Clinical trial NCT06268379: BMA and Dynamic Nomogram for Survival Prediction in Patients With CRC
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
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Colon Cancer
Model Disease
Keywords: Colorectal cancer; Translational statistics; Online dynamic nomogram; Bayesian variable selection; Survival prediction
Lead sponsor: Cabrini Health
Sponsor class: OTHER
First posted: 2024-02-20
Enrollment: 2475
Sex: ALL
Minimum age (years): 22
Maximum age (years): 101
Standard ages: ADULT
OLDER_ADULT
Observational model: OTHER
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: A retrospective study was conducted with the Cabrini Monash Colorectal Neoplasia Database 15. This prospectively maintained database includes data from both private (Cabrini) and public (The Alfred) hospitals in Melbourne, Australia. The study focused on patients who underwent surgery for colon cancer from January 2010 to December 2021.

Brief 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.

Detailed 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.

Eligibility criteria: 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.

Primary outcomes:
measure: OS
timeFrame: 2011-2021
measure: RFS
timeFrame: 2011-2021
Secondary outcomes:
Other outcomes:
Locations:
city: Melbourne
country: Australia
facility: Cabrini Health
state: Victoria
Study officials:
References:
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/caac.21660. Epub 2021 Feb 4.
citation: 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.
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.
citation: 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):7734. doi: 10.3390/ijerph18157734.
citation: Maity AK, Basu S, Ghosh S. Bayesian Criterion Based Variable Selection. J R Stat Soc Ser C Appl Stat. 2021 Aug;70(4):835-857. doi: 10.1111/rssc.12488. Epub 2021 Aug 7.