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Related Concept Videos

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...
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Data Collection by Observations

Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
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Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
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Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One example of...
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...
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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,...
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Censoring Survival Data

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 reasons...

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A method to estimate off-schedule observations in a longitudinal study.

Katherine W Reeves1, Roslyn A Stone, Francesmary Modugno

  • 1Department of Public Health, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA, USA. kwreeves@schoolph.umass.edu

Annals of Epidemiology
|March 8, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new method using local linear interpolation to estimate breast density measurements collected off-schedule in epidemiological studies. This approach offers more accurate results than simple matching for longitudinal data analysis.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Medical Imaging Analysis

Background:

  • Epidemiological studies often collect data at irregular intervals, leading to missing measurements at planned visits.
  • Longitudinal breast density data in the Study of Women's Health Across the Nation (SWAN) were collected retrospectively from off-schedule mammograms.
  • Accurate estimation of breast density at scheduled visits is crucial for understanding health trends.

Purpose of the Study:

  • To propose and evaluate a novel method for estimating off-schedule breast density measurements at planned study visit times.
  • To improve the accuracy of longitudinal data analysis in epidemiological studies with irregularly collected data.

Main Methods:

  • Utilized local linear interpolation to estimate missing breast density values.
  • Employed multiply imputed error terms from subject-specific normal distributions based on within-subject standard deviations.
  • Assessed the validity and implications of the proposed estimation approach.

Main Results:

  • The proposed method yielded a small average prediction error of 0.11 cm² for breast density.
  • Coefficients from random intercept models differed significantly when using the proposed method versus simple nearest-visit matching.
  • The association between BMI changes and dense breast area showed different results depending on the estimation method used.

Conclusions:

  • Local linear interpolation with multiple imputation provides more accurate breast density estimations than simple matching, as it accounts for temporal changes.
  • The proposed method is suitable for other epidemiological studies dealing with linearly changing variables collected off-schedule over short periods.
  • This approach enhances the reliability of findings from studies with non-ideal data collection schedules.