Building prediction models for coronary heart disease by synthesizing multiple longitudinal research findings.
BACKGROUND
No methodology is currently available to allow the combining of individual risk factor information derived from different longitudinal studies for a chronic disease in a multivariate fashion. This paper introduces such a methodology, named Synthesis Analysis, which is essentially a multivariate meta-analytic technique.


METHODS
The construction and validation of statistical models using available data sets.


RESULTS
Two analyses are presented. (1) With the same data, Synthesis Analysis produced a similar prediction model to the conventional regression approach when using the same risk variables. Synthesis Analysis produced better prediction models when additional risk variables were added. (2) A four-variable empirical logistic model for death from coronary heart disease was developed with data from the Framingham Heart Study. A synthesized prediction model with five new variables added to this empirical model was developed using Synthesis Analysis and literature information. This model was then compared with the four-variable empirical model using the first National Health and Nutrition Examination Survey (NHANES I) Epidemiologic Follow-up Study data set. The synthesized model had significantly improved predictive power (chi = 43.8, P<0.00001).


CONCLUSIONS
Synthesis Analysis provides a new means of developing complex disease predictive models from the medical literature.
Hu
Guizhou
G
BioSignia, Inc., Durham, NC 27713, USA.
Root
Martin M
MM
D003327:Coronary Disease / Q000453:epidemiology*; D006801:Humans; D008137:Longitudinal Studies; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D009749:Nutrition Surveys; D011237:Predictive Value of Tests; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors