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Research article
Optimal risk classification with statistical evidence in endometrial cancer
Jiabin Chen
National Sun Yat-sen University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-6859-4293
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
It is often clinically useful to classify tumor markers into risk groups. This study was aimed to investigate whether beginning with a statistically sound method would find cut points more reasonable than conventional ones.
Methods
We used data of endometrial cancer including 442 patients. The optimal number of cutoffs was based on the Akaike criterion and statistical algorithms were adapted to find the best locations. Codes were provided as a package.
Results
Myometrium invasion was an independent risk factor for lymph nodal metastasis when stratified into three groups by 0.41 and 0.89. Tumor size was an independent risk factor for overall survival when stratified into two groups by 4.11 cm. Both had better prediction than conventional choices and clinical relevance.
Conclusion
A statistically sound algorithm should be used to stratify patients into risk groups.
Keywords
risk classification, Akaike, survival, logistic regression, endometrial cancer
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This is a preprint. It has not completed peer review.
Research article
Optimal risk classification with statistical evidence in endometrial cancer
Jiabin Chen
National Sun Yat-sen University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-6859-4293
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 28 May, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Health Economics & Outcomes Research
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
It is often clinically useful to classify tumor markers into risk groups. This study was aimed to investigate whether beginning with a statistically sound method would find cut points more reasonable than conventional ones.
Methods
We used data of endometrial cancer including 442 patients. The optimal number of cutoffs was based on the Akaike criterion and statistical algorithms were adapted to find the best locations. Codes were provided as a package.
Results
Myometrium invasion was an independent risk factor for lymph nodal metastasis when stratified into three groups by 0.41 and 0.89. Tumor size was an independent risk factor for overall survival when stratified into two groups by 4.11 cm. Both had better prediction than conventional choices and clinical relevance.
Conclusion
A statistically sound algorithm should be used to stratify patients into risk groups.
Figure 1
Figure 2