Synthesis Analysis  

Synthesis Analysis is a statistical procedure used to combine regression coefficients for a common outcome or disease, which have been extracted from independent research studies. BioSignia has applied Synthesis Analysis statistics to the development of evidence-based disease risk models and has improved existing disease risk models with recent epidemiological findings.

Synthesis Analysis is different in purpose from meta-analysis, in that meta-analysis can only combine similar findings from independent studies if the variables are the same. Meta-analysis summarizes from many research studies the effect of a single risk factor on a specific disease.

Synthesis Analysis combines the research on multiple risk factors for a single disease. For example, many studies have shown that high blood pressure (hypertension) can lead to heart attack. A meta-analysis might combine all the relevant research on the effect of hypertension for heart disease and may conclude that hypertension increased the risk of heart attack four-fold. Another meta-analysis might summarize all of the studies on the effect that smoking has on heart disease. They in turn might conclude that smokers have a three-fold increased risk of heart disease.

Synthesis Analysis, on the other hand, combines risk factors such as hypertension and smoking into one multiple risk factor equation for heart disease. Synthesis Analysis builds on meta-analyses. The problem of combining separate risk factors is that people who have one risk factor may also have another. In the example above, smokers tend to have higher blood pressure.

If these factors were combined without accounting for this overlap the assessment of risk from the combined factors would be overstated. It might be concluded that smokers with hypertension have a twelve-fold increased risk (3x4=12) over non-smokers with normal blood pressure. In actuality, the risk for this group is most likely closer to six or seven. Synthesis Analysis adjusts for the collinearity (overlap) of risk factors so that they can be combined without overestimation of risk.

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