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Melle S. Sieswerda

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Articles

Identifying Confounders Using Bayesian Networks and Estimating Treatment Effect in Prostate Cancer With Observational Data

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By Melle S. Sieswerda, Inigo Bermejo, Iñigo Bermejo
When evaluating treatment effect, data from a double-blinded, randomized controlled trial (RCT) are considered the golden standard; random assignment ensures that baseline characteristics between intervention and control group are similar. Unfortunately, RCTs have limitations too. They are costly to perform and tend to have short-term primary outcomes.
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Predicting Lung Cancer Survival Using Probabilistic Reclassification of TNM Editions With a Bayesian Network

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By Melle S. Sieswerda, Inigo Bermejo, Iñigo Bermejo, Mieke J. Aarts, Gijs Geleijnse
| ascopubs.org
In cancer care, the TNM system for classification of malignant tumors guides treatment decisions, aids in stratifying patients for research, and helps clinicians assess prognosis.1,2 This is done by classifying characteristics of the tumor (T descriptor), local lymph nodes (N descriptor), and distant metastases (M descriptor). These descriptors can subsequently be used to compute a stage grouping, essentially summarizing the information.
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Impact on Quality of Documentation and Workload of the Introduction of a National Information Standard for Tumor Board Reporting

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By Aafke Honkoop, Melle S. Sieswerda, Joan B. Heijns, Maud Bessems
| ascopubs.org
Kees C. W. J. Ebben, MSc1; Melle S. Sieswerda, MD, MSc1; Ernest J. T. Luiten, MD, PhD2; Joan B. Heijns, MD3; Carmen C. van der Pol, MD4; Maud Bessems, MD, PhD5,6; Aafke H. Honkoop, MD, PhD6,7; Mathijs P. Hendriks, MD8; Janneke Verloop, PhD1; and Xander A. A. M.
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