The Application of Simulation to Quantifying the Influence of Bias in Perinatal Epidemiology
| dc.contributor.author | Dunne, Jennifer | |
| dc.contributor.supervisor | Gavin Pereira | en_US |
| dc.contributor.supervisor | Gizachew Tessema | en_US |
| dc.date.accessioned | 2023-10-26T01:15:24Z | |
| dc.date.available | 2023-10-26T01:15:24Z | |
| dc.date.issued | 2023 | en_US |
| dc.identifier.uri | http://hdl.handle.net/20.500.11937/93624 | |
| dc.description.abstract |
Perinatal aetiological associations derived from observational data are susceptible to various types of bias. This thesis demonstrated the application of simulation methodologies to quantify the influence of bias in perinatal epidemiology through a series of simulation studies which quantified the magnitude and direction of bias mechanisms. A framework to guide epidemiologists in the development, implementation and reporting of simulation studies to quantify bias was developed. Simulation is a potent tool to the quantification of bias. | en_US |
| dc.publisher | Curtin University | en_US |
| dc.title | The Application of Simulation to Quantifying the Influence of Bias in Perinatal Epidemiology | en_US |
| dc.type | Thesis | en_US |
| dcterms.educationLevel | PhD | en_US |
| curtin.department | Curtin School of Population Health | en_US |
| curtin.accessStatus | Open access | en_US |
| curtin.faculty | Health Sciences | en_US |
| curtin.contributor.orcid | Dunne, Jennifer [0000-0002-1001-732X] | en_US |