To be or not to be associated : power study of four statistical modeling approaches to identify parasite associations in cross-sectional studies
Citation
Vaumourin , E , Vourc'h , G , Telfer , S , Lambin , X , Salih , D , Seitzer , U , Morand , S , Charbonnel , N , Vayssier-Taussat , M & Gasqui , P 2014 , ' To be or not to be associated : power study of four statistical modeling approaches to identify parasite associations in cross-sectional studies ' , Frontiers in cellular and infection microbiology , vol. 4 , 62 . https://doi.org/10.3389/fcimb.2014.00062
Rights
Copyright © 2014 Vaumourin, Vourc'h, Telfer, Lambin, Salih, Seitzer, Morand, Charbonnel, Vayssier-Taussat and Gasqui. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. http://creativecommons.org/licenses/by/3.0/