Abstract
Objectives
To construct new prostate cancer staging lookup tables based on a dataset collated by the British Association of Urological Surgeons (BAUS) and to validate them and compare their predictive power with Partin tables.
Patients and methods
Complete data on 1701 patients was collated between 1999 and 2008 across 57 UK centres. Lookup tables were created for prediction of pathological stage (PS) using PSA level, biopsy Gleason score (GS) and clinical stage, replicating Partin's original approach.
Tables were generated using logistic regression (LR) and bootstrap resampling methods and were internally validated and externally validated using concordance indices (CI) and area under the receiver operating characteristic curve (AUROC) respectively.
Results
The CI and AUROC analyses indicate that Partin tables performed poorly on UK data in comparison with US data.
The UK prostate cancer tables performed better than Partin tables but the predictive power of all models was relatively poor.
Conclusion
The study shows that the predictive power of Partin tables is reduced when applied to the UK population.
Models generated using LR methodology have fundamental limitations, and we suggest alternative modelling methods such as Bayesian networks.
To construct new prostate cancer staging lookup tables based on a dataset collated by the British Association of Urological Surgeons (BAUS) and to validate them and compare their predictive power with Partin tables.
Patients and methods
Complete data on 1701 patients was collated between 1999 and 2008 across 57 UK centres. Lookup tables were created for prediction of pathological stage (PS) using PSA level, biopsy Gleason score (GS) and clinical stage, replicating Partin's original approach.
Tables were generated using logistic regression (LR) and bootstrap resampling methods and were internally validated and externally validated using concordance indices (CI) and area under the receiver operating characteristic curve (AUROC) respectively.
Results
The CI and AUROC analyses indicate that Partin tables performed poorly on UK data in comparison with US data.
The UK prostate cancer tables performed better than Partin tables but the predictive power of all models was relatively poor.
Conclusion
The study shows that the predictive power of Partin tables is reduced when applied to the UK population.
Models generated using LR methodology have fundamental limitations, and we suggest alternative modelling methods such as Bayesian networks.
| Original language | English |
|---|---|
| Pages (from-to) | 224-235 |
| Number of pages | 12 |
| Journal | British Journal of Medical and Surgical Urology |
| Volume | 5 |
| Issue number | 5 |
| Early online date | 30 Jan 2012 |
| DOIs | |
| Publication status | Published - Sept 2012 |
Keywords
- Bayesian networks
- logistic regression
- lookup table
- Partin table
- prostate cancer
- predictive model for staging
