相关实验视频

Updated: Jul 24, 2025

Measurement of Lifespan in Drosophila melanogaster
10:00

Measurement of Lifespan in Drosophila melanogaster

Published on: January 7, 2013

34.4K

不朽的时间偏差与时间变化的暴露在环境流行病学:肺癌存活率的案例研究

Paige Sheridan, Chen Chen, Caroline A Thompson

    American journal of epidemiology
    |July 4, 2023
    PubMed
    概括

    不朽的时间偏差是临床研究中常见的问题,可能会影响环境流行病学研究. 这项研究表明,在随访期间平均空气污染暴露可以膨胀风险估计,而非离散时间方法,使暴露与随访开始保持一致.

    关键词:
    癌症 癌症 癌症 癌症 癌症有关因果推理的推理.环境流行病学环境流行病学不朽的时间偏差观察性研究是指观察性研究.目标试验 目标试验 目标试验

    更多相关视频

    Pretargeted Radioimmunotherapy Based on the Inverse Electron Demand Diels-Alder Reaction09:44

    Pretargeted Radioimmunotherapy Based on the Inverse Electron Demand Diels-Alder Reaction

    Published on: January 29, 2019

    10.1K
    Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index06:55

    Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

    Published on: January 8, 2020

    14.5K

    相关概念视频

    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...
    289
    Truncation in Survival Analysis01:09

    Truncation in Survival Analysis

    Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
    Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
    241
    Assumptions of Survival Analysis01:15

    Assumptions of Survival Analysis

    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.
    157
    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...
    12.0K
    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...
    227
    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,...
    190