Bayesian meta‐analysis of test accuracy in the absence of a perfect reference test applied to bone scintigraphy for the diagnosis of complex regional pain syndrome. Issue 6 (19th October 2015)

• Record Type: Journal Article
• Authors: Held, Ulrike; Brunner, Florian; Steurer, Johann; Wertli, Maria M.
• Is Part Of: Biometrical journal. Volume 57:Issue 6(2015:Nov.)
• Page Start: 1020
• Page End: 1037
• Publication Date: 2015-10-19
• Subjects: Biometry -- Periodicals; Medical statistics -- Periodicals; 570.15195
• DOI: 10.1002/bimj.201400155
• Languages: English
• ISSNs: 0323-3847

Abstract

<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <p>There is conflicting evidence about the accuracy of bone scintigraphy (BS) for the diagnosis of complex regional pain syndrome 1 (CRPS 1). In a meta‐analysis of diagnostic studies, the evaluation of test accuracy is impeded by the use of different imperfect reference tests. The aim of our study is to summarize sensitivity and specificity of BS for CRPS 1 and to identify factors to explain heterogeneity. We use a hierarchical Bayesian approach to model test accuracy and threshold, and we present different models accounting for the imperfect nature of the reference tests, and assuming conditional dependence between BS and the reference test results. Further, we include disease duration as explanatory variable in the model. The models are compared using summary ROC curves and the deviance information criterion (DIC). Our results show that those models which account for different imperfect reference tests with conditional dependence and inclusion of the covariate are the ones with the smallest DIC. The sensitivity of BS was 0.87 (95% credible interval 0.73–0.97) and the overall specificity was 0.87 (0.73–0.95) in the model with the smallest DIC, in which missing values of the covariate are imputed within the Bayesian framework. The estimated effect of duration of symptoms on the threshold parameter was 0.17 (−0.25 to 0.57). We demonstrate that the Bayesian models presented in this paper are useful to address typical problems occurring in meta‐analysis of diagnostic studies, including conditional dependence between index test and reference test, as well as missing values in the study‐specific covariates.</p> </abstract>