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An algorithm using the Interactive Scientific Processor is proposed for estimating parameters of the multi-state model for reverse and repeated transitions. The algorithm was applied to longitudinal data on diabetes mellitus collected at a diabetes hospital in Bangladesh. On the basis of blood glucose level, three distinct types of transitions from one state to another were detected. These are Transition, Reverse Transition, and Repeated Transition. Four variables are included in the models for the transitions: Sex, Body Mass Index (BMI), Age, and Area. For transition from state 2 to state 1, only sex has a significant association, indicating a higher rate of transition for male patients than that of female patients (p-value < 0.05). The variable BMI is significantly associated (p-value < 0.05) with the blood glucose level, implying that a lower BMI accelerates transition from a lower level of blood glucose to a higher level (p-value < 0.05).
