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Related Concept Videos

Longitudinal Studies01:26

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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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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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Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
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In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...
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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Estimating peer effects in longitudinal dyadic data using instrumental variables.

A James O'Malley1, Felix Elwert2, J Niels Rosenquist3

  • 1The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire 03766, U.S.A.

Biometrics
|May 1, 2014
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Summary
This summary is machine-generated.

Researchers used genetic data as instrumental variables (IV) to overcome biases in observational studies and identify causal peer effects. This method successfully estimated the influence of body mass index (BMI) on friends and spouses.

Keywords:
Body‐mass indexCausalityDirected acyclic graphsDyadGenesHomophilyInstrumental variableLongitudinalMendelian randomizationPeer effectSocial networkTwo‐stage least squares

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Area of Science:

  • Social Sciences
  • Genetics
  • Epidemiology

Background:

  • Identifying causal peer effects from observational data is difficult due to latent homophily and unobserved confounding.
  • Social contagion and induction are key concepts in understanding trait and behavior transmission within networks.

Purpose of the Study:

  • To investigate the use of genes as instrumental variables (IV) for identifying causal peer effects in the presence of homophily and confounding.
  • To develop and apply novel IV strategies for robust causal inference in social networks.

Main Methods:

  • Utilized directed acyclic graphs (DAGs) to model complex data-generating processes.
  • Employed multiple instrumental variable (IV) strategies, including fixed genes/alleles and time-varying gene expression.
  • Addressed potential biases like pleiotropy, homophily across time and phenotypes, population stratification, and endogeneity.

Main Results:

  • A single fixed gene as an IV may fail if it influences past treatment values.
  • Multiple genes or time-varying gene expression, when instrumenting exclusion violations, can identify peer effects.
  • IV identification remains feasible despite numerous complexities previously thought to impede it.

Conclusions:

  • Genes can serve as powerful instrumental variables to identify causal peer effects in complex social network data.
  • The study successfully applied these methods to estimate body mass index (BMI) peer effects in the Framingham Heart Study.
  • Findings suggest a significant positive causal peer effect of BMI among friends.