Longitudinal data analysis of mean passage time among malnutrition states: an application of Markov chains. Issue 15 (17th November 2016)

• Record Type: Journal Article
• Authors: Jeyaseelan, Visalakshi; Sebastian, Tunny; Lakshmanan, Jeyaseelan; Bangdiwala, Shrikant I
• Is Part Of: Journal of applied statistics. Volume 43:Issue 15(2016)
• Page Start: 2729
• Page End: 2739
• Publication Date: 2016-11-17
• Subjects: Longitudinal data -- mean first passage time -- Monte Carlo simulation -- Markov regression -- malnutrition; 60J05 -- 60J28 -- 62M02 -- 62M05 -- 62P10; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2016.1143454
• Languages: English
• ISSNs: 0266-4763

Abstract

ABSTRACT: Clinical prognosis of patients can be best described from a longitudinal study and a Markov regression model is an appropriate way of analyzing the prognosis of disease when the outcomes are serially dependent. Mean first passage time (MFPT) is a method to estimate the average number of transitions between the states of a Markov chain. The present study used the secondary data from a longitudinal study which was done during 1982–1986. This study was to illustrate the MFPT among the states of malnutrition, which were classified as Normal, Mild/Moderate and Severe among children aged 5–7 years, in South India. The 95% confidence interval (CI) for the MFPT was calculated using Monte Carlo simulation. Markov regression models were used to test for the association of state transitions across the risk factors. The average time taken for an underweight child to transit from Severe state of malnutrition to become Normal was nearly 2.73 (95% CI 2.60–2.86) years and 3.41 (95% CI 3.25–3.58) years in Rural area and 2.31(95% CI 2.20–2.42) in Urban area. The significant difference between the MFPT for some risk factors are useful to plan interventions. It will especially be useful to find the impact of duration among school-going children on their cognitive disorders.