When multivariable relationships were assessed between lnOLA and the explanatory variables parity and calf birth weight, only the latter remained significantly (P = 0.02) associated with lnOLA.
Univariate relationships were analyzed using chi-square tests and multivariable relationships were evaluated by taking the final logistic regression model obtained above and replacing the summary measure of cognitive impairment with each of the individual components of the six-item screener.
When multivariable relationships were assessed between lnDO and the explanatory variables, the variables luteal phase pattern, lnOLA, calf 200-d weight, calf birth weight categorised by its quartiles and parity remained in the model after the backwards selection procedure had been employed (Table 4).
All multivariable exposure outcome relationships were monotonic and approximately linear on the natural log scale over the central part of the exposure distribution (see Supplemental Material, Figures S1 S4).
The bivariate statistical association during the early course of illness between insight, explanatory models and outcome, lost their statistical significance when baseline and clinical variables were included in the multivariable analysis arguing that such relationships are confounded by illness characteristics.
However, some statistical methods, such as exploratory and confirmatory factor analyses, are mandatory for exploring the underlying multivariable relationships and could be helpful in building up a picture of what is really being measured.
Based on characterization of the plantation sites and measured noise attenuation, a multivariable linear relationship was established for excess noise attenuation.
After multivariable-adjustment most of these relationships were not significant, and age was the most influential variable in reducing the association magnitudes.
Predicted relationships were analyzed by multivariable regression modelling and causal mediation analysis.
Relationships were tested using multivariable logistic regression models controlling for healthcare access and demographic factors, including limited English proficiency (LEP).
