Dr. Jemila Hamid* PhD MSc BSc

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Dr. Jemila Hamid* PhD MSc BSc
Adjunct Professor

Professor, Department of Mathematics and Statistics
University of Ottawa


Work E-mail: jhamid@uottawa.ca
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Jemila Hamid

Biography

Professor Jemila Hamid holds a PhD and MSc in mathematical statistics AND Statistics & Computer Science double major undergraduate training. She has worked in a collaborative interdisciplinary environment as a biostatistician for almost 15 years. Currently Dr. Hamid is a full professor in the department of Mathematics and Statistics, University of Ottawa and is an affiliate scientist at the CHEO research institute. She conducts both methodological and applied research; and has published extensively both in statistics and biomedical journals. Professor Hamid has supervised several graduate students in Statistics, Biostatistics and Health Research Methodology as well as mentored several medical residents and clinical fellows. Her methodological research includes multivariate methods, methods for longitudinal data and growth curves, methods for interrupted time series analysis, methods for analysis of high-dimensional data as well as methods for data integration including evidence synthesis methods such as meta-analysis, network meta-analysis and additive models. In her applied work, she has been working in wide-range of clinical applications requiring wide-range of statistical methods and computational algorithms.

 

Selected Publications

  1. Jana S, Balakrishnan N, Hamid JS. Bayesian Growth Curve Model Useful for High-Dimensional Longitudinal Data. Journal of Applied Statistics 2019, 46:5, 814-834, DOI: 10.1080/02664763.2018.1517145
  2. Tricco AC, Thomas SM, Veroniki A, Hamid JS, Cogo E, et al. Quality improvement strategies to prevent falls in older adults: A systematic review and network meta-analysis, Journal of Age and Ageing 2019, doi: 10.1093/ageing/afy219
  3. Jana S, Balakrishnan N, Hamid JS. Inference in the Growth Curve Model under Multivariate Skewed Normal Distribution, Sankahya B 2018, DOI: 10.1007/s13571-018-0174-1
  4. Jana S, Balakrishnan N, Hamid JS. Estimation of the Parameters of an Extended Growth Curve Model under Multivariate Skew Normal Distribution. Journal of Multivariate Analysis 2018, Volume 166: 111-128
  5. Islam S, Anand S, McQueen M, Hamid JS, Thabane L, Yusuf S, Beyene J. Classification rules for identifying individuals at high risk of developing Myocardial Infarction based on ApoB, ApoA1 and the ratio were determined using a Bayesian approach. Journal of Applied Statistics 2018, 45(2)
  6. Daly C, Higgins V, Adeli K, Grey VL, Hamid JS. Reference interval estimation: Methodological comparison using extensive simulations and empirical data, Clinical Biochemistry 2017
  7. Ewusie J, Beyene J, Ahiadeke C, Hamid JS. Malnutrition in Pre-School Children across Different Geographic Areas and Socio-Demographic Groups in Ghana, Journal of Maternal and Child Health 2017, 21(4):
  8. Tricco AC, Thomas SM, Veroniki AA, Hamid JS, Cogo E, Strifler L, Khan PA, Robson R, Sibley KM, MacDonald H, MSc; Riva JJ, Thavorn K, Wilson C, Holroyd-Leduc J, Kerr GD, Feldman F, Majumdar SR, Jaglal SB, Hui W, Straus SE. Comparative Efficacy of Preventing Falls in Older Adults: A systematic review and network meta-analysis. JAMA 2017. 318(17):1687-1699.
  9. Jana S, von Rosen D, Balakrishinan N, Hamid JS. High dimensional extension of the growth curve model and its application in genetics. Journal of Statistical Methods and Applications 2017
  10. Bakel L, Hamid JS, Ewusie J, Liu K, Mussa J, Straus S, Parkin P, Cohen E. International Variation in Asthma and Bronchiolitis Guidelines. Pediatrics 2017; 140(5).
  11. Parkin P, Hamid JS, Borkhoff C, Abdullah K, Atenafu E, Birken C, Maguire J, Azad A, Hihhins V, Adeli K. Laboratory reference intervals in the assessment of iron status in young children. BMJ Pediatrics Open 2017; 1(1).
  12. Beyene J, Hamid JS. Longitudinal Data Analysis in Genome-Wide Association Studies.  Journal of Genetic Epidemiology 2014, Sep;38 Suppl 1:S68-73.
  13. Ewusie J, Beyene J, Ahiadeke C, Hamid JS. Prevalence of Anemia among Under-5 Children in the Ghanaian Population: Estimates from the Ghana Demographic and Health Survey, BMC Public Health 2014, 14:626 
  14. Hamid JS, Greenwood CMT, Beyene J. Weighted Kernel Fisher Discriminant (wKFD) Analysis for Integrating Heterogeneous data, Computational Statistics and Data Analysis 2012; 56: 2031–2040
  15. Hamid JS, Meaney C, Crowcroft NS, Granerod J, Beyene J. Potential risk factors associated with human encephalitis: An application of canonical correlation analysis, BMC Medical Research Methodology 2011; 11 (120)
  16. Hamid JS, Beyene J, von Rosen D. A novel trace test for the mean parameter in a multivariate growth curve model, Journal of Multivariate Analysis 2011; 102(2):238-251
  17. Hamid JS, Meaney C, Crowcroft NS, Granerod J, Beyene J. Cluster analysis for identifying sub-groups and selecting potential discriminatory variables in human encephalitis, BMC Infectious Diseases 2010, 10:364.
  18. Hamid JS, Beyene J. A multivariate growth curve model for ranking genes in replicated time course microarray data. Statistical Applications in Genetics and Molecular Biology 2009. Vol. 8, Iss. 1, Article 1.
  19. Beyene J, Atenafu EG, Hamid JS, To T, Sung L. Determining relative importance of variables in developing and validating a predictive model. BMC Medical Research Methodology 2009, 9:64 doi:10.1186/1471-2288-9-64
  20. Hamid JS, Roslin NM, Paterson AD, Beyene J. Using a latent growth curve model for an integrative assessment of the effects of genetic and environmental factors on multiple phenotypes. BMC Proceedings 2009; 3(Suppl 7): S44

Fields of Interest

  • Multivariate Methods
  • Growth Curve Models
  • Generalized Multivariate Analysis of Variance (GMANOVA) Models
  • Analysis of Longitudinal Data
  • Systematic Reviews and Meta-Analysis and Network Meta-Analysis
  • Methods for Data-Integration
  • Statistical Methods in Diagnostic and Laboratory Medicine
  • Analysis of High-Dimensional Data
  • Analysis of Routinely Collected Data
  • Statistical Methods Commonly Used in Implementation Science
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