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Related Concept Videos

Longitudinal Studies01:26

Longitudinal Studies

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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...
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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...
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Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
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Introduction To Survival Analysis01:18

Introduction To Survival Analysis

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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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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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Modeling Disease Progression with Longitudinal Markers.

Lurdes Y T Inoue1, Ruth Etzioni2, Christopher Morrell3

  • 1Department of Biostatistics, University of Washington, F-600 Health Sciences Building, Box 357232, Seattle, WA, 98195.

Journal of the American Statistical Association
|January 24, 2014
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Summary
This summary is machine-generated.

This study introduces a Bayesian model to track prostate cancer progression using biomarker data and patient age. The model links biomarker levels to disease onset and advancement, improving understanding of prostate cancer natural history.

Keywords:
Markov Chain Monte Carlo methodsNatural history modeldisease progressionlatent variableslongitudinal responseprostate specific antigen

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Oncology

Background:

  • Understanding disease progression is crucial for effective treatment strategies.
  • Longitudinal biomarker data offers insights into disease dynamics.
  • Prostate cancer's natural history requires further elucidation.

Purpose of the Study:

  • To propose a novel Bayesian natural history model for disease progression.
  • To jointly model longitudinal biomarker levels, age at detection, and disease status.
  • To investigate the natural history of prostate cancer using prostate-specific antigen (PSA) data.

Main Methods:

  • Developed a Bayesian natural history model.
  • Employed joint modeling of longitudinal biomarker data and clinical variables.
  • Utilized an underlying latent disease process to link biomarker levels to disease onset and progression.
  • Applied the model to data from the Baltimore Longitudinal Study of Aging.

Main Results:

  • The proposed model successfully links longitudinal biomarker responses to the natural history of disease.
  • Demonstrated the dependence of disease transition to advanced stages on biomarker levels.
  • Provided insights into prostate cancer progression using PSA dynamics.

Conclusions:

  • The Bayesian model offers a robust framework for studying disease progression.
  • Joint modeling enhances the understanding of factors influencing disease onset and advancement.
  • This approach can be applied to other diseases with longitudinal biomarker data.