Clinical trials and data linkage

Australia has world-class health data, but we’re not using it to its full potential, especially to boost medical breakthroughs through clinical trials. By linking existing data, we can uncover new insights, save time and effort. Combining administrative data has great potential to enhance findings and reduce some of the burden of data gathering.


Linking administrative data to clinical trial data can significantly enhance your research. It provides valuable insights, reduces the effort needed to collect data, and can improve the quality of findings. Below are some of the key benefits of using linked, routinely collected data in clinical trials:

Reduced costs
Time and effort saved
Increased validity and reliability
Reduced reporting and recall bias
Real world data

Access data that is already routinely collected. The cost of accessing this data is substantially less than the cost of prospective data collection.

Linking pre-existing data from whole populations and/or across decades can be done more efficiently than prospective data collection.

Linkage with whole of population data allows you to describe the representativeness of your trial population and the generalisability of its outcomes.

Augmenting trial data with linked administrative data can minimise missing information, improve data quality through multiple sources, and reduce reliance on patient memory.

Provide insight on the effects of interventions in the general population, including those who don’t fit the strict eligibility criteria of a clinical trial.


Potential ways to use linked data

Potential ways to use linked data

Measure healthcare use before/after intervention

Total feasibility assessment

Innovative clincial trial design

Pre-trial research on disease epidemiology, measure disease burden and key determinants, and identify eligible individuals or communities using linked data

Utilise longitudinal administrative data to assess healthcare and pharmaceutical use pre- and post-intervention.

Linkage with administrative health data can help assess feasibility before initiating a trial, reducing unnecessary research efforts

Facilitate more efficient trial approaches, such as cluster, adaptive, and platform trials, enabling faster translation of findings.

Measurement of primary and secondary endpoints

Identify eligible individuals or communities

Surveillance and monitoring

Health economic analysis

Whole-population data enables tracking of primary endpoints such as death, or secondary endpoints like hospital readmissions.

Support site selection, identify at-risk populations, and determine the number of eligible individuals meeting trial criteria within a given jurisdiction.

Assess product or intervention utilisation and monitor safety and report adverse drug events.

Certain data collections include payment and cost information, supporting robust health economic evaluations.

Long term follow-up

Representativeness of trial participants

Pre-recruitment insights and monitoring

Leverage linked data for extended follow-up, supplement missing follow-up data retrospectively, and assess long-term health outcomes and healthcare utilisation using the longitudinal structure of administrative datasets.

Linked data can be used to assess whether participants are representative of the broader population and examine representation of rural and remote populations, First Nations Australians, or individuals from lower socioeconomic backgrounds.

Linked data collected before recruitment can be used to establish the journey of participants before enrolment, such as pre-recruitment hospitalisation as a measure of the trajectory to care.


An industry-first offering that lowers the barriers to accessing linked data – improving health services and patient outcomes across Australia.

The Population Health Research Network (PHRN) is committed to supporting clinical trialists in using linked real-world data. In 2023, the PHRN received funding to develop a national integrated gateway that will help clinical trial researchers discover, link, access, and learn about linked data more efficiently. This initiative—Trial Link—aims to:

  • Improve the efficiency and cost-effectiveness of data linkage in clinical trials
  • Enable researchers to more easily discover, access, and use linked data
To achieve this, PHRN is delivering a program of work across five key areas:

Using linked data in clinical trials: how the PHRN can support you

Discover
Data
Access
Guide
Learn
  • New data product with shorter approval times (aggregate, synthetic)
  • Routine linkage of clinical registries
  • Data utility comparison studies
  • Establish a dedicated ELSI (Ethical, Legal & Social Implications) service to support triallists with:
    – legal guidance
    – ethics expertise
    – consumer involvement

Project examples

Pearce A et al. (2014). Can administrative data be used to measure chemotherapy side effects? https://doi.org/10.1586/14737167.2015.990888

Colvin L et al. (2013). Off-label use of ondansetron in pregnancy in Western Australia https://doi.org/10.1155/2013/909860

Colvin L et al. (2012). Early morbidity and mortality following in utero exposure to selective serotonin reuptake inhibitors: a population-based study in Western Australia https://doi.org/10.2165/11634190-000000000-00000  

Kerr SJ et al. (2011) All-cause mortality of elderly Australian veterans using COX-2 selective or nonselective NSAIDs: a longitudinal study https://doi.org/10.1111/j.1365-2125.2010.03702.x

Whitstock MT, Pearce CM, Ridout SC, Eckermann EJ. (2010). A retrospective analysis of VIOXX in Australia: using clinical trial data and linked administrative health data to predict patient groups at risk of an adverse drug event doi.org/10.1111/j.1753-6405.2009.00579.x

PReed RL, Roeger L, Kwok YH, et al. (2022). A general practice intervention for people at risk of poor health outcomes: the Flinders QUEST cluster randomised controlled trial and economic evaluation. https://doi.org/10.5694/mja2.51484 

Cynthia Papendick et al. (2019). A randomized trial of a 1-hour troponin T protocol in suspected acute coronary syndromes: Design of the Rapid Assessment of Possible ACS In the Emergency Department with high sensitivity Troponin T (RAPID-TnT) study. https://doi.org/10.1161/CIRCULATIONAHA.119.042891

Dieng M, Khanna N, Kasparian NA, et al. (2019). Cost-effectiveness of a psycho-educational intervention targeting fear of cancer recurrence in people treated for early-stage melanoma. https://doi.org/10.1007/s40258-019-00483-6

Gorham G, Howard K, Togni S, Lawton P, Hughes J, Majoni SW, et al. (2017). Economic and quality of care evaluation of dialysis service models in remote Australia: Protocol for a mixed methods study. https://doi.org/10.1186/s12913-017-2273-5 

Watts CG, Cust AE, Menzies SW, Mann GJ, Morton RL. (2016). Cost-Effectiveness of Skin Surveillance Through a Specialized Clinic for Patients at High Risk of Melanoma. https://doi.org/10.1200/JCO.2016.68.4308

Taylor C et al. (2016). Hydroxyethyl starch versus saline for resuscitation of patients in intensive care: long-term outcomes and cost-effectiveness analysis of a cohort from CHEST. https://doi.org/10.1016/S2213-2600(16)30120-5

Ward RL et al. (2015). Cost of cancer care for patients undergoing chemotherapy: The Elements of Cancer Care study. https://doi.org/10.1111/ajco.12354

Riley GF. (2009). Administrative and claims records as sources of health care cost data https://doi.org/10.1097/MLR.0b013e31819c95aa

Mikolaizak AS, Harvey L, Toson B, et al. (2022). Linking health service utilisation and mortality data-unravelling what happens after fall-related paramedic care. https://doi.org/10.1093/ageing/afab254

Dennis M, Kotchetkova I, Cordina R, Celermajer DS. (2018). Long-Term Follow-up of Adults Following the Atrial Switch Operation for Transposition of the Great Arteries – A Contemporary Cohort. https://doi.org/10.1016/j.hlc.2017.10.008

Hague WE et al. (2016). Long-Term Effectiveness and Safety of Pravastatin in Patients With Coronary Heart Disease: Sixteen Years of Follow-Up of the LIPID Study. https://doi.org/10.1161/CIRCULATIONAHA.115.018580  

Gallagher M, Cass A, Bellomo R, Finfer S, Gattas D, Lee J, et al. (2014). Long-term survival and dialysis dependency following acute kidney injury in intensive care: extended follow-up of a randomized controlled trial. https://doi.org/10.1371/journal.pmed.1001601

d’Udekem Y, Iyengar AJ, Galati JC, Forsdick V, Weintraub RG, Wheaton GR, et al. (2014). Redefining expectations of long-term survival after the Fontan procedure: twenty-five years of follow-up from the entire population of Australia and New Zealand. https://doi.org/10.1161/CIRCULATIONAHA.113.007764

Lewis JR, Calver J, Zhu K, et al. (2011). Calcium supplementation and the risks of atherosclerotic vascular disease in older women: results of a 5-year RCT and a 4.5-year follow-up. https://doi.org/10.1002/jbmr.176

Calver J, Wiltshire A, Holman CD, et al. (2005). Does health assessment improve health outcomes in indigenous people? An RCT with 13 years of follow-up. https://doi.org/10.1111/j.1467-842X.2005.tb00058.x

Magliano D, Liew D, Pater H, et al. (2003). Accuracy of the Australian National Death Index: comparison with adjudicated fatal outcomes among Australian participants in the Long-term Intervention with Pravastatin in Ischaemic Disease (LIPID) study. https://doi.org/10.1111/j.1467-842X.2003.tb00615.x

Harper C, Mafham M, Herrington W, et al. (2021). Comparison of the accuracy and completeness of records of serious vascular events in routinely collected data vs clinical trial–adjudicated direct follow-up data in the UK: secondary analysis of the ASCEND randomized clinical trial. https://dx.doi.org/10.1001/jamanetworkopen.2021.39748

Rahman ST, Waterhouse M, Romero BD, et al. (2023). Vitamin D supplementation and the incidence of cataract surgery in older Australian adults. https://doi.org/10.1016/j.ophtha.2022.09.015

Coorey, G., Campain, A., Mulley, J. et al. (2022). Utilisation of government-subsidised chronic disease management plans and cardiovascular care in Australian general practices. https://doi.org/10.1186/s12875-022-01763-2

Hay AE, Leung YW, Pater JL, et al. (2018). Pilot study of the ability to probabilistically link clinical trial patients to administrative data and determine long-term outcomes https://doi.org/10.1177/1740774518815653