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Identifying maternal and infant factors associated with newborn size in rural Bangladesh by partial least squares (PLS) regression analysis

Author

Listed:
  • Alamgir Kabir
  • Md Jahanur Rahman
  • Abu Ahmed Shamim
  • Rolf D W Klemm
  • Alain B Labrique
  • Mahbubur Rashid
  • Parul Christian
  • Keith P West Jr.

Abstract

Birth weight, length and circumferences of the head, chest and arm are key measures of newborn size and health in developing countries. We assessed maternal socio-demographic factors associated with multiple measures of newborn size in a large rural population in Bangladesh using partial least squares (PLS) regression method. PLS regression, combining features from principal component analysis and multiple linear regression, is a multivariate technique with an ability to handle multicollinearity while simultaneously handling multiple dependent variables. We analyzed maternal and infant data from singletons (n = 14,506) born during a double-masked, cluster-randomized, placebo-controlled maternal vitamin A or β-carotene supplementation trial in rural northwest Bangladesh. PLS regression results identified numerous maternal factors (parity, age, early pregnancy MUAC, living standard index, years of education, number of antenatal care visits, preterm delivery and infant sex) significantly (p

Suggested Citation

  • Alamgir Kabir & Md Jahanur Rahman & Abu Ahmed Shamim & Rolf D W Klemm & Alain B Labrique & Mahbubur Rashid & Parul Christian & Keith P West Jr., 2017. "Identifying maternal and infant factors associated with newborn size in rural Bangladesh by partial least squares (PLS) regression analysis," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-16, December.
    • Handle: RePEc:plo:pone00:0189677
    DOI: 10.1371/journal.pone.0189677
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    References listed on IDEAS

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    1. Mevik, Björn-Helge & Wehrens, Ron, 2007. "The pls Package: Principal Component and Partial Least Squares Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 18(i02).
    2. Ian T. Jolliffe, 1982. "A Note on the Use of Principal Components in Regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 31(3), pages 300-303, November.
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    2. Eshetu Shifaw & Jinming Sha & Xiaomei Li & Shang Jiali & Zhongcong Bao, 2020. "Remote sensing and GIS-based analysis of urban dynamics and modelling of its drivers, the case of Pingtan, China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(3), pages 2159-2186, March.

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