婚姻状况对宫癌相关预后的影响:一种倾向性得分匹配研究
Kaige Pei1,2, Mingrong Xi3,4
1Department of Gynecology and Obstetrics, West China Second University Hospital, Sichuan University, No. 20, Section 3, Renmin South Road, Wuhou District, Chengdu, Sichuan Province, China.
Scientific reports
|October 8, 2025
概括
未婚状态与子宫癌预后较差有关,未婚患者的癌症特异性存活率和整体存活率明显较低. 这项研究证实了婚姻状况作为子宫癌结果的独立风险因素.
科学领域:
- 在瘤学瘤学.
- 流行病学 流行病学
- 公共卫生 公共卫生
背景情况:
- 子宫癌是一个重要的全球健康问题,在全球范围内排名为女性第四常见的癌症.
- 虽然已知婚姻状况会影响各种癌症的预后,但其对宫癌结果的具体影响需要进一步阐明.
研究的目的:
- 调查婚姻状况与宫癌预后之间的关联.
- 确定未婚状态是否是宫癌特定存活率 (CSS) 和整体存活率 (OS) 的独立风险因素.
主要方法:
- 这是一项大型队列研究,涉及30853名宫癌患者.
- 倾向性得分匹配 (PSM) 用于平衡已婚和未婚群体之间的基线特征.
- 使用多变量考克斯回归分析来确定生存的独立风险因素.
主要成果:
- 在PSM之前,与已婚患者相比,未婚患者的CSS和OS显着较低 (p <0.001).
- 在1:1PSM之后,未婚状态仍然与较差的CSS (HR=1.07,p=0.003) 和OS (HR=1.13,p < 0.001) 有关.
- 多变量分析证实,单身身份是CSS (HR=1.12,p < 0.001) 和OS (HR=1.19,p < 0.001) 的独立风险因素.
结论:
- 未婚状态独立地与宫癌患者的预后较差有关.
- 婚姻状况是影响子宫癌结局的重要因素.
- 需要进一步的前性和机制性研究来验证这些发现,并探索潜在的生物机制.
相关概念视频
Cancer Survival Analysis
645
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
645
Wilcoxon Signed-Ranks Test for Matched Pairs
459
The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
459
Comparing the Survival Analysis of Two or More Groups
553
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
553
Odds Ratio
1.7K
The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...
1.7K
Sign Test for Matched Pairs
381
The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
To conduct the sign test, we first calculate the differences in...
381
Hazard Ratio
560
The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
For example, in a clinical trial...
560


