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Longitudinal Research02:20

Longitudinal Research

13.7K
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...
13.7K
Longitudinal Studies01:26

Longitudinal Studies

674
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...
674
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

718
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...
718
Applications of Life Tables01:22

Applications of Life Tables

416
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...
416
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

968
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...
968
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.3K
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...
1.3K

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Updated: Apr 4, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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Longitudinal Mixed Membership Trajectory Models for Disability Survey Data.

Daniel Manrique-Vallier1

  • 1Department of Statistics, Indiana University.

The Annals of Applied Statistics
|September 1, 2015
PubMed
Summary
This summary is machine-generated.

New methods analyze longitudinal disability data. Findings show most elderly experience late-onset disability, with younger generations developing it later than older ones.

Keywords:
Cohort analysisMCMCMixed MembershipMultivariate analysisNLTCSTrajectories

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Area of Science:

  • Gerontology
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Functional disability in the U.S. elderly population is a significant public health concern.
  • Understanding the progression and generational differences in disability onset is crucial for effective long-term care planning.
  • Existing methods may not fully capture the complexity of individual disability trajectories over time.

Purpose of the Study:

  • To develop novel analytical methods for discrete multivariate longitudinal data.
  • To apply these methods to analyze functional disability trends in the U.S. elderly.
  • To investigate inter-generational changes in disability onset using birth-cohort effects.

Main Methods:

  • Development of a mixed membership framework allowing individuals multiple memberships in extreme profiles.
  • Modeling of time-dependent trajectories of progression into disability.
  • Extension of models to incorporate birth-cohort effects for inter-generational analysis.

Main Results:

  • Most individuals exhibit disability trajectories characterized by a late onset.
  • Younger birth cohorts demonstrate a tendency to develop disabilities at a later life stage compared to older cohorts.
  • The analysis provides insights into the evolving patterns of functional disability across generations.

Conclusions:

  • The developed methods offer a robust approach to analyzing complex longitudinal disability data.
  • Findings suggest a shift towards later-onset disability in more recent birth cohorts.
  • This research has implications for public health policy and long-term care strategies for aging populations.