title: Building prediction models for coronary heart disease by synthesizing multiple longitudinal research findings.
abstract: 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.
authors.affiliation: BioSignia, Inc., Durham, NC 27713, USA.
authors.forename: Guizhou
authors.initials: G
authors.lastname: Hu
authors.forename: Martin M
authors.initials: MM
authors.lastname: Root
mesh_terms: 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