TY - JOUR
T1 - Identifying maternal and infant factors associated with newborn size in rural Bangladesh by partial least squares (PLS) regression analysis
AU - Kabir, Alamgir
AU - Rahman, Jahanur
AU - Shamim, Abu Ahmed
AU - Klemm, Rolf D.W.
AU - Labrique, Alain B.
AU - Rashid, Mahbubur
AU - Christian, Parul
AU - West, Keith P.
N1 - Funding Information:
Supported by a grant from the Bill and Melinda Gates Foundation, Seattle, WA Global Control of Micronutrient Deficiency (Grant GH614); Micronutrients for Health Cooperative Agreement (HRN-A-00-97-00015); Global Research Activity Cooperative Agreement (GHS-A-00-03-00019-00) between Johns Hopkins University and the Office of Health, Infectious Diseases and Nutrition; and the US Agency for International Development, Washington, DC. Additional director in kind support was provided by the Sight and Life Research Institute (Baltimore, MD); Nutrilite Health Institute (Nutrilite Division, Access Business Group, LLC, Buena Park, CA); the Canadian International Development Agency (CIDA), Ottawa, Canada; and the National Integrated Population and Health Program of the Ministry of Health and Family Welfare of the Government of the People’s Republic of Bangladesh. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. First of all we would like to express our sincere gratitude to deceased Professor Mohammed Nasser (Department of Statistics, University of Rajshahi) for his continuous support until his death to accomplsh this study. He guided us in every aspects of this research. We gratefully acknowledge the contribution of the JiVitA study team comprised more than 800 Bangladeshi staff who carried out or supervised data collection, entry, and management and provided logistical, mapping, laboratory, and administrative support. We also acknowledge Angela KC who supported us in English editing and the Department of Statistics, University of Rajshahi whose faculties provided expert comments during the development of manuscript.
Publisher Copyright:
© 2017 Kabir et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2017/12
Y1 - 2017/12
N2 - 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<0.001) associated with newborn size. Among them, preterm delivery had the largest negative influence on newborn size (Standardized β = -0.29 − -0.19; p<0.001). Scatter plots of the scores of first two PLS components also revealed an interaction between newborn sex and preterm delivery on birth size. PLS regression was found to be more parsimonious than both ordinary least squares regression and principal component regression. It also provided more stable estimates than the ordinary least squares regression and provided the effect measure of the covariates with greater accuracy as it accounts for the correlation among the covariates and outcomes. Therefore, PLS regression is recommended when either there are multiple outcome measurements in the same study, or the covariates are correlated, or both situations exist in a dataset.
AB - 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<0.001) associated with newborn size. Among them, preterm delivery had the largest negative influence on newborn size (Standardized β = -0.29 − -0.19; p<0.001). Scatter plots of the scores of first two PLS components also revealed an interaction between newborn sex and preterm delivery on birth size. PLS regression was found to be more parsimonious than both ordinary least squares regression and principal component regression. It also provided more stable estimates than the ordinary least squares regression and provided the effect measure of the covariates with greater accuracy as it accounts for the correlation among the covariates and outcomes. Therefore, PLS regression is recommended when either there are multiple outcome measurements in the same study, or the covariates are correlated, or both situations exist in a dataset.
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U2 - 10.1371/journal.pone.0189677
DO - 10.1371/journal.pone.0189677
M3 - Article
C2 - 29261760
AN - SCOPUS:85038822243
SN - 1932-6203
VL - 12
JO - PloS one
JF - PloS one
IS - 12
M1 - e0189677
ER -