Multilevel Modeling of Blood Pressure Data in the Framingham Heart Study
Author Information
Author(s): Briollais Laurent, Tzontcheva Anjela, Bull Shelley
Primary Institution: Mount Sinai Hospital, Toronto, Ontario, Canada
Hypothesis
Can multilevel modeling effectively analyze longitudinal blood pressure data in familial studies?
Conclusion
Multilevel modeling is a powerful method for detecting genetic influences on blood pressure over time.
Supporting Evidence
- Evidence for linkage was found in several chromosomal regions.
- Multilevel modeling allowed for better partitioning of variability.
- Heritability estimates for mean SBP were around 54%.
Takeaway
This study used a special math method to look at blood pressure changes in families over time to find out how genes affect it.
Methodology
Multilevel modeling was used to analyze longitudinal systolic blood pressure data from the Framingham Heart Study.
Potential Biases
Potential bias due to treatment effects not being fully corrected.
Limitations
Linkage results were only found in selected samples, and the analysis may not account for all variability.
Participant Demographics
Participants included 4692 subjects from 330 pedigrees, aged between 25 and 75.
Statistical Information
P-Value
p<0.05
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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