Journal of Biostatistics and Epidemiology 2016. 2(2):81-87.

Multistate transition modeling: An application using hypertension data
Awol Seid


Background & Aim: Multistate models are systems of multivariate survival data where individuals move through a series of  istinct states following certain paths of possible transitions. Such models provide a relevant tool for studying observations of a continuous time process at arbitrary times. The aim of this study was to model the transitions from a healthy (hypertension free) state to an illness (hypertension) state of a hypertensive patient under treatment.
Methods & Materials: In this article, the application of multistate modeling using hypertension data is demonstrated. Hospital data were obtained for a cohort of 353 patients from Jimma University Hospital, Ethiopia.
Results: Three states of the Markov process are defined based on the WHO guideline of high blood pressure, state 1 (BP < 140/90 mmHg), state 2 (BP 140/90 mmHg) and state 3 (dropout). The first state is termed as a healthy state, the second an illness state and the third one is an absorbing state. Initially, the state transition intensities and state occupation probabilities are estimated with no covariate. Then, the effect of gender and family history of hypertension on the state transition intensities are evaluated separately and jointly using proportional intensities model.
Conclusion: The study indicates that gender has a significant effect on the transition intensities but not family history ofhypertension.


Hypertension; Multistate modeling; Markov process

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Awoke A, Awoke T, Alemu S, Megabiaw B. Prevalence and associated factors of hypertension among adults in Gondar, Northwest Ethiopia: a community based cross-sectional study. BMC Cardiovasc Disord 2012; 12: 113.

World Health Organization-international society of hypertension guidelines for the management of hypertension. Guidelines Subcommittee. J Hypertens 1999; 17(2): 151-83.

Borgan O, Anderson PK, Gill RD, Keiding N. Statistical models based on counting processes. Berlin, Germany: Springer; 1995.

Andersen PK, Keiding N. Multi-state models for event history analysis. Stat Methods Med Res 2002; 11(2): 91-115.

Cox DR. Regression models and life-tables. J R Stat Soc Series B 1972; 34(2): 187-220.

Cox DR. The statistical analysis of dependencies in point processes. In: Lewis PA, Editor. Stochastic point processes: statistical analysis, theory, and applications. New York, NY: Wiley-Interscience; 1972.

Klein JP, Keiding N, Copelan EA. Plotting summary predictions in multistate survival models: probabilities of relapse and death in remission for bone marrow transplantation patients. Stat Med 1993; 12(24): 2315-32.

Keiding N, Kvist K, Hartvig H, Tvede M, Juul S. Estimating time to pregnancy from current durations in a cross-sectional sample. Biostatistics 2002; 3(4): 565-78.

Putter H. Tutorial in biostatistics: Competing risks and multi-state models Analyses using the mstate package [Online]. [cited 2016]; Available from: URL: https://cran.rproject. org/web/packages/mstate/vignettes/Tu torial.pdf

Aguirre-Hernandez R, Farewell VT. A Pearson-type goodness-of-fit test for stationary and time-continuous Markov regression models. Stat Med 2002; 21(13): 1899-911.

Huszti E, Abrahamowicz M, Alioum A, Binquet C, Quantin C. Relative survival multistate Markov model. Stat Med 2012; 31(3): 269-86.

Saint-Pierre P, Combescure C, Daures JP, Godard P. The analysis of asthma control under a Markov assumption with use of covariates. Stat Med 2003; 22(24): 3755-70.

Minh HV, Byass P, Chuc NT, Wall S. Gender differences in prevalence and socioeconomic determinants of hypertension: findings from the WHO STEPs survey in a rural community of Vietnam. J Hum Hypertens 2006; 20(2): 109-15.


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