长期条件积累和老年人死亡率的轨迹:使用英语老龄化纵向研究的基于群体的轨迹建模方法

相关实验视频

Updated: Jun 21, 2025

Assessment of Dependence in Activities of Daily Living Among Older Patients in an Acute Care Unit
06:52

Assessment of Dependence in Activities of Daily Living Among Older Patients in an Acute Care Unit

Published on: September 30, 2020

9.7K

Christos V Chalitsios1, Cornelia Santoso1, Yvonne Nartey1

  • 1Primary Care Research Centre, University of Southampton, Southampton, UK.

BMJ open
|July 11, 2024
PubMed
概括

老年人发展长期条件 (LTC) 沿着不同的轨迹. 确定这些模式有助于预测死亡风险,并为更好的健康结果量身定制干预措施.

关键词:
流行病学流行病学老年医学 老年医学公共卫生公共卫生.

更多相关视频

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K
Measurement of Lifespan in Drosophila melanogaster10:00

Measurement of Lifespan in Drosophila melanogaster

Published on: January 7, 2013

34.3K

相关概念视频

Longitudinal Research02:20

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
11.9K
Longitudinal Studies01:26

Longitudinal Studies

Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
156
Actuarial Approach01:20

Actuarial Approach

The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
73
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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...
175
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
123
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

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...
212