IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v43y2016i15p2729-2739.html
   My bibliography  Save this article

Longitudinal data analysis of mean passage time among malnutrition states: an application of Markov chains

Author

Listed:
  • Visalakshi Jeyaseelan
  • Tunny Sebastian
  • Jeyaseelan Lakshmanan
  • Shrikant I Bangdiwala

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.

Suggested Citation

  • Visalakshi Jeyaseelan & Tunny Sebastian & Jeyaseelan Lakshmanan & Shrikant I Bangdiwala, 2016. "Longitudinal data analysis of mean passage time among malnutrition states: an application of Markov chains," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(15), pages 2729-2739, November.
    • Handle: RePEc:taf:japsta:v:43:y:2016:i:15:p:2729-2739
    DOI: 10.1080/02664763.2016.1143454
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2016.1143454
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664763.2016.1143454?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. J.Robert Beck & Stephen G. Pauker, 1983. "The Markov Process in Medical Prognosis," Medical Decision Making, , vol. 3(4), pages 419-458, December.
    2. Nicky J. Welton & A. E. Ades, 2005. "Estimation of Markov Chain Transition Probabilities and Rates from Fully and Partially Observed Data: Uncertainty Propagation, Evidence Synthesis, and Model Calibration," Medical Decision Making, , vol. 25(6), pages 633-645, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Steven M. Shechter & Matthew D. Bailey & Andrew J. Schaefer & Mark S. Roberts, 2008. "The Optimal Time to Initiate HIV Therapy Under Ordered Health States," Operations Research, INFORMS, vol. 56(1), pages 20-33, February.
    2. repec:jss:jstsof:38:i08 is not listed on IDEAS
    3. Tushar Srivastava & Nicholas R. Latimer & Paul Tappenden, 2021. "Estimation of Transition Probabilities for State-Transition Models: A Review of NICE Appraisals," PharmacoEconomics, Springer, vol. 39(8), pages 869-878, August.
    4. Villacorta, Pablo J. & Verdegay, José L., 2016. "FuzzyStatProb: An R Package for the Estimation of Fuzzy Stationary Probabilities from a Sequence of Observations of an Unknown Markov Chain," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 71(i08).
    5. Karina Hansen & Christophe Lançon & Mondher Toumi, 2006. "Pharmacoeconomic modelling in schizophrenia," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 7(1), pages 19-29, March.
    6. Fernando Alarid-Escudero & Eline Krijkamp & Eva A. Enns & Alan Yang & M. G. Myriam Hunink & Petros Pechlivanoglou & Hawre Jalal, 2023. "A Tutorial on Time-Dependent Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example," Medical Decision Making, , vol. 43(1), pages 21-41, January.
    7. Donna M. Edwards & Ross D. Shachter & Douglas K. Owens, 1998. "A Dynamic HIV-Transmission Model for Evaluating the Costs and Benefits of Vaccine Programs," Interfaces, INFORMS, vol. 28(3), pages 144-166, June.
    8. de Wit, G.Ardine & Ramsteijn, Paul G & de Charro, Frank Th, 1998. "Economic evaluation of end stage renal disease treatment," Health Policy, Elsevier, vol. 44(3), pages 215-232, June.
    9. João Costa Freitas & Alberto Adrego Pinto & Óscar Felgueiras, 2024. "Game Theory for Predicting Stocks’ Closing Prices," Mathematics, MDPI, vol. 12(17), pages 1-49, August.
    10. Lukas J.A. Stalpers & Hans J.M. Van Gasteren & Willem A.J. Van Daal, 1989. "DEALE-ing with Life Expectancy and Mortality Rates," Medical Decision Making, , vol. 9(2), pages 150-152, June.
    11. Bruce A. Craig & Peter P. Sendi, 2002. "Estimation of the transition matrix of a discrete‐time Markov chain," Health Economics, John Wiley & Sons, Ltd., vol. 11(1), pages 33-42, January.
    12. Gordon B. Hazen, 2022. "Augmenting Markov Cohort Analysis to Compute (Co)Variances: Implications for Strength of Cost-Effectiveness," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 3170-3180, November.
    13. Franck Maunoury & Anastasiia Motrunich & Maria Palka-Santini & Stéphanie F Bernatchez & Stéphane Ruckly & Jean-François Timsit, 2015. "Cost-Effectiveness Analysis of a Transparent Antimicrobial Dressing for Managing Central Venous and Arterial Catheters in Intensive Care Units," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-14, June.
    14. Rowan Iskandar, 2018. "A theoretical foundation for state-transition cohort models in health decision analysis," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-11, December.
    15. Aslam Anis & Huiying Sun & Sonia Singh & John Woolcott & Bohdan Nosyk & Marc Brisson, 2006. "A Cost-Utility Analysis of Losartan versus Atenolol in the Treatment of Hypertension with Left Ventricular Hypertrophy," PharmacoEconomics, Springer, vol. 24(4), pages 387-400, April.
    16. Sixten Borg & Ulf Persson & Tine Jess & Ole Østergaard Thomsen & Tryggve Ljung & Lene Riis & Pia Munkholm, 2010. "A Maximum Likelihood Estimator of a Markov Model for Disease Activity in Crohn’s Disease and Ulcerative Colitis for Annually Aggregated Partial Observations," Medical Decision Making, , vol. 30(1), pages 132-142, January.
    17. Rachael Fleurence & Christopher Hollenbeak, 2007. "Rates and Probabilities in Economic Modelling," PharmacoEconomics, Springer, vol. 25(1), pages 3-6, January.
    18. C. Kent Kwoh & J. Robert Beck & Stephen G. Pauker, 1984. "Repeated Syncope with Negative Diagnostic Evaluation," Medical Decision Making, , vol. 4(3), pages 351-377, August.
    19. C. Armero & G. García‐Donato & A. López‐Quílez, 2010. "Bayesian methods in cost–effectiveness studies: objectivity, computation and other relevant aspects," Health Economics, John Wiley & Sons, Ltd., vol. 19(6), pages 629-643, June.
    20. Marta Soares & Luísa Canto e Castro, 2012. "Continuous Time Simulation and Discretized Models for Cost-Effectiveness Analysis," PharmacoEconomics, Springer, vol. 30(12), pages 1101-1117, December.
    21. Beate Jahn & Sarah Friedrich & Joachim Behnke & Joachim Engel & Ursula Garczarek & Ralf Münnich & Markus Pauly & Adalbert Wilhelm & Olaf Wolkenhauer & Markus Zwick & Uwe Siebert & Tim Friede, 2022. "On the role of data, statistics and decisions in a pandemic," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(3), pages 349-382, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:japsta:v:43:y:2016:i:15:p:2729-2739. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.