Reanalysis of CVD Risk Factors Via Likelihood Methods
Primary Purpose
Cardiovascular Diseases, Heart Diseases, Atherosclerosis
Status
Completed
Phase
Locations
Study Type
Observational
Intervention
Sponsored by
About this trial
This is an observational trial for Cardiovascular Diseases
Eligibility Criteria
No eligibility criteria
Sites / Locations
Outcomes
Primary Outcome Measures
Secondary Outcome Measures
Full Information
NCT ID
NCT00005408
First Posted
May 25, 2000
Last Updated
March 15, 2016
Sponsor
National Heart, Lung, and Blood Institute (NHLBI)
1. Study Identification
Unique Protocol Identification Number
NCT00005408
Brief Title
Reanalysis of CVD Risk Factors Via Likelihood Methods
Study Type
Observational
2. Study Status
Record Verification Date
May 2000
Overall Recruitment Status
Completed
Study Start Date
July 1992 (undefined)
Primary Completion Date
undefined (undefined)
Study Completion Date
April 1994 (Actual)
3. Sponsor/Collaborators
Name of the Sponsor
National Heart, Lung, and Blood Institute (NHLBI)
4. Oversight
5. Study Description
Brief Summary
To reanalyze data on risk factors for cardiovascular disease (CVD) including total cholesterol and high density lipoprotein (HDL) cholesterol for the subjects in the first, second, and third exams of the NHLBI Twin Study.
Detailed Description
BACKGROUND:
The results of these longitudinal analyses yielded new insights on genetic effects affecting CVD risk factors during the aging process.
DESIGN NARRATIVE:
The analyses utilized maximum likelihood estimators of genetic variance which were asymptotically more efficient than the method-of-moments estimators used in previous analyses. The models used incorporated terms to partition the variance in a trait from twin data into either i) additive genetic variance and unshared environmental variance (the AE model), ii) additive genetic variance, dominance genetic variance, and unshared environmental variance (the ADE model), or iii) additive genetic variance, shared environmental variance, and unshared environmental variance (the ACE model). The AE, ADE, and ACE models were fitted separately to data from each of the three exams to obtain a cross-sectional analysis. The investigators also extended these models for use with longitudinal data by incorporating terms to represent the covariance of variance components from different exams.
Two important additional objectives of this study were i) to introduce resistant estimation techniques in twin modeling, which trimmed the effect of outlier data points smoothly, and ii) to carefully study the performance of maximum likelihood and method-of-moments estimators when assumptions of the twin model were violated. The results of these parts of the study should yield a more complete understanding of the relative merits and limitations of twin modeling procedures.
The study completion date listed in this record was obtained from the "End Date" entered in the Protocol Registration and Results System (PRS) record.
6. Conditions and Keywords
Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Cardiovascular Diseases, Heart Diseases, Atherosclerosis
7. Study Design
10. Eligibility
Sex
Male
Maximum Age & Unit of Time
100 Years
Accepts Healthy Volunteers
No
Eligibility Criteria
No eligibility criteria
12. IPD Sharing Statement
Citations:
PubMed Identifier
8314059
Citation
Williams CJ, Wijesiri UW. Lipid data from NHLBI veteran twins: interpreting genetic analyses when model assumptions fail. Genet Epidemiol. 1993;10(6):551-6. doi: 10.1002/gepi.1370100637.
Results Reference
background
PubMed Identifier
8369388
Citation
Williams CJ. On the covariance between parameter estimates in models of twin data. Biometrics. 1993 Jun;49(2):557-68.
Results Reference
background
PubMed Identifier
7598664
Citation
Wijesiri UW, Williams CJ. Approximate solutions for the maximum-likelihood estimates in models of univariate human twin data. Behav Genet. 1995 May;25(3):211-6. doi: 10.1007/BF02197179. Erratum In: Behav Genet 1997 Jan;27(1):83.
Results Reference
background
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Reanalysis of CVD Risk Factors Via Likelihood Methods
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