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Related Concept Videos

Censoring Survival Data01:09

Censoring Survival Data

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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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Comparing the Survival Analysis of Two or More Groups01:20

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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...
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Assumptions of Survival Analysis01:15

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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.
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Kaplan-Meier Approach01:24

Kaplan-Meier Approach

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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,...
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Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

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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
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Introduction To Survival Analysis01:18

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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.
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Random Change-Point Non-linear Mixed Effects Model for left-censored longitudinal data: An application to HIV

Binod Manandhar1, Hongbin Zhang1

  • 1City University of New York, Graduate School of Public Health, 55 W 125th St,New York, NY 10027.

Proceedings. American Statistical Association. Annual Meeting
|June 10, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical model to identify unknown change points in longitudinal data. The method accurately estimates individual trends after an event, using HIV viral load data as an example.

Keywords:
Antiretroviral therapyCensored dataChange-pointExpectation maximizationLongitudinal dataMetropolis-Hastings samplerMixed-effect modelStochastic approximation

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Longitudinal Data Analysis

Background:

  • Change-point models are crucial for analyzing longitudinal data to detect trend shifts.
  • Identifying the exact time of change is challenging, especially with unknown change points in population data.
  • Existing models often struggle with non-linear trends and left-censored observations.

Purpose of the Study:

  • To develop and validate an unknown change-point model for longitudinal data.
  • To accommodate both linear and non-linear mixed effects before and after a change point.
  • To handle left-censored data within a random change-point non-linear mixed effects framework.

Main Methods:

  • Utilized the stochastic approximation expectation maximization (SAEM) algorithm.
  • Incorporated the Metropolis-Hasting sampler for parameter estimation.
  • Applied the model to longitudinal viral load (VL) data from the New York City HIV surveillance registry.

Main Results:

  • Successfully fitted a random change-point non-linear mixed effects model to the VL data.
  • The model effectively estimated individual-specific trends and change points.
  • Demonstrated the model's capability in handling left-censored observations in real-world epidemiological data.

Conclusions:

  • The proposed unknown change-point model provides a robust framework for analyzing longitudinal data with trend shifts.
  • The SAEM algorithm with Metropolis-Hasting sampler is effective for fitting complex mixed-effects models.
  • This methodology offers valuable insights for understanding disease progression and intervention effects using HIV viral load data.