
(2017). But I'm a Data Scientist Too, Aren't I? .
Annual Meeting of the Eastern North American Region of the International Biometric Society.
Hwang, H., Wright ST., & Ryan L. M.
(2017). Covariate Discretisation on Big Data.
10th Conference of the IASC-ARS/68th Annual NZSA Conference.
Ryan, L. M.
(2016). Characterizing Child Growth Trajectories.
Joint Statistical Meeting 2016.
Ryan, L. M.
(2016). Trajectory modelling based on early childhood longitudinal growth data.
PAGE 2016.
Journal Articles
Akkaya‐Hocagil, T., Ryan L. M., Cook R. J., Jacobson S. W., Richardson G. A., Day N. L., et al.
(2022). A hierarchical meta-analysis for settings involving multiple outcomes across multiple cohorts.
Stat. 11(1), e462. doi: 10.1002/sta4.462
Hwang, H., & Ryan L. M.
(2020). Statistical strategies for the analysis of massive data sets.
Biometrical Journal. 62(2), 270-281. doi: 10.1002/bimj.201900034
Lee, J. Y. L., Green P. J., & Ryan L. M.
(2020). Analysis of grouped data using conjugate generalized linear mixed modelsSummary.
Biometrika. 107(1), 231-237. doi: 10.1093/biomet/asz053
Anderson, C., Hafen R., Sofrygin O., Ryan L. M., & Community members. of the H. B. G.
(2019). Comparing predictive abilities of longitudinal child growth models.
Statistics in Medicine. 38(19), 3555 - 3570. doi: 10.1002/sim.7693
Ryan, L. M.
(2019). Four papers on child growth modelling.
Statistics in Medicine. 38, 3505-3506. doi: 10.1002/sim.8180
Huque, H., Anderson C., Walton R., Wolford S., & Ryan L. M.
(2018). Smooth individual level covariates adjustment in disease mapping.
Biometrical Journal. 60(3), 597-615. doi: 10.1002/bimj.201700143
Ryan, L. M., Wright S., & Pham T.
(2018). A novel case‐control subsampling approach for rapid model exploration of large clustered binary data.
Statistics in Medicine. 37(6), 899-913. doi: 10.1002/sim.7543
Lee, J., Brown J., & Ryan L. M.
(2017). Sufficiency Revisited: Rethinking Statistical Algorithms in the Big Data Era.
The American Statistician. 71(3), 202 - 208. doi: 10.1080/00031305.2016.1255659
Wang, J., Bartlett M., & Ryan L. M.
(2017). On the impact of nonresponse in logistic regression: application to the 45 and Up study.
BMC Medical Research Methodology. 17(1), doi: 10.1186/s12874-017-0355-z
Anderson, C., & Ryan L. M.
(2017). A Comparison of Spatio-Temporal Disease Mapping Approaches Including an Application to Ischaemic Heart Disease in New South Wales, Australia.
International Journal of Environmental Research and Public Health. 14(2), 146-162. doi: 10.3390/ijerph14020146
Wang, J., Bartlett M., & Ryan L. M.
(2017). Non-ignorable missingness in logistic regression.
Statistics in Medicine. 36(19), 3005 - 3021. doi: 10.1002/sim.7349
Cramb, S., Mengersen KL., Lambert P. C., Ryan L. M., & Baade P. D.
(2016). A flexible parametric approach to examining spatial variation in relative survival.
Statistics in Medicine. 35(29), 5448-5463. doi: 10.1002/sim.7071
Waller, D., Mondy P., Brama T., Fisher J., King A., Malkov K., et al.
(2016). Determining the effect of vein visualization technology on donation success, vasovagal symptoms, anxiety and intention to re-donate in whole blood donors aged 18-30 years: A randomized controlled trial.
Vox Sanguinis. 111(2), 135-143. doi: 10.1111/vox.12407
Huque, MH., Carroll R. J., Diao N., Christiani D. C., & Ryan L. M.
(2016). Exposure Enriched Case-Control (EECC) design for the assessment of gene-environment interaction.
Genetic Epidemiology. 40(7), 570-578. doi: 10.1002/gepi.21986
Huque, MH., Bondell H. D., Carroll R. J., & Ryan L. M.
(2016). Spatial regression with covariate measurement error: A semiparametric approach.
Biometrics. 72(3), 678-686. doi: 10.1111/biom.12474
Huque, MH., Anderson C., Walton R., & Ryan L. M.
(2016). Individual level covariate adjusted conditional autoregressive (indiCAR) model for disease mapping.
International Journal of Health Geographics. 15(1), doi: 10.1186/s12942-016-0055-7
Ryan, L. M., Wand M., & Malecki A. A.
(2016). Bringing coals to Newcastle.
Significance. 13(6), 32-37. doi: 10.1111/j.1740-9713.2016.00982.x
Ryan, L. M.
(2015). A Conversation with Nan Laird.
Statistical Science. 30(4), 582-596. doi: 10.1214/15-STS528
Hunt, P. R., Friesen M. C., Sama S., Ryan L. M., & Milton D.
(2015). Log-Linear Modeling of Agreement among Expert Exposure Assessors.
Annals of Occupational Hygiene. 59(6), 764-774. doi: 10.1093/annhyg/mev011
Cramb, S., Baade P. D., White N., Ryan L. M., & Mengersen KL.
(2015). Inferring lung cancer risk factor patterns through joint Bayesian spatio-temporal analysis.
Cancer Epidemiology. 39(3), 430-439. doi: 10.1016/j.canep.2015.03.001
Huque, MH., Bondell H. D., & Ryan L. M.
(2014). On the impact of covariate measurement error on spatial regression modelling.
Environmetrics. 25(8), 560-570. doi: 10.1002/env.2305
Technical reports and unrefereed outputs
Ryan, L. M., Clark S., Pagendam D., & Hyndman R. J.
(2022). Statistical and machine learning strategies for multi-bore forecasting for the Namoi Valley.
Ryan, L. M.
(2020). Namoi Valley groundwater level modelling report.
Ryan, L. M., Chen V., Beavan A., Speilmann J., Sisson S., & Kohn R.
(2019). A longitudinal analysis of the developmental trajectories of domain specific and domain generic abilities in high-level football players.
