Cubic Spline Regression: An Application to Early Bipolar Disorder Dynamics

Abstract

Owing to the fact that the major challenge of predicting the risk of having bipolar is the absence of a gold standard to distinguish between true cases and false positive; this study employed the extension of cubic spline function to the multinomial model to explore the risk tendency of unnoticed early bipolar across three different groups of mood disorder. The intermediate group was used to accommodate for false negative and false positive while mapping the true value of bipolar risk tendency across the three groups to a scale. Hence for all distributions of “yes” ticked in a mood disorder questionnaire, the study predicts the bipolar risk tendency while simultaneously accommodating for the patients response bias. The coefficients of the polynomial are obtained using the maximum likelihood method. The spline graph reveals how bipolar disorder build up slowly and lingers in the body for long without been noticed due to fluctuations in risk tendency of the mood scores.