Multiple covariate analysis, such as patient characteristics and procedural techniques, has been used to identify risk factors [ 6– 7].
However, in this study, in multiple regression analysis, such an association was not found, possibly because of the limited number of patients who smoke or have diabetes or anti-phospholipid antibodies.
Both quantitative and qualitative methods are employed by investigators, and findings are often explicitly conceptualized and presented at multiple levels of analysis, such as community, the service system, the organization, the group, the family, and the individual.
In the multiple linear regression analysis such variables as age, smoking status, BMI SBP, log HOMA-IR, log CRP, log TG, and TC/HDL-C ratio (PRE) or HDL-C (POST) were assayed as independent variables, whereas log A-FABP was assessed as a dependent variable.
Since there is a lack of suitable sensitivity analysis procedures for decomposition analysis, such as multiple imputation, we are, however, unable to show the true impact of this imbalance.
Recently, more efficient and less biased alternatives to complete case analysis, such as multiple imputation or inverse probability weighting, have been developed and included in statistical software packages, thus enabling standardized imputation of missing data on stage in cancer studies.
The difficulty in identifying unique genomic probes from the breakpoint region precluded additional analysis such as multiple color interphase FISH to delineate the order of BAC clones within the BP1 region.
There are multiple analysis techniques that deal with such problems, more specifically with system capacity analysis.
In principle, a common majority-rule consensus can then be computed to summarize the results of such a "multiple analysis" [ 33], as discussed in Farris et al. [ 34].
The investigation of multiple outcomes and multiple models is motivated in an etiological analysis such as the present, but may increase the risk of type-I error.
