Development and validation of clinical prediction models for breast cancer incidence and mortality: a protocol for a dual cohort study
Introduction Breast cancer is the most common cancer and the leading cause of cancer-related death in women worldwide. Risk prediction models may be useful to guide risk-reducing interventions (such as pharmacological agents) in women at increased risk or inform screening strategies for early detect...
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2022-03-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/12/3/e050828.full |
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| author | Stavros Petrou Julia Hippisley-Cox David Dodwell Simon Lord Ashley Kieran Clift Mike Brady Gary S. Collins |
| author_facet | Stavros Petrou Julia Hippisley-Cox David Dodwell Simon Lord Ashley Kieran Clift Mike Brady Gary S. Collins |
| author_sort | Stavros Petrou |
| collection | DOAJ |
| description | Introduction Breast cancer is the most common cancer and the leading cause of cancer-related death in women worldwide. Risk prediction models may be useful to guide risk-reducing interventions (such as pharmacological agents) in women at increased risk or inform screening strategies for early detection methods such as screening.Methods and analysis The study will use data for women aged 20–90 years between 2000 and 2020 from QResearch linked at the individual level to hospital episodes, cancer registry and death registry data. It will evaluate a set of modelling approaches to predict the risk of developing breast cancer within the next 10 years, the ‘combined’ risk of developing a breast cancer and then dying from it within 10 years, and the risk of breast cancer mortality within 10 years of diagnosis. Cox proportional hazards, competing risks, random survival forest, deep learning and XGBoost models will be explored. Models will be developed on the entire dataset, with ‘apparent’ performance reported, and internal-external cross-validation used to assess performance and geographical and temporal transportability (two 10-year time periods). Random effects meta-analysis will pool discrimination and calibration metric estimates from individual geographical units obtained from internal-external cross-validation. We will then externally validate the models in an independent dataset. Evaluation of performance heterogeneity will be conducted throughout, such as exploring performance across ethnic groups.Ethics and dissemination Ethics approval was granted by the QResearch scientific committee (reference number REC 18/EM/0400: OX129). The results will be written up for submission to peer-reviewed journals. |
| format | Article |
| id | doaj-art-d40ff2bf00b34be59c77612eee8d1c24 |
| institution | OA Journals |
| issn | 2044-6055 |
| language | English |
| publishDate | 2022-03-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-d40ff2bf00b34be59c77612eee8d1c242025-08-20T02:17:40ZengBMJ Publishing GroupBMJ Open2044-60552022-03-0112310.1136/bmjopen-2021-050828Development and validation of clinical prediction models for breast cancer incidence and mortality: a protocol for a dual cohort studyStavros Petrou0Julia Hippisley-Cox1David Dodwell2Simon Lord3Ashley Kieran Clift4Mike Brady5Gary S. Collins6Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UKprofessor of clinical epidemiology and general practice8 University of Oxford, Oxford, UK5 Early Phase Clinical Trials Unit, Department of Oncology, Medical Siences Division, University of Oxford, Oxford, UKNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK3Welsh Ambulance Service NHS Trust, UKCentre for Statistics in Medicine, University of Oxford, Oxford, UKIntroduction Breast cancer is the most common cancer and the leading cause of cancer-related death in women worldwide. Risk prediction models may be useful to guide risk-reducing interventions (such as pharmacological agents) in women at increased risk or inform screening strategies for early detection methods such as screening.Methods and analysis The study will use data for women aged 20–90 years between 2000 and 2020 from QResearch linked at the individual level to hospital episodes, cancer registry and death registry data. It will evaluate a set of modelling approaches to predict the risk of developing breast cancer within the next 10 years, the ‘combined’ risk of developing a breast cancer and then dying from it within 10 years, and the risk of breast cancer mortality within 10 years of diagnosis. Cox proportional hazards, competing risks, random survival forest, deep learning and XGBoost models will be explored. Models will be developed on the entire dataset, with ‘apparent’ performance reported, and internal-external cross-validation used to assess performance and geographical and temporal transportability (two 10-year time periods). Random effects meta-analysis will pool discrimination and calibration metric estimates from individual geographical units obtained from internal-external cross-validation. We will then externally validate the models in an independent dataset. Evaluation of performance heterogeneity will be conducted throughout, such as exploring performance across ethnic groups.Ethics and dissemination Ethics approval was granted by the QResearch scientific committee (reference number REC 18/EM/0400: OX129). The results will be written up for submission to peer-reviewed journals.https://bmjopen.bmj.com/content/12/3/e050828.full |
| spellingShingle | Stavros Petrou Julia Hippisley-Cox David Dodwell Simon Lord Ashley Kieran Clift Mike Brady Gary S. Collins Development and validation of clinical prediction models for breast cancer incidence and mortality: a protocol for a dual cohort study BMJ Open |
| title | Development and validation of clinical prediction models for breast cancer incidence and mortality: a protocol for a dual cohort study |
| title_full | Development and validation of clinical prediction models for breast cancer incidence and mortality: a protocol for a dual cohort study |
| title_fullStr | Development and validation of clinical prediction models for breast cancer incidence and mortality: a protocol for a dual cohort study |
| title_full_unstemmed | Development and validation of clinical prediction models for breast cancer incidence and mortality: a protocol for a dual cohort study |
| title_short | Development and validation of clinical prediction models for breast cancer incidence and mortality: a protocol for a dual cohort study |
| title_sort | development and validation of clinical prediction models for breast cancer incidence and mortality a protocol for a dual cohort study |
| url | https://bmjopen.bmj.com/content/12/3/e050828.full |
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