TY - JOUR
T1 - Differences between univariate and bivariate models for summarizing diagnostic accuracy may not be large
AU - Simel, David L.
AU - Bossuyt, Patrick M. M.
PY - 2009
Y1 - 2009
N2 - Objective: Experts recommend random effects bivariate logitnormal sensitivity and specificity estimates, rather than directly summarized univariate likelihood ratios (LRs) for diagnostic test meta-analyses. We assessed whether bivariate measures might cause different clinical conclusions compared with those from simpler univariate measures. Study Design: From two articles that described the benefits of bivariate random effects measures, we reanalyzed results and compared outcomes to univariate random effects summary estimates of sensitivity, specificity, and LRs. We also reanalyzed data from two published clinical examination studies to assess differences in the two methods. Results: The median difference between bivariate and univariate methods for sensitivity was 1.5% (range: 0-6%) and for specificity was 1.5% (range: 0-4%). Using a pretest probability of 50%, the median difference in posterior probability was 2.5% (interquartile range: 2.2-3.2%, overall range: 0-11%). For sparse data, continuity adjustment affected the differences. Adding 0.5 to each cell of studies containing at least one cell with zero patients provided the most consistent result. Conclusions: Bivariate estimates of sensitivity and specificity generate summary LRs similar to those derived with univariate methods. Our empiric results suggest that recalculating LRs in published research will not likely create dramatic changes as a function of the random effects measure chosen. (C) 2009 Elsevier Inc. All rights reserved
AB - Objective: Experts recommend random effects bivariate logitnormal sensitivity and specificity estimates, rather than directly summarized univariate likelihood ratios (LRs) for diagnostic test meta-analyses. We assessed whether bivariate measures might cause different clinical conclusions compared with those from simpler univariate measures. Study Design: From two articles that described the benefits of bivariate random effects measures, we reanalyzed results and compared outcomes to univariate random effects summary estimates of sensitivity, specificity, and LRs. We also reanalyzed data from two published clinical examination studies to assess differences in the two methods. Results: The median difference between bivariate and univariate methods for sensitivity was 1.5% (range: 0-6%) and for specificity was 1.5% (range: 0-4%). Using a pretest probability of 50%, the median difference in posterior probability was 2.5% (interquartile range: 2.2-3.2%, overall range: 0-11%). For sparse data, continuity adjustment affected the differences. Adding 0.5 to each cell of studies containing at least one cell with zero patients provided the most consistent result. Conclusions: Bivariate estimates of sensitivity and specificity generate summary LRs similar to those derived with univariate methods. Our empiric results suggest that recalculating LRs in published research will not likely create dramatic changes as a function of the random effects measure chosen. (C) 2009 Elsevier Inc. All rights reserved
U2 - https://doi.org/10.1016/j.jclinepi.2009.02.007
DO - https://doi.org/10.1016/j.jclinepi.2009.02.007
M3 - Article
C2 - 19447007
SN - 0895-4356
VL - 62
SP - 1292
EP - 1300
JO - Journal of Clinical Epidemiology
JF - Journal of Clinical Epidemiology
IS - 12
ER -