The Application of Simulation to Quantifying the Influence of Bias in Perinatal Epidemiology
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 m...
| Main Author: | |
|---|---|
| Format: | Thesis |
| Published: |
Curtin University
2023
|
| Online Access: | http://hdl.handle.net/20.500.11937/93624 |
| _version_ | 1848765757331079168 |
|---|---|
| author | Dunne, Jennifer |
| author_facet | Dunne, Jennifer |
| author_sort | Dunne, Jennifer |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:40:19Z |
| format | Thesis |
| id | curtin-20.500.11937-93624 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:40:19Z |
| publishDate | 2023 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-936242023-10-26T01:15:24Z The Application of Simulation to Quantifying the Influence of Bias in Perinatal Epidemiology Dunne, Jennifer 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. 2023 Thesis http://hdl.handle.net/20.500.11937/93624 Curtin University fulltext |
| spellingShingle | Dunne, Jennifer The Application of Simulation to Quantifying the Influence of Bias in Perinatal Epidemiology |
| title | The Application of Simulation to Quantifying the Influence of Bias in Perinatal Epidemiology |
| title_full | The Application of Simulation to Quantifying the Influence of Bias in Perinatal Epidemiology |
| title_fullStr | The Application of Simulation to Quantifying the Influence of Bias in Perinatal Epidemiology |
| title_full_unstemmed | The Application of Simulation to Quantifying the Influence of Bias in Perinatal Epidemiology |
| title_short | The Application of Simulation to Quantifying the Influence of Bias in Perinatal Epidemiology |
| title_sort | application of simulation to quantifying the influence of bias in perinatal epidemiology |
| url | http://hdl.handle.net/20.500.11937/93624 |