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Method Article
Sebastiaan Remmers
Erasmus University Medical Center Rotterdam
Corresponding Author
ORCiD: https://orcid.org/0000-0001-9530-6448
License:
This work is licensed under a CC BY 4.0 License. Read Full License
The objective of this study is to develop and validate patient-level prediction models for patients on watchful waiting (WW) estimating the risk of developing symptomatic progression, hospitalization, ER visit, initiation of curative or palliative treatment, and survival. Estimation for all clinical models will be done based on 1) age and clinical measurements (e.g., PSA) 6 months before diagnosis, 2) age, clinical measurements 6 months before diagnosis, and clinical conditions one year before diagnosis. Finally, a clinically usable model will be developed based on expert clinical input. All prediction models will be implemented using Lasso logistic regression for the time at risk analyses.
Keywords
big data, prostatic neoplasm, prediction
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This is a list of supplementary files associated with this preprint. Click to download.
- FinalprotocolPackagePLPv2.docx
Full protocol including tables and apendices
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An open repository of community-contributed protocols sponsored by Nature Research.
Learn more about Protocol Exchange
Method Article
Sebastiaan Remmers
Erasmus University Medical Center Rotterdam
Corresponding Author
ORCiD: https://orcid.org/0000-0001-9530-6448
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Posted 07 Jun, 2021
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License:
This work is licensed under a CC BY 4.0 License. Read Full License
The objective of this study is to develop and validate patient-level prediction models for patients on watchful waiting (WW) estimating the risk of developing symptomatic progression, hospitalization, ER visit, initiation of curative or palliative treatment, and survival. Estimation for all clinical models will be done based on 1) age and clinical measurements (e.g., PSA) 6 months before diagnosis, 2) age, clinical measurements 6 months before diagnosis, and clinical conditions one year before diagnosis. Finally, a clinically usable model will be developed based on expert clinical input. All prediction models will be implemented using Lasso logistic regression for the time at risk analyses.
Figure 1
Figure 2
This is a list of supplementary files associated with this preprint. Click to download.
- FinalprotocolPackagePLPv2.docx
Full protocol including tables and apendices
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