Quantifying the influence of bias in reproductive and perinatal epidemiology through simulation
The application of simulated data in epidemiological studies enables the illustration and quantification of the magnitude of various types of bias commonly found in observational studies. This was a review of the application of simulation methods to the quantification of bias in reproductive and per...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Language: | English |
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ELSEVIER SCIENCE INC
2021
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| Subjects: | |
| Online Access: | http://purl.org/au-research/grants/nhmrc/1099655 http://hdl.handle.net/20.500.11937/93729 |
| _version_ | 1848765775924428800 |
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| author | Dunne, Jennifer Tessema, Gizachew Ognjenovic, Milica Pereira, Gavin |
| author_facet | Dunne, Jennifer Tessema, Gizachew Ognjenovic, Milica Pereira, Gavin |
| author_sort | Dunne, Jennifer |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The application of simulated data in epidemiological studies enables the illustration and quantification of the magnitude of various types of bias commonly found in observational studies. This was a review of the application of simulation methods to the quantification of bias in reproductive and perinatal epidemiology and an assessment of value gained. A search of published studies available in English was conducted in August 2020 using PubMed, Medline, Embase, CINAHL, and Scopus. A gray literature search of Google and Google Scholar, and a hand search using the reference lists of included studies was undertaken. Thirty-nine papers were included in this study, covering information (n =14), selection (n = 14), confounding (n = 9), protection (n=1), and attenuation bias (n=1). The methods of simulating data and reporting of results varied, with more recent studies including causal diagrams. Few studies included code for replication. Although there has been an increasing application of simulation in reproductive and perinatal epidemiology since 2015, overall this remains an underexplored area. Further efforts are required to increase knowledge of how the application of simulation can quantify the influence of bias, including improved design, analysis and reporting. This will improve causal interpretation in reproductive and perinatal studies. |
| first_indexed | 2025-11-14T11:40:37Z |
| format | Journal Article |
| id | curtin-20.500.11937-93729 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:40:37Z |
| publishDate | 2021 |
| publisher | ELSEVIER SCIENCE INC |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-937292023-11-22T02:02:10Z Quantifying the influence of bias in reproductive and perinatal epidemiology through simulation Dunne, Jennifer Tessema, Gizachew Ognjenovic, Milica Pereira, Gavin Science & Technology Life Sciences & Biomedicine Public, Environmental & Occupational Health Selection Bias Confounding Information Bias Misclassification Collider  Statistical Modelling LEFT TRUNCATION BIAS TO-PREGNANCY DATA GESTATIONAL-AGE BIRTH-WEIGHT SENSITIVITY-ANALYSIS MEASUREMENT ERROR SELECTION BIAS PRETERM BIRTH TIME IMPACT Confounding Information Bias Misclassification;, Collider Selection Bias Statistical Modelling Bias Computer Simulation Female Humans Pregnancy Humans Pregnancy Computer Simulation Female Bias The application of simulated data in epidemiological studies enables the illustration and quantification of the magnitude of various types of bias commonly found in observational studies. This was a review of the application of simulation methods to the quantification of bias in reproductive and perinatal epidemiology and an assessment of value gained. A search of published studies available in English was conducted in August 2020 using PubMed, Medline, Embase, CINAHL, and Scopus. A gray literature search of Google and Google Scholar, and a hand search using the reference lists of included studies was undertaken. Thirty-nine papers were included in this study, covering information (n =14), selection (n = 14), confounding (n = 9), protection (n=1), and attenuation bias (n=1). The methods of simulating data and reporting of results varied, with more recent studies including causal diagrams. Few studies included code for replication. Although there has been an increasing application of simulation in reproductive and perinatal epidemiology since 2015, overall this remains an underexplored area. Further efforts are required to increase knowledge of how the application of simulation can quantify the influence of bias, including improved design, analysis and reporting. This will improve causal interpretation in reproductive and perinatal studies. 2021 Journal Article http://hdl.handle.net/20.500.11937/93729 10.1016/j.annepidem.2021.07.033 English http://purl.org/au-research/grants/nhmrc/1099655 http://purl.org/au-research/grants/nhmrc/1173991 http://purl.org/au-research/grants/nhmrc/1195716 ELSEVIER SCIENCE INC unknown |
| spellingShingle | Science & Technology Life Sciences & Biomedicine Public, Environmental & Occupational Health Selection Bias Confounding Information Bias Misclassification Collider  Statistical Modelling LEFT TRUNCATION BIAS TO-PREGNANCY DATA GESTATIONAL-AGE BIRTH-WEIGHT SENSITIVITY-ANALYSIS MEASUREMENT ERROR SELECTION BIAS PRETERM BIRTH TIME IMPACT Confounding Information Bias Misclassification;, Collider Selection Bias Statistical Modelling Bias Computer Simulation Female Humans Pregnancy Humans Pregnancy Computer Simulation Female Bias Dunne, Jennifer Tessema, Gizachew Ognjenovic, Milica Pereira, Gavin Quantifying the influence of bias in reproductive and perinatal epidemiology through simulation |
| title | Quantifying the influence of bias in reproductive and perinatal epidemiology through simulation |
| title_full | Quantifying the influence of bias in reproductive and perinatal epidemiology through simulation |
| title_fullStr | Quantifying the influence of bias in reproductive and perinatal epidemiology through simulation |
| title_full_unstemmed | Quantifying the influence of bias in reproductive and perinatal epidemiology through simulation |
| title_short | Quantifying the influence of bias in reproductive and perinatal epidemiology through simulation |
| title_sort | quantifying the influence of bias in reproductive and perinatal epidemiology through simulation |
| topic | Science & Technology Life Sciences & Biomedicine Public, Environmental & Occupational Health Selection Bias Confounding Information Bias Misclassification Collider  Statistical Modelling LEFT TRUNCATION BIAS TO-PREGNANCY DATA GESTATIONAL-AGE BIRTH-WEIGHT SENSITIVITY-ANALYSIS MEASUREMENT ERROR SELECTION BIAS PRETERM BIRTH TIME IMPACT Confounding Information Bias Misclassification;, Collider Selection Bias Statistical Modelling Bias Computer Simulation Female Humans Pregnancy Humans Pregnancy Computer Simulation Female Bias |
| url | http://purl.org/au-research/grants/nhmrc/1099655 http://purl.org/au-research/grants/nhmrc/1099655 http://purl.org/au-research/grants/nhmrc/1099655 http://hdl.handle.net/20.500.11937/93729 |