• Cen, M.; Meng, X.; Hu, X.; Liu, J.; Wu, J. (2025). PDE-Based Bayesian Hierarchical Modeling for Event Spread, with Application to COVID-19 Infection. arXiv. https://arxiv.org/abs/2509.13174
• Arimoro, O.I.; Lix, L.M.; Patten, S.B.; Sawatzky, R.; Sebille, V.; Liu, J; Wiebe, S.; Josephson, C.B.; Sajobi, T.T. (2025). Tree-based latent variable model for assessing differential item functioning in patient-reported outcome measures: a simulation study. Quality of Life Research. https://doi.org/10.1007/s11136-025-04018-6
• Gheisary, Z.; Hoja, I.; Liu, J.; Papagerakis, P.; Weber, L.P.; Fenton, M.; Katselis, G.S.; Lieffers, J.R.L.; Papagerakis, S. (2024). Association of Sleep Quality and General, Mental, and Oral Health with Lifestyle Traits (Dietary Intake, Smoking Status) in Arthritis: A Cross-Sectional Study from the Canadian Community Health Survey (CCHS). Nutrients. 16(13): 2091. https://doi.org/10.3390/nu16132091
• Sharma, Y.; Laison, E.K.; Philippsen, T.; Ma, J.; Kong, J.; Ghaemi, S.; Liu, J.; Hu, F.; Nasri, B. (2024). Models and data used to predict the abundance and distribution of Ixodes scapularis (black-legged tick) in North America: a scoping review. The Lancet Regional Health – Americas. 32. https://doi.org/10.1016/j.lana.2024.100706
• Sajobi, T.T.; Sanusi, R.A.; Mayo, N.E.; Sawatzky, R.; Nielsen, L.K.; Sebille, V.; Liu, J.; Bohm, E.; Awosoga, O.; Norris, C.M.; Wilton, S.B.; James, M.T.; Lix, L.M. (2024). Unsupervised item response theory models for assessing sample heterogeneity in patient-reported outcomes measures. Quality of Life Research. 33: 853-864. https://doi.org/10.1007/s11136-023-03560-5
• Fu, H.; Neil, E.J.; Liu, J.; Si, B. (2024). A continuous root water uptake isotope mixing model. Water Resources Research. 60(8): e2023WR036852. https://doi.org/10.1029/2023WR036852
• Mansell, H.; Rosaasen, N.; Wichart, J.; Thielke, R.W.; Blackburn, D.; Liu, J.; Mainra, R.; Shoker, A.; Groot, B.; Wen, K.; Wong, A.; Bateni, B.; Luo, C.; Trivedi, P. (2024). Video education and behavior contract to improve outcomes after renal transplantation (VECTOR); a randomized controlled trial. Patient Preference and Adherence. 18: 1589-1602. https://doi.org/10.2147/PPA.S467142
• Liu, J.; Bellows, B.; Hu, X.J.; Wu, J.; Zhou, Z.; Soteros, C.; Wang, L. (2023). A new time-varying coefficient regression approach for analyzing infectious disease data. Scientific Reports. 13. https://doi.org/10.1038/s41598-023-41551-1
• Gheisary, Z.; Mahmood, R.; Shivanantham, A.H.; Liu, J.; Lieffers, J.R.L.; Papagerakis, P.; Papagerakis, S. (2022). The clinical, microbiological and immunological effects of probiotic supplementation on prevention and treatment of periodontal diseases: a systematic review and meta-analysis. Nutrients. 14: 1036. https://doi.org/10.3390/nu14051036
• Liu, J.; Afful, A.; Mansell, H.; Ma, Y. (2022). Bias analysis for misclassification errors in both the response variable and covariate. The American Statistician. 76: 353-362. https://doi.org/10.1080/00031305.2022.2066725
• Sajobi, T.T.; Lix, L.M.; Russell, L.; Schulz, D.; Liu, J.; Zumbo, B.D.; Sawatzky, R. (2022). Accuracy of mixture item response theory models for identifying sample heterogeneity in patient-reported outcomes: a simulation study. Quality of Life Research. 31(12): 3423-3432. https://doi.org/10.1007/s11136-022-03169-0
• Zhang, K.; Liu, J.; Liu, Y.; Zhang, P.; Carroll, R.J. (2022). Bayesian adjustment for measurement error in an offset variable in a Poisson regression model. Statistical Modelling. 22: 509-526. https://doi.org/10.1177/1471082X211008011
• Mansell, H.; Rosaasen, N.; Wichart, J.; Mainra, R.; Shoker, A.; Hoffert, M.; Blackburn, D.F.; Liu, J.; Groot, B.; Trivedi, P.; Willenborg, E.; Amararajan, M.; Wu, H.; Afful, A. (2021). A randomized controlled trial of a pre-transplant educational intervention in kidney patients. Transplantation Direct. 7(10): e753. https://doi.org/10.1097/TXD.0000000000001202
• Liu, J.; Ma, Y.; Johnstone, J. (2020). A Goodness-of-fit Test for Zero-Inflated Poisson Mixed Effects Models in Tree Abundance Studies. Computational Statistics & Data Analysis. 144: 106887. https://doi.org/10.1016/j.csda.2019.106887
• Obeidat, M.; Liu, J.; Osgood, N.; Klassen, G. (2019). Bayesian methods for time series of count data. Communications in Statistics – Simulation and Computation. 51(2): 486-504. https://doi.org/10.1080/03610918.2019.1655574
• Mansell, H.; Rosaasen, N.; West-Thielke, P.; Wichart, J.; Daley, C.; Mainra, R.; Shoker, A.; Liu, J.; Blackburn, D. (2019). Randomised controlled trial of a video intervention and behaviour contract to improve medication adherence after renal transplantation: the VECTOR study protocol. BMJ Open. 9(3): e025495. https://doi.org/10.1136/bmjopen-2018-025495
• Yan, Y.; Burbridge, C.; Shi, J.; Liu, J.; Kusalik, T. (2019). Effects of input data quantity on genome-wide association studies (GWAS). International Journal of Data Mining & Bioinformatics. 22: 19-43. https://doi.org/10.1504/IJDMB.2019.099286
• Salway, T.; Plöderl, M.; Liu, J.; Gustafson, P. (2019). Effects of multiple forms of information bias on estimated prevalence of suicide attempts by sexual orientation: An application of a Bayesian misclassification correction method to data from a systematic review. American Journal of Epidemiology. 188(1): 239-249. https://doi.org/10.1093/aje/kwy200
• Safarishahrbijari, A., Teyhouee, A., Waldner, C., Liu, J., Osgood, N. (2017). Predictive accuracy of particle filtering in dynamic models supporting outbreak projections. BMC Infectious Diseases, 17(648). https://doi.org/10.1186/s12879-017-2726-9
• Liu, J.; Gustafson, P.; Huo, D. (2016). Bayesian adjustment for the misclassification in both dependent and independent variables with application to a breast cancer study. Statistics in Medicine, 35(23):4252-63.
• Wu, X.; Sawatzky, R.; Hopman, W.; Mayo, N.; Sajobi, T.T.; Liu, J.; Prior, J.; Papaioannou, A.; Josse, R.G.; Towheed,T.; Davison, K.S.; Lix, L.M. (2016) Differential Item functioning in the SF-36 physical functioning and mental health sub-scales: a population-based investigation in the Canadian multicentre osteoporosis study. PLOS ONE, 11(3): e0151519. https://doi.org/10.1371/journal.pone.0151519
• Lix, L.M.; Chan, E.K.H.; Sawatzky, R.; Sajobi, T.T.; Liu, J.; Hopman, W.; Mayo, N. (2016). Response shift and disease activity in inflammatory bowel disease. Quality of Life Research, 25, 1751-1760.
• Brown, C.D.; Liu, J.; Yan, G.; Johnstone, J.F. (2015). Disentangling legacy effects from environmental filters of post-fire assembly of boreal tree assemblages. Ecology, 96, 3023-3032.
• Liu, J.; Liu, W.; Wu, L.; Yan, G. (2015). A flexible approach for multivariate mixed-effects models with non-ignorable missing values. Journal of Statistical Computation and Simulation, 85, 3727-3743.
• Sighoko, D.; Liu, J.; Hou, N.; Gustafson, P.; Huo, D. (2014). Discordance in hormone receptor status among primary, metastatic, and second primary breast cancers: biological difference or misclassification? The Oncologist, 19, 592-601.
• Liu, Y.; Liu, J.; Zhang, F. (2013). Bias analysis for misclassification in a multicategorical exposure in a logistic regression model. Statistics and Probability Letters, 83, 2621-2626.
• Vidovic, S.; Tsoi, S.; Medihala, P.; Liu, J.; Wylie, J.L.; Levette, P.N.; Korber, D. (2013). Molecular and antimicrobial susceptibility analyses distinguish clinical from bovine Escherichia coli O157 strains. Journal of Clinical Microbiology, 51, 2082-8.
• Lix, L.M.; Sajobi, T.T.; Sawatzky, R.; Liu, J.; Mayo, N.E.; Huang, Y.; Graff, L.A.; Walker, J.R.; Ediger, J.; Clara, I.; Sexton, K.; Carr, R.; Bernstein, C.N. (2013). Relative importance measures for reprioritization response shift. Quality of Life Research, 22, 695-703.
• Liu, J. and Gustafson P. (2012). Regression with fractional polynomials when interactions are erroneously omitted. Journal of Statistical Planning and Inference, 142,1348-1355.
• Alam M.K.; EI-Ganiny A.M.; Afronz, S.; Sanders, D.A.; Liu, J.; Kaminskyj, S.G. (2012). Aspergillus nidulans galactofuranose biosynthesis affects antifungal drug sensitivity. Fungal Genetics and Biology, 49, 1033-43.
• Szmigielski, A.M.; Schoenau, J.J.; Johnson, E.N.; Holm, F.A.; Sapsford, K.L.; Liu, J. (2012). Effects of soil factors on phytotoxicity and dissipation of sulfentrazone in canadian prairie soils. Communications in Soil Science and Plant Analysis, 43, 896-904.
• Zhang P.; Liu, J.; Dong, J.; Holovati, J.L.; Letcher, B.; McGann L.E. (2011). A Bayesian adjustment for multiplicative measurement errors for a calibration problem with applications to a stem cell study, Biometrics, 68(1), 268-274. https://doi.org/10.1111/j.1541-0420.2011.01633.x
• Liu, J. and Gustafson, P. (2010). On the detectability of different forms of interaction in regression models, Metrika, 75(3), 347-365. https://doi.org/10.1007/s00184-010-0294-8
• Ng, C.T.; Joe, H.; Karlis, D.; Liu, J. (2010). Composite likelihood for time series models with a latent autoregressive process, Statistica Sinica, 21, 279-305.
• Liu, J.; Gustafson, P.; Cherry, N.; Burstyn, I. (2009). Bayesian analysis of a matched case-control study with expert prior information on both the misclassification of exposure and the exposure-disease association. Statistics in Medicine, 28, 3411-3423. https://doi.org/10.1002/sim.3670
• Wu, L.; Liu, W.;Liu, J. (2009). A longitudinal study of children's aggressive behaviours based on multivariate mixed models with incomplete data. Canadian Journal of Statistics, 37, 435-452.
• Szmigielski, A.M.; Schoenau, J.J.; Johnson, E.; Holm, F.A.; Sapsford, K.L.; Liu, J. (2009). Development of a lab bioassay and effect of soil properties on sulfentrazone phytotoxicity in soil. Weed Technology, 23, 486-491.
• Burstyn, I.; Kapur, N.; Shalapay, C.; Bamforth, F.; Wild, C.; Liu, J.; LeGatt, D. (2009). Evaluation of the accuracy of self-reported smoking in pregnancy when the biomarker level in an active smoker is uncertain. Nicotine & Tobacco Research, 11, 670-678. https://doi.org/10.1093/ntr/ntp077
• Liu, J. and Gustafson, P.(2008). On average predictive comparisons and interactions. International Statistical Review, 76, 419-432.
• Chen, D.; Liu, J.; Zhang, F., 2006. The reversible nearest particle systems on a finite set. Bernoulli, 12, 101-111.