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. DESIGN: The construction and validation of statistical models using available data sets. METHODS AND 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.
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. DESIGN: The construction and validation of statistical models using available data sets. METHODS AND 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: Ruth Q Wolever; Daniel M Webber; Justin P Meunier; Jeffrey M Greeson; Evangeline R Lausier; Tracy W Gaudet Journal: Altern Ther Health Med Date: 2011 Jul-Aug Impact factor: 1.305
Authors: Anke Bruninx; Bart Scheenstra; Andre Dekker; Jos Maessen; Arnoud van 't Hof; Bas Kietselaer; Iñigo Bermejo Journal: Prev Med Rep Date: 2021-12-16
Authors: Qin Zhu; Die Luo; Xiaojun Zhou; Xianxu Cai; Qi Li; Yuanan Lu; Jiayan Chen Journal: Int J Environ Res Public Health Date: 2021-06-18 Impact factor: 3.390