BMA and Dynamic Nomogram for Survival Prediction in Patients With CRC

Colon Cancer Clinical Trial

Official title:

Developing a Clinician-friendly Online Tool for Survival Prediction in Colon Cancer Patients: A Bayesian Model Averaging for Risk Factor Selection and Dynamic Nomogram

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT06268379
• Other study ID #: #08-01-03-23
• Status: Completed
• Start date: February 15, 2010
• Est. completion date: December 15, 2021

Study information

• Verified date: February 2024
• Source: Cabrini Health
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational [Patient Registry]

Clinical Trial Summary

This project will examine the outstanding statistical techniques for predicting the survival of patients with colorectal cancer (CRC) (colorectal neoplasia database). The motivating clinical question that led to proposing this project is based on the general assumption that: "Right-sided colorectal cancer (CRC) has worse survival than left-sided CRC." The question is, which aspects of the patient's characteristics are responsible for this difference? This led us to BMA model selection and provide a clinician-friendly online nomogram.

Description:

Translational statistics merges biostatistics and clinical research to communicate research findings effectively. Nomograms, graphical representations integrating independent prognostic factors, are valuable tools in colorectal cancer (CRC) research. Bayesian models for variable selection in survival outcome prediction offer advantages through Bayesian model averaging (BMA). This study aimed to utilise BMA for variable selection and develop a clinician-friendly online dynamic nomogram for survival prediction. A retrospective study utilised the Cabrini Monash colorectal neoplasia database, including colon cancer patients who underwent surgery. Data on demographics, perioperative risks, treatment details, mortality, morbidity, and survival were collected. BMA was employed for Bayesian variable selection to identify effective risk factors for survival prediction. Sensitivity analyses using Cox-LASSO and imputation of missing data were performed. Prognostic online dynamic nomograms were constructed using selected risk factors and the R-package DynNom.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 2475
• Est. completion date: December 15, 2021
• Est. primary completion date: December 15, 2021
• Gender: All
• Age group: 22 Years to 101 Years

Eligibility

Inclusion Criteria:

In this study, patients were included based on specific selection criteria: being 18 years old or older, having a diagnosis of colon adenocarcinoma (or post polypectomy of the same condition), and having undergone surgery for colon cancer. Exclusion Criteria: Patients with rectal cancer, neuroendocrine tumours, lymphomas and those who underwent trans-anal surgery were not included in the study.

Related Conditions & MeSH terms

• Colon Cancer
• Colonic Neoplasms

Intervention

Procedure:
• Surgery
Not an interventional study, it is an observational, longitudinal study.

Locations

Country	Name	City	State
Australia	Cabrini Health	Melbourne	Victoria

Sponsors (1)

Lead Sponsor	Collaborator
Cabrini Health

Country where clinical trial is conducted

Australia, 

References & Publications (5)

Borumandnia N, Doosti H, Jalali A, Khodakarim S, Charati JY, Pourhoseingholi MA, Talebi A, Agah S. Nomogram to Predict the Overall Survival of Colorectal Cancer Patients: A Multicenter National Study. Int J Environ Res Public Health. 2021 Jul 21;18(15):77 — View Citation

Jalali A, Alvarez-Iglesias A, Roshan D, Newell J. Visualising statistical models using dynamic nomograms. PLoS One. 2019 Nov 15;14(11):e0225253. doi: 10.1371/journal.pone.0225253. eCollection 2019. — View Citation

Maity AK, Basu S, Ghosh S. Bayesian criterion-based variable selection. Journal of the Royal Statistical Society Series C: Applied Statistics. 2021;70(4):835-857.

Siegel RL, Miller KD, Goding Sauer A, Fedewa SA, Butterly LF, Anderson JC, Cercek A, Smith RA, Jemal A. Colorectal cancer statistics, 2020. CA Cancer J Clin. 2020 May;70(3):145-164. doi: 10.3322/caac.21601. Epub 2020 Mar 5. — View Citation

Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209-249. doi: 10.3322/caa — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	OS	Overall Survival, time from sugary to death or last follow up	2011-2021
Primary	RFS	Relapse-free Survival, time from sugary to death or last follow up for those without relapse.	2011-2021