Related Experiment Video
Updated: Aug 25, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Demographic and pathologic factor regression to a growth rate model of p16-negative oral cavity squamous cell
Jacob G J Wihlidal1, Keng Yeow Tay2, S Danielle MacNeil1
1Department of Otolaryngology, Head and Neck Surgery, Schulich School of Medicine and Dentistry University of Western Ontario London Ontario Canada.
Objectives:
The current study aims to quantify the growth rate of p16-negative oral cavity squamous cell carcinoma, characterize causative relationships between demographic risk factors and tumor growth, and examine pathologic findings associated with the tumor growth rate at a tertiary care institution. It is hypothesized that causative relationships will be drawn between the individual sociodemographic and pathologic factors and oral cavity p16-negative squamous cell carcinoma growth rate.
Methods:
Prospectively recruited participants, receiving surgical intervention only, were followed from initial staging CT scan to surgical resection. Interval growth was calculated in cm3/week. Demographic information including age, sex, smoking history, alcohol consumption history, previous all-type malignancy, previous chemotherapy treatment, previous head or neck radiation exposure, and time interval elapsed between diagnosis and surgery was collected from each participant, and regression analysis was applied to determine causality.
Results:
Summary statistics revealed a mean growth rate for the study sample of 1.385cm3/week. Statistically significant regression correlations were detected between tumor growth and alcohol consumption, origination at the retromolar trigone, and clinical nodal stage.
Conclusions:
Through a small prospective cohort sample, the current study suggests clinical associations between alcohol consumption, origination at the retromolar trigone, and clinical nodal stage with rate of tumor growth. Future work will validate these relationships in a larger patient cohort, and against stronger modeling techniques.
Level Of Evidence:
Prospective non-random cohort design.
More Related Videos
Related Concept Videos
Cancer Survival Analysis
Abnormal Proliferation
Tumor Progression
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
The Retinoblastoma Gene
The first-ever tumor suppressor gene called Rb was identified in retinoblastoma - a rare eye tumor in children. In inherited forms of the disease, a child inherits one defective copy of the Rb gene, which predisposes them to retinoblastoma. However,...
Parametric Survival Analysis: Weibull and Exponential Methods
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Comparing the Survival Analysis of Two or More Groups

