Overview
Background
Dr Trish Gilholm is a Research Fellow (4 years post‑PhD) within the Children’s Intensive Care Research Program, Child Health Research Centre. Her emerging research programs explore 1) long‑term outcomes for critically ill children using data linkage and 2) adaptive trial designs in paediatric critical care. Dr Gilholm completed her PhD in statistics at the Australian Centre of Excellence in Mathematical and Statistical Frontiers, Queensland University of Technology (PhD conferral September 2021) and was awarded an Executive Dean Commendation for Outstanding Doctoral Thesis Award for her PhD thesis. Through her developing research programs in adaptive trial design and data linkage, she has established a unique research profile within paediatric critical care. She is currently supervising 1xHonours (Principal Advisor), 1xPhD (Associate Advisor) and regularly supervises undergraduate and medical school research projects. Her outstanding commitment to research and future potential as a researcher was recognised with the 2024 Child Health Research Centre Rising Star of the Year Award.
Availability
- Dr Trish Gilholm is:
- Available for supervision
Qualifications
- Bachelor (Honours) of Psychological Science, The University of Queensland
- Masters (Research), Utrecht University
- Doctor of Philosophy, Queensland University of Technology
Research interests
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Long-term outcomes of critically ill children.
Children admitted to intensive care units (ICUs) are at higher risk of long-term physical, cognitive and psychological morbidities, which can impact their quality of life into adulthood. Through data linkage of the national PICU registry with external data sources encompassing health, education and socio-economic domains, my research develops predictive models of long-term developmental and educational outcomes. These models identify the modifiable and non-modifiable factors during PICU admission which contribute to poor long-term outcomes for these children.
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Adaptive clinical trials in paediatric critical care
Adaptive designs offer a flexible and efficient alternative to traditional randomised controlled trials (RCTs), however their use in the paediatric intensive care unit setting has been limited. My research aims to identify the barriers to implementation, increase awareness in the PICU community, and demonstrate the effectiveness of adaptive designs in paediatric critical care RCTs.
Works
Search Professor Trish Gilholm’s works on UQ eSpace
2020
Journal Article
Identifying latent subgroups of children with developmental delay using Bayesian sequential updating and Dirichlet process mixture modelling
Gilholm, Patricia, Mengersen, Kerrie and Thompson, Helen (2020). Identifying latent subgroups of children with developmental delay using Bayesian sequential updating and Dirichlet process mixture modelling. PLoS One, 15 (6) e0233542, 1-17. doi: 10.1371/journal.pone.0233542
2018
Journal Article
Is inspiring group members an effective predictor of social dominance in early adolescence? direct and moderated effects of behavioral strategies, social skills, and gender on resource control and popularity
Vermande, Marjolijn M., Gilholm, Patricia A., Reijntjes, Albert H. A., Hessen, Dave J., Sterck, Elisabeth H. M. and Overduin-de Vries, Anne M. (2018). Is inspiring group members an effective predictor of social dominance in early adolescence? direct and moderated effects of behavioral strategies, social skills, and gender on resource control and popularity. Journal of Youth and Adolescence, 47 (9), 1813-1829. doi: 10.1007/s10964-018-0830-9
Funding
Current funding
Past funding
Supervision
Availability
- Dr Trish Gilholm is:
- Available for supervision
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Available projects
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Methodological Advances in Paediatric Critical Care Trials: Novel Approaches for Cluster Crossover and Registry‑Enabled Designs
Join a leading paediatric critical care research team and contribute to advancing clinical trial methodology. This PhD project focuses on developing and applying innovative statistical approaches for cluster crossover and registry‑enabled randomized trials, using the landmark RESONANCE‑PICU study as a real‑world application setting.
The successful candidate will explore cutting‑edge methods for trial design and data integration within large clinical registries, working closely with biostatisticians, clinicians, and national research networks. This is an exceptional opportunity for a statistics/mathematics graduate to help shape how future large‑scale trials are conducted, while gaining hands‑on experience with rich, high‑quality paediatric ICU data.
Ideal for candidates with a background in statistics, biostatistics, mathematics, data science, epidemiology, or related quantitative fields, this PhD offers strong mentorship, collaboration across Australia and New Zealand, and the chance to make a meaningful impact on clinical research and child health.
Supervision history
Current supervision
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Doctor Philosophy
Machine learning in the Paediatric Intensive Care Unit: Development of Risk Prediction Models
Associate Advisor
Other advisors: Dr Sainath Raman, Dr Moloud Abdar, Professor Kristen Gibbons
Media
Enquiries
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