Identifying determinants and estimating the risk of inadequate and excess gestational weight gain using a multinomial logistic regression model

When there are three or more nominal categories of a response variable, the binomial logistic regression approach is widely used to model the relationships of exposure variables with different binomial responses one at a time. However, some of the separate binomial comparisons would be redundant. This approach is also suboptimal because of the loss of information that will result when only a subset of the data is analyzed at a time and the multiple testing problems arising from analysis of several pairs of categories. These drawbacks of fitting separate binomial regression models to a multicategory nominal outcome variable can be overcome using a single multinomial regression modeling framework. In this study, we compared the results using a multinomial regression with the separate two binomial regressions to determine factors associated with excess and inadequate weight gain during pregnancy in a data set from a gestational weight gain study involving a cross-sectional survey of 312 women with singleton pregnancies. We found that both approaches identified the same set of predictors, ie, higher neuroticism, planning to gain more weight than the recommended level, and bedtime television watching, with P-values ≤0.05 of the excessive (versus appropriate) weight gain, for which the subgroup size was moderate. The final list of significant predictors of inadequate (versus appropriate) weight gain identified by multinomial regression were planned weight gain below the recommended range, overweight or obese women, and bedtime television watching, while those by a separate binomial approach were self-efficacy towards achieving healthy weight, lack of weight satisfaction, and bedtime television watching, which differed between the two approaches where the final set of predictors were identified by a variable selection process and the comparisons were made in a small subgroup. A multinomial approach is a useful analytical framework that researchers may consider when they have multinomial response categories because this approach allows nonredundant comparisons to be made, avoiding the need to analyze a subset of the data one at a time and also allows for risk prediction of multinomial categories from a well validated multinomial model, and will not lead to multiple testing problems.