在曾经结婚的孟加拉妇女中,与第一次分娩间隔时间相关的因素:对Cox-PH模型和参数模型的比较分析
Sarmistha Paul Setu1, Rasel Kabir1, Md Akhtarul Islam1,2
1Statistics Discipline, Science Engineering and Technology School, Khulna University, Khulna, Bangladesh.
PLOS global public health
|December 18, 2024
概括
日志-正常模型最好地识别了影响孟加拉妇女第一次分娩间隔 (FBI) 的因素. 关键因素包括教育,结婚年龄和避孕药的使用,这对于生育控制至关重要.
科学领域:
- 人口统计学 人口统计学
- 公共卫生 公共卫生
- 生物统计学 生物统计学
背景情况:
- 生育率受到第一个出生间隔 (FBI) 的影响.
- 了解影响FBI的因素对于人口增长和生育管理至关重要.
- 这项研究重点关注曾经结婚的孟加拉妇女.
研究的目的:
- 为了比较考克斯比例危险 (Cox-PH) 和加速失效时间 (AFT) 模型的性能.
- 评估显著因素对时间到第一个出生间隔 (FBI) 的影响.
- 在孟加拉妇女中确定FBI的关键决定因素.
主要方法:
- 利用了来自14,941名曾经结婚的孟加拉妇女 (2017-18 BDHS) 的数据.
- 应用了Cox-PH和AFT模型与韦布尔,指数和日志常态分布.
- 使用Akaike信息标准 (AIC) 和贝叶斯信息标准 (BIC) 选择最合适的模型.
主要成果:
- 与其他模型相比,AFT模型中的日志正常分布提供了更好的匹配.
- 与FBI相关的重要因素包括妇女的年龄,第一次结婚的年龄,母亲/父亲的教育,避孕药的使用和配偶年龄差异.
- 参数式AFT模型 (日志-正常) 有效评估了影响FBI的共变量.
结论:
- 参数 AFT 模型,特别是日志正常分布,更适合分析影响第一个出生间隔的因素.
- 促进高等教育,适当的结婚年龄和计划生育知识 (避孕) 可以帮助控制生育和管理分娩间隔.
相关概念视频
Assumptions of Survival Analysis
96
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
96
Parametric Survival Analysis: Weibull and Exponential Methods
356
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
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...
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...
356
Introduction To Survival Analysis
180
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
The primary goal of survival analysis is to estimate survival time—the time...
180
Comparing the Survival Analysis of Two or More Groups
149
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...
149
The Mantel-Cox Log-Rank Test
300
The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of...
300
Applications of Life Tables
50
Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...
50


