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ARENA 14.5 is used to generate the remaining life and usage of remanufactured item at failure, using a bivariate random number generator and time history of replacements under warranty and repeat sales over the simulation time interval.
Studies were combined using a bivariate random effects model.
We analysed the data at the patient level using a bivariate random effects regression model [ 15].
The differences in sensitivities and specificities were tested using a bivariate random effect model.
All included studies were combined using a bivariate random effects model to generate pooled sensitivity and specificity of STRATIFY at ≥2 points.
All included studies were combined using a bivariate random effects model to generate pooled estimates of sensitivity and specificity at ≥13.5 seconds.
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We performed the majority of analyses using a bivariate random-effects model against culture-based DST as the reference standard.
Using a bivariate random-effects model, water quality was found to be significantly worse in HSW compared with the source.
We carried out meta-analyses to estimate the pooled sensitivity and specificity of Xpert separately for TB detection and rifampicin resistance detection using a bivariate random-effects model.
We conducted a systematic review and meta-analysis to calculate the diagnostic accuracy of various NITs using a bivariate random-effects model.
Where there was a strong and statistically significant prognostic association between a test and an outcome measure (defined by an OR > 5, with a 95% CI > 1), we went on to calculate sensitivity, specificity, and likelihood ratios, again using data from the 2 × 2 tables and synthesising predictive measures using a bivariate random-effects meta-analysis model.
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