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Prognostic factors for squamous cervical carcinoma identified by competing-risks analysis: A study based on the SEER
Chengfeng Hu1, Junyan Cao1,2, Li Zeng1
1Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, China.
Medicine
|October 1, 2022
Summary
A competing-risks model, specifically the sub-distribution hazard model, offers a more accurate prognosis for squamous cervical carcinoma (SCC) patients than traditional Cox regression. This advanced statistical method better identifies key survival factors.
Area of Science:
- Oncology
- Biostatistics
- Epidemiology
Background:
- Cervical cancer, particularly squamous cervical carcinoma (SCC), presents a significant global health challenge with high incidence and mortality rates.
- Accurate prognostic evaluation is crucial for guiding clinical practice and improving patient outcomes in SCC management.
- Traditional Cox regression analysis may have limitations in accurately assessing survival in the presence of competing risks.
Purpose of the Study:
- To compare the efficacy of competing-risks models (cause-specific and sub-distribution hazard models) against conventional Cox regression for identifying prognostic factors in SCC patients.
- To determine the most suitable statistical method for predicting survival and informing clinical decisions in SCC.
Main Methods:
- Analysis of data from 5591 SCC patients from the Surveillance, Epidemiology, and End Results (SEER) database (2004-2013).
- Univariate analysis using cumulative incidence functions to identify potential risk factors.
- Comparison of Cox regression, cause-specific (CS) hazard, and sub-distribution (SD) hazard competing-risks models to assess prognostic factors.
Main Results:
- Age, metastasis, AJCC stage, surgery, chemotherapy, radiation sequence, lymph node dissection, tumor size, and tumor grade were significant prognostic factors across all models.
- Race and radiation status showed prognostic significance in Cox and CS analyses but differed in the SD analysis.
- Marital status (separated, divorced, widowed) was an independent prognostic factor in Cox regression but showed different results in CS and SD analyses.
Conclusions:
- Competing-risks models provide a more accurate identification of prognostic factors for SCC survival compared to standard Cox regression, which can yield biased results.
- The sub-distribution (SD) hazard model is potentially superior for estimating clinical prognosis in SCC patients.
- SD model results closely align with CS analysis, suggesting robust findings within competing-risks frameworks.
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