Data collected were recorded into a template developed in Microsoft Excel 2013 and later exported to SPSS version 20 (IBM SPSS Statistics 22; Armonk, NY) for statistical analysis. All differences were considered statistically significant at P-values < 0.05. Proportion of Mf positivity was expressed as a percentage of the number examined at different time points of the follow ups. Chi-square test was used to check for significant differences in the positive rates between the screening techniques at different screening time points.
A web-based application described by Lim et al. [49 (link)] and based on Bayesian Latent Class Models (LCM) was used to determine the accuracy (sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV)) of the diagnostic tests using microscopy as an imperfect gold standard with the help of a simplified interface of three-tests in one-population model (Walter and Irwig model) [49 (link)]. In brief, Bayesian LCMs estimate accuracies of diagnostic tests based on the true disease status of each patient. Bayesian LCMs do not assume that any diagnostic test or combination of diagnostic tests is perfect [50 (link), 51 ]. Table S1 shows the data input into the Web-based application template.
A web-based application described by Lim et al. [49 (link)] and based on Bayesian Latent Class Models (LCM) was used to determine the accuracy (sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV)) of the diagnostic tests using microscopy as an imperfect gold standard with the help of a simplified interface of three-tests in one-population model (Walter and Irwig model) [49 (link)]. In brief, Bayesian LCMs estimate accuracies of diagnostic tests based on the true disease status of each patient. Bayesian LCMs do not assume that any diagnostic test or combination of diagnostic tests is perfect [50 (link), 51 ]. Table S
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