Estimating Growth Parameters Using a Random Coefficient Regression Model
Author Information
Author(s): Jonathan Corbett, Aldi Kraja, Ingrid B Borecki, Michael A Province
Primary Institution: Washington University School of Medicine
Hypothesis
Can a random coefficient regression model effectively estimate growth parameters for serum glucose levels over time?
Conclusion
The random coefficient regression model showed strong evidence for linkage at one locus affecting serum glucose levels.
Supporting Evidence
- The RCR model identified a significant linkage at a locus on chromosome 5 with a LOD score of 65.78.
- The two-point slope phenotype showed evidence for linkage at one locus on chromosome 5 with a LOD score of 4.16.
- Single-point phenotypes also provided significant evidence for linkage to chromosome 5 with LOD scores of 17.90 and 27.24.
Takeaway
Researchers used a special math model to see how glucose levels change over time, and they found a strong link to a specific gene.
Methodology
The study used a random coefficient regression model implemented in SAS to analyze serum glucose levels from simulated data.
Limitations
The study relied on simulated data, which may not fully represent real-world scenarios.
Statistical Information
P-Value
p<0.05
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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