Model-Free Time Curves for Longitudinal Data Analysis
Primary Purpose
Cardiovascular Diseases, Heart Diseases
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
NCT00005457
First Posted
May 25, 2000
Last Updated
February 26, 2016
Sponsor
National Heart, Lung, and Blood Institute (NHLBI)
1. Study Identification
Unique Protocol Identification Number
NCT00005457
Brief Title
Model-Free Time Curves for Longitudinal Data Analysis
Study Type
Observational
2. Study Status
Record Verification Date
June 2000
Overall Recruitment Status
Completed
Study Start Date
January 1991 (undefined)
Primary Completion Date
undefined (undefined)
Study Completion Date
June 1994 (Actual)
3. Sponsor/Collaborators
Name of the Sponsor
National Heart, Lung, and Blood Institute (NHLBI)
4. Oversight
5. Study Description
Brief Summary
To enhance statistical methods for epidemiological studies by extending the Disturbed Highest Derivative Polynomial (DHDP) to models for binary-logistic and Poisson data and by including random subject effects in the Gaussian model.
Detailed Description
DESIGN NARRATIVE:
In previous work the investigator had developed the Disturbed Highest Derivative Polynomial (DHDP) as a model-free time curve and had published the theoretical development for its use as the overall time curve in a linear Gaussian model for longitudinal data with fixed covariate effects and autocorrelated errors but without subject effects. For the logistic model, the DHDP would replace the constant which appeared in the log odds in the non-longitudinal case. The first-order DHDP was a straight line whose slope received random disturbances over time. As such, it was capable of fitting a rich variety of arbitrarily changing time curves. The second-order DHDP would generally provide a fit with smaller high frequency variation. There are a number of longitudinal data analysis methods currently available for Gaussian and binary-logistic data. They all have in common the requirement to explicitly model the overall time curve--usually by a low order deterministic polynomial. The main significance of this proposal was to represent the overall time curve by a DHDP, thereby allowing the possibility for fitting arbitrarily changing time curves without explicitly modeling the form of the change over time. The order of the DHDP can be selected by a modification of the Akaike Information Criterion. The Poisson model should be useful in fitting the periodic reported incidence of a rare disease. The relationship of the DHDP to the Smoothing Polynomial Spline (SPS) was shown and methods were developed for using a SPS instead of a DHDP in analysis. Robustness of the methods were examined by computer simulation studies which evaluated and compared the ability of the DHDP and SPS models to estimate covariate effects and time curves when the time curves were generated by processes other than DHDP.
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
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
5397071
Citation
Cioffi FA, Giammusso V. [Glucocorticoid therapy in head injuries]. Rass Int Clin Ter. 1969 Mar 31;49(6):370-7. No abstract available. Italian.
Results Reference
background
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Model-Free Time Curves for Longitudinal Data Analysis
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