Computational protocol: Xpert® Mtb/Rif assay for pulmonary tuberculosis and rifampicin resistance in adults

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[…] We performed descriptive analyses for the results of the included studies using SPSS and present key study characteristics in Characteristics of included studies. We used data reported in the two-by-two tables to calculate sensitivity and specificity estimates and 95% confidence intervals (CI) for individual studies and to generate forest plots using Review . We chose to use data that were not subject to discrepant analyses (ie unresolved data), since resolved data after discrepant analyses are a potential for risk of bias (Hadgu ).We carried out meta-analyses to estimate the pooled sensitivity and specificity of Xpert separately for TB detection (I. A. and I. B) and rifampicin resistance detection (II. A.). We determined pooled estimates using an adaptation of the bivariate random-effects model (Reitsma ) to allow for a hierarchical structure for the two multicentre studies (Boehme ; Boehme ). The bivariate random-effects approach allowed us to calculate the pooled estimates of sensitivity and specificity while dealing with potential sources of variation caused by (1) imprecision of sensitivity and specificity estimates within individual studies; (2) correlation between sensitivity and specificity across studies; and (3) variation in sensitivity and specificity between studies.We estimated all models using a Bayesian approach with non-subjective prior distributions and implemented using WinBUGS (Version 1.4.3) (Lunn ). Under the Bayesian approach, all unknown parameters must be provided a prior distribution that defines the range of possible values of the parameter and the likelihood of each of those values based on information external to the data. In order to let the observed data determine the final results, we chose to use low-information prior distributions over the pooled sensitivity and specificity parameters and their between-study standard deviation parameters. The model we used is summarized in the Statistical Appendix together with the WinBUGS program used to implement it (). Information from the prior distribution is combined with the likelihood of the observed data in accordance with Bayes Theorem to obtain a posterior distribution for each unknown parameter.Using a sample from the posterior distribution we can obtain various descriptive statistics of interest. We estimated the median pooled sensitivity and specificity and their 95% credible intervals (CrI).The median or the 50% quantile is the value below which 50% of the posterior sample lies. We chose to report the median because the posterior distributions of some parameters may be skewed and the median would be considered a better point estimate of the unknown parameter than the mean in such cases. The 95% CrI is the Bayesian equivalent of the classical (frequentist) 95% CI. (We have indicated 95% CI for individual study estimates and 95% CrI for pooled study estimates as appropriate).The 95% CrI may be interpreted as an interval that has a 95% probability of capturing the true value of the unknown parameter given the observed data and the prior information. We also extracted estimates of the 'predicted' sensitivity and specificity in a future study together with their 95% CrIs. The predicted value gives an idea of heterogeneity at the study level. We can compare the predicted intervals to the pooled intervals to get an idea of the heterogeneity. With a large number of studies, the pooled interval may be narrow. However, if there is considerable variability in sensitivity and specificity estimates between studies, this variability will be reflected in a wide predicted interval despite the large number of studies. We generated the plots using R (version 2.15.1) (). […]

Pipeline specifications

Software tools SPSS, WinBUGS
Applications Drug design, Miscellaneous
Organisms Homo sapiens, Human immunodeficiency virus 2
Diseases Tuberculosis, Tuberculosis, Pulmonary, HIV Infections, Tuberculosis, Multidrug-Resistant
Chemicals Rifampin