\title{studies on the accuracy of venous ultrasonography for the diagnosis of deep venous thrombosis}
\description{results from diagnostic accuracy studies examining the accuracy of venous ultrasonography for the diagnosis of deep venous thrombosis.}
\bold{id}       \tab \code{numeric}   \tab study id \cr
\bold{patients} \tab \code{character} \tab patient group (either symptomatic or asymptomatic patients) \cr
\bold{tp}       \tab \code{numeric}   \tab number of true positives \cr
\bold{np}       \tab \code{numeric}   \tab number of positive patients (cases) \cr
\bold{tn}       \tab \code{numeric}   \tab number of true negatives \cr
\bold{nn}       \tab \code{numeric}   \tab number of negative patients (non-cases)
the studies included in the dataset examined the accuracy of venous ultrasonography for the diagnossis of a first deep venous thrombosis in symptomatic and asymptomatic patients. cases and non-cases were determined based on contrast venography. venous ultrasonography was then used to make a diagnosis, leading to a given number of true positives and negatives.
a subset of this dataset (using only the studies with asymptomatic patients) was used by deeks et al. (2005) to illustrate methods for detecting publication bias (or small-study effects) in meta-analyses of diagnostic accuracy studies.
kearon, c., julian, j. a., math, m., newman, t. e., & ginsberg, j. s. (1998). noninvasive diagnosis of deep venous thrombosis. \emph{annals of internal medicine}, \bold{128}(8), 663--677. \verb{https://doi.org/10.7326/0003-4819-128-8-199804150-00011}
deeks, j. j., macaskill, p., & irwig, l. (2005). the performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed. \emph{journal of clinical epidemiology}, \bold{58}(9), 882--893. \verb{https://doi.org/10.1016/j.jclinepi.2005.01.016}
### calculate diagnostic log odds ratios and corresponding sampling variances
dat <- escalc(measure="or", ai=tp, n1i=np, ci=nn-tn, n2i=nn, data=dat, add=1/2, to="all")
### fit random-effects model for the symptomatic patients
res <- rma(yi, vi, data=dat, subset=patients=="symptomatic")
### fit random-effects model for the asymptomatic patients
res <- rma(yi, vi, data=dat, subset=patients=="asymptomatic")
### estimated average diagnostic odds ratio (with 95\% ci)
### regression test for funnel plot asymmetry using se as predictor
lines(coef(reg$fit)[1] + coef(reg$fit)[2]*ys, ys, lwd=2, lty=3)
### regression test for funnel plot asymmetry using total sample size as predictor
reg <- regtest(res, model="lm", predictor="ni")
funnel(res, yaxis="ni", atransf=exp, xlim=c(0,7), at=log(c(1,10,100,1000)), ylim=c(0,300), steps=4)
### regression test for funnel plot asymmetry using 1/sqrt(ess) as predictor (deeks et al., 2005)
dat$invessi <- 1/(4*dat$np) + 1/(4*dat$nn)
tmp <- rma(yi, invessi, data=dat, subset=patients=="asymptomatic")
dat <- to.long(measure="or", ai=tp, n1i=np, ci=tn, n2i=nn,
dat$group <- factor(dat$group, levels=c(1,2), labels=c("sensitivity", "specificity"))
dat <- escalc(measure="plo", xi=out1, mi=out2, data=dat, add=1/2, to="all",
### bivariate random-effects model for logit sensitivity and specificity
res <- rma.mv(yi, vi, mods = ~ 0 + group, random = ~ group | study, struct="un", data=dat)
### estimated average sensitivity and specificity based on the model
predict(res, newmods = rbind(c(1,0),c(0,1)), transf=transf.ilogit, tau2.levels=c(1,2), digits=2)
### estimated average diagnostic odds ratio based on the model
predict(res, newmods = c(1,1), transf=exp, digits=2)
medicine, odds ratios, diagnostic accuracy studies, multivariate models, publication bias