The current statistical procedures implemented in statistical software packages for pooling of diagnostic test accuracy data include hSROC (Rutter and Gatsonis 2001) regression and the bivariate random-effects meta-analysis model (BRMA) ((Reitsma et al. 2005), (Arends et al. 2008), (Chu and Cole 2006), (R. D. Riley, Abrams, Sutton, et al. 2007)). However, these models do not report the overall mean but rather the mean for a central study with random-effect equal to zero and have difficulties estimating the correlation between sensitivity and specificity when the number of studies in the meta-analysis is small and/or when the between-study variance is relatively large (R. D. Riley, Abrams, Lambert, et al. 2007).
This tutorial on advanced statistical methods for meta-analysis of diagnostic accuracy studies discusses and demonstrates Bayesian modeling using CopulaDTA (Nyaga 2017) package in R (R Core Team 2016) to fit different models to obtain the meta-analytic parameter estimates. The focus is on the joint modelling of sensitivity and specificity using copula based bivariate beta distribution. Essentially,
The statistical methods are illustrated by re-analysing data of two published meta-analyses.
Modelling sensitivity and specificity using the bivariate beta distribution provides marginal as well as study-specific parameter estimates as opposed to using bivariate normal distribution (e.g., in BRMA) which only yields study-specific parameter estimates. Moreover, copula based models offer greater flexibility in modelling different correlation structures in contrast to the normal distribution which allows for only one correlation structure.
Introduction
In a systematic review of diagnostic test accuracy, the statistical analysis section aims at estimating the average (across studies) sensitivity and specificity of a test and the variability thereof, among other measures. There tends to be a negative correlation between sensitivity and specificity, which postulates the need for correlated data models. The analysis is statistically challenging because the user
- deals with two summary statistics,
- has to account for correlation between sensitivity and specificity,
- has to account for heterogeneity in sensitivity and specificity across the studies and
- should be allowed to incorporate covariates.
Currently, the HSROC (Rutter and Gatsonis 2001) regression or the bivariate random-effects meta-analysis model (BRMA) ((Reitsma et al. 2005), (Arends et al. 2008), (Chu and Cole 2006), (R. D. Riley, Abrams, Sutton, et al. 2007)) are recommended for pooling of diagnostic test accuracy data. These models fit a bivariate normal distribution which allows for only one correlation structure to the logit transformed sensitivity and specificity. The resulting distribution has no closed form and therefore the mean sensitivity and specificity is only estimated after numerical integration or other approximation methods, an extra step which is rarely taken.
Within the maximum likelihood estimation methods, the BRMA and HSROC models have difficulties in convergence and estimating the correlation parameter when the number of studies in the meta-analysis are small and/or when the between-study variances are relatively large (Takwoingi et al. 2015). When the correlation is close to the boundary of its parameter space, the between study variance estimates from the BRMA are upwardly biased as they are inflated to compensate for the range restriction on the correlation parameter (R. D. Riley, Abrams, Lambert, et al. 2007). This occurs because the maximum likelihood estimator truncates the between-study covariance matrix on the boundary of its parameter space, and this often occurs when the within-study variation is relatively large or the number of studies is small (R. D. Riley, Abrams, Sutton, et al. 2007).
The BRMA and HSROC assume that the transformed data is approximately normal with constant variance, however for sensitivity and specificity and proportions in generals, the mean and variance depend on the underlying probability. Therefore, any factor affecting the probability will change the mean and the variance. This implies that the in models where the predictors affect the mean but assume a constant variance will not be adequate.
Joint modelling of study specific sensitivity and specificity using existing or copula based bivariate beta distributions overcomes the above mentioned difficulties. Since both sensitivity and specificity take values in the interval space (0, 1), it is a more natural choice to use a beta distribution to describe their distribution across studies, without the need for any transformation. The beta distribution is conjugate to the binomial distribution and therefore it is easy to integrate out the random-effects analytically giving rise to the beta-binomial marginal distributions. Moreover no further integration is needed to obtain the meta-analytically pooled sensitivity and specificity. Previously, (Cong, Cox, and Cantor 2007) fitted separate beta-binomial models to the number of true positives and the number of false positives. While the model ignores correlation between sensitivity and specificity, (Cong, Cox, and Cantor 2007) reported that the model estimates are comparable to those from the SROC model (Moses, Shapiro, and Littenberg 1993), the predecessor of the HSROC model.
Ignoring the correlation would have negligible influence on the meta-analysis results when the within-study variability is large relative to the between-study variability (Riley 2009). By utilising the correlation, we allow borrowing strength across sensitivities and specificities resulting in smaller standard errors. The use of copula based mixed models within the frequentist framework for meta-analysis of diagnostic test accuracy was recently introduced by (Nikoloulopoulos 2015) who evaluated the joint density numerically.
This tutorial, presents and demonstrates hierarchical mixed models for meta-analysis of diagnostic accuracy studies. In the first level of the hierarchy, given sensitivity and specificity for each study, two binomial distributions are used to describe the variation in the number of true positives and true negatives among the diseased and healthy individuals, respectively. In the second level, we model the unobserved sensitivities and specificities using a bivariate distribution. While hierarchical models are used, the focus of meta-analysis is on the pooled average across studies and rarely on a given study estimate.
The methods are demonstrated using datasets from two previously published meta-analyses:
- on diagnostic accuracy of telomerase in urine as a tumour marker for the diagnosis of primary bladder cancer since it is a problematic dataset that has convergence issues caused by the correlation parameter being estimated to be -1 and has no covariate (Glas et al. 2003) and
- on the comparison of the sensitivity and specificity of human papillomavirus testing (using the HC2 assay) versus repeat cytology to triage women with minor cytological cervical lesions to detect underlying cervical precancer(Arbyn et al. 2013). The second dataset is used to demonstrate meta-regression with one covariate which can be naturally extended to include several covariates.
Statistical methods for meta-analysis
Definition of copula function
A bivariate copula function describes the dependence structure between two random variables. Two random variables \(X_1\) and \(X_2\) are joined by a copula function C if their joint cumulative distribution function can be written as \[F(x_1, x_2) = C(F_1 (x_1), F_2 (x_2 )), -\infty \le x_1, x_2 \le +\infty\]
According to the theorem of (Sklar 1959), there exists for every bivariate (multivariate in extension) distribution a copula representation C which is unique for continuous random variables. If the joint cumulative distribution function and the two marginals are known, then the copula function can be written as \[C(u, ~v) = F(F_1^{-1} (u), ~F_2^{-1} (v)),~ 0 \le~ u, ~v ~\le~ 1\]
A 2-dimensional copula is in fact simply a 2-dimensional cumulative function restricted to the unit square with standard uniform marginals. A comprehensive overview of copulas and their mathematical properties can be found in (Nelsen 2006). To obtain the joint probability density, the joint cumulative distribution \(F(x_1, x_2)\) should be differentiated to yield \[f(x_1, ~x_2) = f_1(x_1) ~f_2(x_2 ) ~c(F_1(x_1), ~F_2 (x_2))\] where \(f_1\) and \(f_2\) denote the marginal density functions and c the copula density function corresponding to the copula cumulative distribution function C. Therefore from Equation~, a bivariate probability density can be expressed using the marginal and the copula density, given that the copula function is absolutely continuous and twice differentiable.
When the functional form of the marginal and the joint densities are known, the copula density can be derived as follows \[c(F_1(x_1), ~F_2(x_2)) = \frac{f(x_1, ~x_2)}{f_1 (x_1 )~ f_2 (x_2 )}\]
While our interest does not lie in finding the copula function, the equations above serve to show how one can move from the copula function to the bivariate density or vice-versa, given that the marginal densities are known. The decompositions allow for constructions of other and possible better models for the variables than would be possible if we limited ourselves to only existing standard bivariate distributions.
We finish this section by mentioning an important implication when Sklar’s theorem is extended to a meta-regression setting with covariates. According to (Patton 2006), it is important that the conditioning variable remains the same for both marginal distributions and the copula, as otherwise the joint distribution might not be properly defined. This implies that covariate information should be introduced in both the marginals and the association parameters of the model.
The hierarchical model
Since there are two sources of heterogeneity in the data, the within- and between-study variability, the parameters involved in a meta-analysis of diagnostic accuracy studies vary at two levels. For each study \(i\), \(i = 1, ..., n\), let \(Y_{i}~=~(Y_{i1},~ Y_{i2})\) denote the true positives and true negatives, \(N_{i}~=~( N_{i1},~ N_{i2})\) the diseased and healthy individuals respectively, and \(\pi_{i}~ =~ (\pi_{i1},~ \pi_{i2})\) represent the `unobserved’ sensitivity and specificity respectively.
Given study-specific sensitivity and specificity, two separate binomial distributions describe the distribution of true positives and true negatives among the diseased and the healthy individuals as follows \[Y_{ij}~ |~ \pi_{ij}, ~\textbf{x}_i~ \sim~ bin(\pi_{ij},~ N_{ij}), i~=~1, ~\dots ~n, ~j~=~1, ~2\]
where \(\textbf{x}_i\) generically denotes one or more covariates, possibly affecting \(\pi_{ij}\). Equation ~ forms the higher level of the hierarchy and models the within-study variability. The second level of the hierarchy aims to model the between study variability of sensitivity and specificity while accounting for the inherent negative correlation thereof, with a bivariate distribution as follows \[
\begin{pmatrix}
g(\pi_{i1})\\
g(\pi_{i2})
\end{pmatrix} \sim f(g(\pi_{i1}),~ g(\pi_{i2}))~ =~ f(g(\pi_{i1})) ~f(g(\pi_{i2})) ~c(F_1(g(\pi_{i1})),~ F_2(g(\pi_{i2}))),
\]
where \(g(.)\) denotes a transformation that might be used to modify the (0, 1) range to the whole real line. While it is critical to ensure that the studies included in the meta-analysis satisfy the specified entry criterion, there are study specific characteristics like different test thresholds and other unobserved differences that give rise to the second source of variability, the between-study variability. It is indeed the difference in the test thresholds between the studies that gives rise to the correlation between sensitivity and specificity. Including study level covariates allows us to model part of the between-study variability. The covariate information can and should be used to model the mean as well as the correlation between sensitivity and specificity.
In the next section we give more details on different bivariate distributions \(f(g(\pi_{i1}),~g(\pi_{i2}))\) constructed using the logit or identity link function \(g(.)\), different marginal densities and/or different copula densities \(c\). We discuss their implications and demonstrate their application in meta-analysis of diagnostic accuracy studies. An overview of suitable parametric families of copula for mixed models for diagnostic test accuracy studies was recently given by Nikoloupolous (2015). Here, we consider five copula functions which can model negative correlation.
Bivariate Gaussian copula
Given the density and the distribution function of the univariate and bivariate standard normal distribution with correlation parameter \(\rho \in (-1, 1)\), the bivariate Gaussian copula function and density is expressed (Meyer 2013) as \[C(u, ~v, ~\rho) = \Phi_2(\Phi^{-1}(u),~ \Phi^{-1}(v),~ \rho), \] \[c(u, ~v, ~\rho) =~ \frac{1}{\sqrt{1~-~\rho^2}} ~exp\bigg(\frac{2~\rho~\Phi^{-1}(u) ~\Phi^{-1}(v) - \rho^2~ (\Phi^{-1}(u)^2 + \Phi^{-1}(v)^2)}{2~(1 - \rho^2)}\bigg ) \]
The logit transformation is often used in binary logistic regression to relate the probability of “success” (coded as 1, failure as 0) of the binary response variable with the linear predictor model that theoretically can take values over the whole real line. In diagnostic test accuracy studies, the `unobserved’ sensitivities and specificities can range from 0 to 1 whereas their logits = \(log ( \frac{\pi_{ij}}{1~-~ \pi_{ij}})\) can take any real value allowing to use the normal distribution as follows \[
logit (\pi_{ij}) ~\sim~ N(\mu_j, ~\sigma_j) ~<=> ~logit (\pi_{ij}) ~=~ \mu_j ~+~ \varepsilon_{ij},
\] where, \(\mu_j\) is a vector of the mean sensitivity and specificity for a study with zero random effects, and \(\varepsilon_{i}\) is a vector of random effects associated with study \(i\). Now \(u\) is the normal distribution function of \(logit(\pi_{i1}\)) with parameters \(\mu_1\) and \(\sigma_1\), is the normal distribution function of \(logit(\pi_{i2})\) with parameters \(\mu_2\) and \(\sigma_2\), \(\Phi_2\) is the distribution function of a bivariate standard normal distribution with correlation parameter \(\rho \in (-1, ~1)\) and \(\Phi^{-1}\) is the quantile of the standard normal distribution. In terms of \(\rho\), Kendall’s tau is expressed as (\(\frac{2}{\pi}\))arcsin\((\rho)\).
With simple algebra the copula density with normal marginal distributions simplifies to \[
c(u, ~v, ~\rho) = \frac{1}{\sqrt{1 - \rho^2}} ~exp\bigg(\frac{1}{2 ~(1 ~-~ \rho^2)}~ \bigg( \frac{2~\rho~(x - \mu_1)~(y - \mu_2)}{\sigma_1 ~\sigma_2} ~-~ \rho^2 ~\bigg (\frac{{x ~-~ \mu_1}^2}{\sigma_1} ~+~ \frac{{y ~-~ \mu_2}^2}{\sigma_2}\bigg)\bigg ) \bigg ). \]
The product of the copula density above, the normal marginal of \(logit(\pi_{i1}\)) and \(logit(\pi_{i2}\)) form a bivariate normal distribution which characterize the model by (Reitsma et al. 2005), (Arends et al. 2008), (Chu and Cole 2006), and (R. D. Riley, Abrams, Lambert, et al. 2007), the so-called bivariate random-effects meta-analysis (BRMA) model, recommended as the appropriate method for meta-analysis of diagnostic accuracy studies. Study level covariate information explaining heterogeneity is introduced through the parameters of the marginal and the copula as follows \[\boldsymbol{\mu}_j = \textbf{X}_j\textbf{B}_j^{\top}. \]
\(\boldsymbol{X}_j\) is a \(n \times p\) matrix containing the covariates values for the mean sensitivity(\(j = 1\)) and specificity(\(j = 2\)). For simplicity, assume that \(\boldsymbol{X}_1 = \boldsymbol{X}_2 = \boldsymbol{X}\). \(\boldsymbol{B}_j^\top\) is a \(p \times\) 1$ vector of regression parameters, and \(p\) is the number of parameters. By inverting the logit functions, we obtain \[
\pi_{ij} = logit^{-1} (\mu_j + \varepsilon_{ij}).
\]
Therefore, the meta-analytic sensitivity and specificity obtained by averaging over the random study effect, is given by, for \(j = 1, 2\) \[
E(\pi_{j} )~=~E(logit^{-1} (\mu_j ~+~ \varepsilon_{ij}))
~=~\int_{-\infty}^{\infty}logit^{-1}(\mu_j ~+~ \varepsilon_{ij}) f(\varepsilon_{ij},~\sigma_j)~d\varepsilon_{ij},
\]
assuming that \(\sigma_1^2 > 0\) and \(\sigma_2^2 > 0\). The integration above has no analytical expression and therefore needs to be numerically approximated and the standard are not easily available. Using MCMC simulation in the Bayesian framework the meta-analytic estimates can be easily computed as well as a standard error estimate and a credible intervals \(E(\pi_{j})\) with minimum effort by generating predictions of the fitted bivariate normal distribution.
In the frequentist framework, it is more convenient however to use numerical averaging by sampling a large number \(M\) of random-effects \(\hat{\varepsilon}_{ij}\) from the fitted distribution and to estimate the meta-analytic sensitivity and specificity by, for \(j = 1, 2\) \[
\hat{E}(\pi_{j}) = \frac{1}{M} \sum_{i~ =~ 1}^{M}logit^{-1}(\hat{\mu}_j + \hat{\varepsilon}_{ij}).
\]
However, inference is not straightforward in the frequentist framework since the standard errors are not available. When \(\varepsilon_{ij} = 0\), then \[
E(\pi_{j} ~|~ \varepsilon_{ij} = 0) ~=~ logit^{-1}(\mu_j).
\]
Inference for \(E(\pi_{j} ~|~\varepsilon_{ij} ~=~ 0)\), as expressed in Equation~, can be done in both Bayesian and frequentist framework. The equation represents the mean sensitivity and specificity for a ``central" study with \(\varepsilon_{ij} ~=~ 0\). Researchers often seem to confuse \(E(\pi_{j} ~|~\varepsilon_{ij} ~=~ 0)\) with \(E(\pi_{j})\) but due to the non-linear logit transformations, they are clearly not the same parameter.
With the identity link function, no transformation on study-specific sensitivity and specificity is performed. A natural choice for and would be beta distribution functions with parameters (\(\alpha_1, ~\beta_1\)) and (\(\alpha_2, ~\beta_2\)) respectively. Since \(\pi_{ij} ~\sim ~ beta(\alpha_j, ~\beta_j)\), the meta-analytic sensitivity and specificity are analytically solved as follows \[
E(\pi_{j} )
= \frac{\alpha_j}{\alpha_j + \beta_j},
\]
After reparameterising the beta distributions using the mean (\(\mu_j ~=~ \frac{\alpha_j}{\alpha_j ~+~ \beta_j}\)) and certainty (\(\psi_j ~=~ \alpha_j ~+~ \beta_j\)) or dispersion (\(\varphi_j ~=~ \frac{1}{1~ +~ \alpha_j~+~\beta_j}\)) parameters different link functions introduce covariate information to the mean, certainty/dispersion and association (\(\rho\)) parameters. A typical model parameterisation is \[
\boldsymbol{\mu}_j ~=~ logit^{-1}(\textbf{XB}_j^{\top}), \\
\boldsymbol{\psi}_j ~=~ g(\textbf{WC}_j^{\top}), \\
\boldsymbol{\alpha}_j ~=~ \boldsymbol{\mu}_{j} ~\circ~ \boldsymbol{\psi}_{j}, \\
\boldsymbol{\beta}_j ~=~ ( \textbf{1} ~-~ \boldsymbol{\mu}_{j}) ~\circ~ \boldsymbol{\psi}_{j}, \\
\boldsymbol{\rho} ~=~ tanh(\textbf{ZD}_j^{\top}) ~=~ \frac{exp(2 \times \textbf{ZD}_j^{\top}) ~-~ 1}{expa(2\times \textbf{ZD}_j^{\top}) ~+~ 1}. \]
\(\boldsymbol{X, W}\) and \(\boldsymbol{Z}\) are a \(n \times p\) matrices containing the covariates values for the mean, dispersion and correlation which we will assume has similar information and denoted by \(\boldsymbol{X}\) for simplicity purpose, \(p\) is the number of parameters, \(\textbf{B}_j^{\top}\) , \(\textbf{V}_j^{\top}\) and \(\textbf{D}_j^{\top}\) are a \(p \times 1\) vectors of regression parameters relating covariates to the mean, variance and correlation respectively. \(g(.)\) is the log link to mapping \(\textbf{XC}_j^{\top}\) to the positive real number line and \(\circ\) is the Hadamard product.
Frank copula
This flexible copula in the so-called family of Archimedean copulas by (Frank 1979). The functional form of the copula and the density is given by; \[
C(F(\pi_{i1} ), ~F(\pi_{i2} ),\theta) ~=~ - \frac{1}{\theta} ~log \bigg [ 1 + \frac{(e^{-\theta ~F(\pi_{i1})} - 1)
(e^{-\theta~ F(\pi_{i2})} - 1)}{e^{-\theta} - 1} \bigg ], \\
c(F(\pi_{i1} ), ~F(\pi_{i2} ),\theta) ~=~ \frac {\theta~(1 - e^{-\theta})~e^{-\theta~(F(\pi_{i1}) ~ +~ F(\pi_{i2}))}}{[1 - e^{-\theta}-(1 - e^{-\theta ~F(\pi_{i1})})~(1 - e^{-\theta ~F(\pi_{i2})})]^2} . \]
Since \(\theta \in \mathbb{R}\), both positive and negative correlation can be modelled, making this one of the more comprehensive copulas. When \(\theta\) is 0, sensitivity and specificity are independent. For \(\theta > 0\), sensitivity and specificity exhibit positive quadrant dependence and negative quadrant dependence when \(\theta < 0\). The Spearman correlation \(\rho_s\) and Kendall’s tau \(\tau_k\) can be expressed in terms of \(\theta\) as \[
\rho_s = 1 - 12~\frac{D_2 (-\theta) ~-~ D_1(-\theta)}{\theta}, \\
\tau_k = 1 + 4~ \frac{D_1(\theta) ~-~ 1}{\theta},
\]
where \(D_j(\delta)\) is the Debye function defined as \[
D_j(\delta) ~=~ \frac{j}{\delta^j} \int_{\theta}^{\delta}\frac{t^j}{exp(t) ~-~ 1} ~dt, ~j~= 1, ~2.
\]
Covariate information is introduced in a similar manner as earlier. The identity link is used for the association parameter \(\theta\).
Farlie-Gumbel-Morgenstern copula (FGM)
This popular copula by (Farlie 1960), (Gumbel 1960) and (Morgenstern 1956) is defined as \[
C(F(\pi_{i1} ), ~F(\pi_{i2}),~\theta) ~= ~F(\pi_{i1})~F(\pi_{i2})[1 ~+~ \theta~(1 - F(\pi_{i1}))~(1 ~-~ F(\pi_{i2}))], \nonumber \\
c(F(\pi_{i1} ), ~F(\pi_{i2}),~\theta) ~=~ [1 ~+~ \theta~(2~F(\pi_{i1}) ~-~ 1)~(2~F(\pi_{i2}) ~-~ 1)]. \]
Because \(\theta \in (-1, 1)\), the Spearman correlation and Kendall’s tau are expressed in terms of \(\theta\) as \(\theta/3\) and \(2\theta/9\) respectively, making this copula only appropriate for data with weak dependence since \(|\rho_s| ~\leq~ 1/3\). The logit link, log/identity link and Fisher’s z transformation can be used to introduce covariate information in modelling the mean, dispersion and association parameter.
Clayton copula
The Clayton copula function and density by (Clayton 1978) is defined as \[
C(F(\pi_{i1}), ~F(\pi_{i2}),~\theta) = [F(\pi_{i1})^{- \theta} ~+~ F(\pi_{i2})^{-\theta} ~-~ 1]^{\frac{- 1}{\theta}}, \nonumber \\
c(F(\pi_{i1}), ~F(\pi_{i2}),~\theta) = (1 ~+~ \theta)~F(\pi_{i1})^{- (1 ~+~ \theta)} ~F(\pi_{i2})^{- (1 + \theta)}~[F(\pi_{i1} )^{- \theta} + F(\pi_{i2})^{- \theta} ~-~ 1]^{\frac{- (2~\theta ~+~ 1)}{\theta}}. \]
Since \(\theta \in (0, ~\infty)\), the Clayton copula typically models positive dependence; Kendall’s tau equals \(\theta/(\theta ~+~ 2)\). However, the copula function can be rotated by \(90^\circ\) or \(270^\circ\) to model negative dependence. The distribution and density functions following such rotations are given by \[
C_{90} (F(\pi_{i1}), ~F(\pi_{i2}), ~\theta) ~=~ F(\pi_{i2}) ~-~ C(1 ~-~ F(\pi_{i1}), ~F(\pi_{i2}), ~\theta), \nonumber \\
c_{90} (F(\pi_{i1}),~ F(\pi_{i2}),~\theta) ~=~ (1 ~+~ \theta)(1 ~-~ F(\pi_{i1}))^{- (1 ~+~ \theta)}~F(\pi_{i2})^{- (1 ~+~ \theta)} ~[(1 - F(\pi_{i1}))^{-\theta} \nonumber \\
+~ F(\pi_{i2})^{- \theta} - 1]^{\frac{- (2~\theta ~+~ 1)}{\theta}}, \] and \[
C_{270} (F(\pi_{i1}), ~F(\pi_{i2}), ~\theta) ~= ~F(\pi_{i1})- C(F(\pi_{i1}), ~1 ~-~ F(\pi_{i2}),\theta), \nonumber \\
c_{270} (F(\pi_{i1}), ~F(\pi_{i2}), ~\theta) ~=~(1 ~+~ \theta) ~F(\pi_{i1} )^{- (1 ~+~ \theta)}~ (1 ~-~ F(\pi_{i2} ))^{- (1 ~+~ \theta)}~[F(\pi_{i1})^{- \theta} \nonumber \\
+ (1 ~-~ F(\pi_{i2}))^{- \theta} ~-~ 1]^{\frac{- (2~\theta ~+~ 1)}{\theta}}. \]
The logit, log/identity and log/identity links can be used to introduce covariate information in modelling the mean (\(\mu_j\)), certainty (\(\psi_j\))/dispersion (\(\varphi_j\)) and association (\(\theta\)) parameters respectively.
Of course other copula functions that allow for negative association can be chosen. It is also an option to use known bivariate beta distributions. However, it is not always straightforward and analytically attractive to derive the corresponding copula function for all bivariate distributions. The use of existing bivariate beta distributions in meta-analysis of diagnostic accuracy studies has been limited because these densities model positive association( e.g., (Libby and Novick 1982), (Olkin and Liu 2003)), or both positive and negative association but over a restricted range( e.g., (Sarmanov 1966)).
The intercept only model
The CopulaDTA package has five different correlation structures that result to five different bivariate beta-binomial distributions to fit to the data. The correlation structure is specified by indicating copula ="gauss" or "fgm" or "c90" or "270" or "frank" in the cdtamodel function.
gauss.1 <- cdtamodel("gauss")
The code for the fitted model and the model options are displayed as follows
print(gauss.1)
str(gauss.1)
The Gaussian copula bivariate beta-binomial distribution is fitted to the telomerase data with the following code
fitgauss.1 <- fit(
gauss.1,
data = telomerase,
SID = "ID",
iter = 28000,
warmup = 1000,
thin = 30,
seed = 3)
By default, chains = 3 and cores = 3 and need not be specified unless otherwise. From the code above, 28000 samples are drawn from each of the 3 chains, the first 1000 samples are discarded and thereafter every 30\(^{th}\) draw kept such that each chain has 900 post-warm-up draws making a total of 2700 post-warm-up draws. The seed value, seed = 3, specifies a random number generator to allow reproducibility of the results and cores = 3 allows for parallel-processing of the chains by using 3 cores, one core for each chain. They were no initial values specified and in that case, the program randomly generates random values satisfying the parameter constraints.
The trace plots below show satisfactory mixing of the chains and convergence.
Next, obtain the model summary estimates as follows
print(fitgauss.1, digits = 4)
## Posterior marginal mean and median sensitivity and specificity
## with 95% credible intervals
##
## Parameter Mean Lower Median Upper n_eff Rhat
## MUse[1] Sensitivity 0.756762 6.904e-01 0.756036 0.81658 1196.9 1.001
## MUsp[1] Specificity 0.798289 6.171e-01 0.813517 0.90640 704.4 1.004
## ktau[1] Correlation -0.820176 -9.861e-01 -0.876343 -0.33334 269.2 1.015
## Varse[1] Var(Sens) 0.006198 8.321e-06 0.005047 0.01947 165.7 1.007
## Varsp[1] Var(Spec) 0.048111 1.357e-02 0.041060 0.12204 169.5 1.007
##
##
## Model characteristics
##
## Copula function: gauss, sampling algorithm: NUTS(diag_e)
##
## Formula(1): MUse ~ 1
## Formula(2): MUsp ~ 1
## Formula(3): Omega ~ 1
## 3 chain(s)each with iter=28000; warm-up=1000; thin=30.
## post-warmup draws per chain=900;total post-warmup draws=2700.
##
## Predictive accuracy of the model
##
## Log point-wise predictive density (LPPD): -38.0607
## Effective number of parameters: 7.5807
## Watanabe-Akaike information Criterion (WAIC): 91.2828
From the output above, n_eff and Rhat both confirm proper mixing of the chains with little autocorrelation. The meta-analytic sensitivity MUse[1] and specificity MUsp[1] is 0.7568 [0.6904, 0.8166] and 0.7983 [0.6171, 0.9064] respectively. The between-study variability in sensitivity and specificity is 0.0062 [0, 0.0195] and 0.0048 [0.0136, 0.1220], respectively. The Kendall’s tau correlation between sensitivity and specificity is estimated to be -0.8202 [-0.9861, -0.3333].
The command below produces a series of forest plots.
## $G1
##
## $G2
##
## $G3
## Warning: Removed 2 rows containing missing values (geom_errorbar).
$G1 is a plot of the study-specific sensitivity and specificity (magenta points) and their corresponding 95 % exact confidence intervals (black lines). $G2 is a plot of the posterior study-specific (blue points) and marginal(blue diamond) sensitivity and specificity and their corresponding 95 % credible intervals (black lines).
$G3 is a plot of the posterior study-specific (blues stars), and marginal(blue diamond) sensitivity and specificity and their corresponding 95 % credible intervals (black lines). The study-specific sensitivity and specificity (magenta points) and their corresponding 95 % exact confidence intervals (thick grey lines) are also presented.
As observed in plots above, the posterior study-specific sensitivity and specificity are less extreme and variable than the “observed” study-specific sensitivity and specificity. In other words, there is “shrinkage” towards the overall mean sensitivity and specificity as studies borrow strength from each other in the following manner: the posterior study-specific estimates depends on the global estimate and thus also on all other the studies.
The other four copula based bivariate beta distributions are fitted as follows
c90.1 <- cdtamodel(copula = "c90")
fitc90.1 <- fit(c90.1,
data=telomerase,
SID="ID",
iter=28000,
warmup=1000,
thin=30,
seed=718117096)
c270.1 <- cdtamodel(copula = "c270")
fitc270.1 <- fit(c270.1,
data=telomerase,
SID="ID",
iter=19000,
warmup=1000,
thin=20,
seed=3)
fgm.1 <- cdtamodel(copula = "fgm")
fitfgm.1 <- fit(fgm.1,
data=telomerase,
SID="ID",
iter=19000,
warmup=1000,
thin=20,
seed=3)
frank.1 <- cdtamodel(copula = "frank")
fitfrank.1 <- fit(frank.1,
data=telomerase,
SID="ID",
iter=19000,
warmup=1000,
thin=20,
seed=1959300748)
For comparison purpose, the current recommended model; the BRMA, which uses normal marginals is also fitted to the data though it is not part of the CopulaDTA package. The model is first expressed in Stan modelling language in the code below and is stored within R environment as character string named BRMA1.
BRMA1 <- "
data{
int<lower = 0> N;
int<lower = 0> tp[N];
int<lower = 0> dis[N];
int<lower = 0> tn[N];
int<lower = 0> nondis[N];
}
parameters{
real etarho;
vector[2] mul;
vector<lower = 0>[2] sigma;
vector[2] logitp[N];
vector[2] logitphat[N];
}
transformed parameters{
vector[N] p[2];
vector[N] phat[2];
real MU[2];
vector[2] mu;
real rho;
real ktau;
matrix[2,2] Sigma;
rho = tanh(etarho);
ktau = (2/pi())*asin(rho);
for (a in 1:2){
for (b in 1:N){
p[a][b] = inv_logit(logitp[b][a]);
phat[a][b] = inv_logit(logitphat[b][a]);
}
mu[a] = inv_logit(mul[a]);
}
MU[1] = mean(phat[1]);
MU[2] = mean(phat[2]);
Sigma[1, 1] = sigma[1]^2;
Sigma[1, 2] = sigma[1]*sigma[2]*rho;
Sigma[2, 1] = sigma[1]*sigma[2]*rho;
Sigma[2, 2] = sigma[2]^2;
}
model{
etarho ~ normal(0, 10);
mul ~ normal(0, 10);
sigma ~ cauchy(0, 2.5);
for (i in 1:N){
logitp[i] ~ multi_normal(mul, Sigma);
logitphat[i] ~ multi_normal(mul, Sigma);
}
tp ~ binomial(dis,p[1]);
tn ~ binomial(nondis, p[2]);
}
generated quantities{
vector[N*2] loglik;
for (i in 1:N){
loglik[i] = binomial_lpmf(tp[i]| dis[i], p[1][i]);
}
for (i in (N+1):(2*N)){
loglik[i] = binomial_lpmf(tn[i-N]| nondis[i-N], p[2][i-N]);
}
}
"
Next, prepare the data by creating a list as follows
datalist = list(
tp = telomerase$TP,
dis = telomerase$TP + telomerase$FN,
tn = telomerase$TN,
nondis = telomerase$TN + telomerase$FP,
N = 10)
In the data block the dimensions and names of variables in the dataset are specified, here Ns indicate the number of studies in the dataset. The parameters block introduces the unknown parameters to be estimated. These are etarho; a scalar representing the Fisher’s transformed form of the association parameter \(\rho\), mul;a \(2 \times 1\) vector representing the mean of sensitivity and specificity on the logit scale for a central study where the random-effect is zero, sigma; a \(2 \times 1\) vector representing the between study standard deviation of sensitivity and specificity on the logit scale, logitp; a \(Ns \times 2\) array of study-specific sensitivity in the first column and specificity in the second column on logit scale, and logitphat; a \(Ns \times 2\) array of predicted sensitivity in the first column and predicted specificity in the second column on logit scale.
The parameters are further transformed in the transformed parameters block. Here, p is a \(2 \times Ns\) array of sensitivity in the first column and specificity in the second column after inverse logit transformation of logitp, and phat is a \(2 \times Ns\) array of predicted sensitivity in the first column and predicted specificity in the second column after inverse logit transformation of logitphat to be used in computing the meta-analytic sensitivity and specificity. mu is a \(2 \times 1\) vector representing the mean of sensitivity and specificity for a certain study with a random effect equal to 0, MU is a \(2 \times 1\) vector containing the meta-analytic sensitivity and specificity, Sigma; a $2 \(\times\) 2$ matrix representing the variance-covarince matrix of sensitivity and specificity on the logit scale, rho and ktau are scalars representing the Pearson’s and Kendall’s tau correlation respectively. The prior distributions for the all parameters and data likelihood are defined in the model block. Finally, in the generated quantities block, loglik is a \((2Ns) \times 1\) vector of the log likelihood needed to compute the WAIC.
Next, call the function stan from the rstan package to translate the code into C++, compile the code and draw samples from the posterior distribution as follows
brma.1 <- stan(model_code = BRMA1,
data = datalist,
chains = 3,
iter = 5000,
warmup = 1000,
thin = 10,
seed = 3,
cores = 3)
The parameter estimates are extracted and the chain convergence and autocorrelation examined further with the following code
print(brma.1, pars=c('MU', 'mu', 'rho', "Sigma"), digits=4, prob=c(0.025, 0.5, 0.975))
## Inference for Stan model: 61572683b29d52354783115614fab729.
## 3 chains, each with iter=5000; warmup=1000; thin=10;
## post-warmup draws per chain=400, total post-warmup draws=1200.
##
## mean se_mean sd 2.5% 50% 97.5% n_eff Rhat
## MU[1] 0.7525 0.0018 0.0517 0.6323 0.7562 0.8415 796 0.9999
## MU[2] 0.7908 0.0034 0.1095 0.5273 0.8094 0.9539 1045 1.0008
## mu[1] 0.7668 0.0013 0.0388 0.6869 0.7688 0.8369 891 0.9990
## mu[2] 0.8937 0.0027 0.0753 0.6943 0.9115 0.9825 789 0.9992
## rho -0.9311 0.0070 0.1353 -0.9996 -0.9813 -0.5626 372 1.0077
## Sigma[1,1] 0.3376 0.0091 0.2918 0.0579 0.2554 0.9851 1026 1.0023
## Sigma[1,2] -1.2291 0.0272 0.8765 -3.4195 -1.0031 -0.2724 1040 0.9991
## Sigma[2,1] -1.2291 0.0272 0.8765 -3.4195 -1.0031 -0.2724 1040 0.9991
## Sigma[2,2] 5.6827 0.1282 4.1931 1.4720 4.6330 16.9031 1070 1.0002
##
## Samples were drawn using NUTS(diag_e) at Mon Oct 09 09:19:55 2017.
## For each parameter, n_eff is a crude measure of effective sample size,
## and Rhat is the potential scale reduction factor on split chains (at
## convergence, Rhat=1).
The meta-analytic sensitivity (MU[1]) and specificity (MU[2]) and 95% credible intervals are 0.7525[0.6323, 0.8415] and 0.7908[0.5273, 0.9539] respectively. This differs from what the authors published (0.75[0.66, 0.74] and 0.86[0.71, 0.94]) in two ways. The authors fitted the standard bivariate normal distribution to the logit transformed sensitivity and specificity values across the studies allowing for heterogeneity between the studies and disregarded the higher level of the hierarchical model. Because of this the authors had to use a continuity correction of 0.5 since the seventh study had “observed” specificity equal to 1, a problem not encountered in the hierarchical model. Secondly the authors do not report the meta-analytic values but rather report the mean sensitivity (mu[1]) and specificity (mu[2]) for a particular, hypothetical study with random-effect equal to zero, which in our case is 0.7668[0.6869, 0.8369] and 0.8937[0.6943, 0.9825] respectively and is comparable to what the authors reported. This discrepancy between MU and mu will indeed increase with increase in the between study variability. Here, (MU[1]) and (mu[1]) are similar because the between-study variability in sensitivity in the logit scale (Sigma[1,1]) is small 0.333 [0.0579, 0.9851]. (MU[2]) is subtantially smaller than (mu[2]) as a result of the subtantial between-study heterogeneity in specificity in the logit scale (Sigma[2,2]) = 5.6827 [1.4720, 16.9031].
The figure below shows satisfactory chain mixing with little autocorrelation for most of the fitted models except the Clayton copula models.
f1.1 <- traceplot(fitgauss.1)
f1.2 <- traceplot(fitc90.1)
f1.3 <- traceplot(fitc270.1)
f1.4 <- traceplot(fitfgm.1)
f1.5 <- traceplot(fitfrank.1)
#draws <- as.array(brma.1, pars=c('MU'))
f1.6 <- rstan::stan_trace(brma.1, pars=c('MU'))
## Loading required package: lattice
## Loading required package: plyr
multiplot(f1.1, f1.2, f1.3, f1.4, f1.5, f1.6, cols=2)
The mean sensitivity and specificity as estimated by all the fitted distributions are in the table below.
brma.summary1 <- data.frame(Parameter=c("Sensitivity", "Specificity", "Correlation", "Var(lSens)", "Sigma[1,2]", "Sigma[2,1]","Var(lSpec)"),
summary(brma.1, pars=c('MU', 'ktau', 'Sigma'))$summary[,c(1, 4, 6, 8:10)])
brma.summary1 <- brma.summary1[-c(5,6),]
names(brma.summary1)[2:5] <- c("Mean", "Lower", "Median", "Upper")
library(loo)
## This is loo version 1.1.0
Table1 <- cbind(Model=rep(c("Gaussian", "C90", "C270", "FGM", "Frank", "BRMA"), each=5),
rbind(summary(fitgauss.1)$Summary,
summary(fitc90.1)$Summary,
summary(fitc270.1)$Summary,
summary(fitfgm.1)$Summary,
summary(fitfrank.1)$Summary,
brma.summary1),
WAIC = t(data.frame(rep(c(summary(fitgauss.1)$WAIC[1],
summary(fitc90.1)$WAIC[1],
summary(fitc270.1)$WAIC[1],
summary(fitfgm.1)$WAIC[1],
summary(fitfrank.1)$WAIC[1],
loo::waic(extract_log_lik(brma.1, parameter_name="loglik"))[3]), each=5))))
## Warning: 6 (30.0%) p_waic estimates greater than 0.4.
## We recommend trying loo() instead.
rownames(Table1) <- NULL
print(Table1, digits=4)
## Model Parameter Mean Lower Median Upper n_eff
## 1 Gaussian Sensitivity 0.756762 6.904e-01 0.756036 8.166e-01 1196.857
## 2 Gaussian Specificity 0.798289 6.171e-01 0.813517 9.064e-01 704.379
## 3 Gaussian Correlation -0.820176 -9.861e-01 -0.876343 -3.333e-01 269.179
## 4 Gaussian Var(Sens) 0.006198 8.321e-06 0.005047 1.947e-02 165.705
## 5 Gaussian Var(Spec) 0.048111 1.357e-02 0.041060 1.220e-01 169.508
## 6 C90 Sensitivity 0.751379 6.913e-01 0.753546 8.098e-01 25.638
## 7 C90 Specificity 0.807051 6.549e-01 0.821119 9.069e-01 119.897
## 8 C90 Correlation -0.528340 -9.766e-01 -0.725178 -4.020e-18 4.111
## 9 C90 Var(Sens) 0.004885 3.400e-04 0.003297 1.955e-02 11.615
## 10 C90 Var(Spec) 0.045694 1.556e-02 0.038049 1.020e-01 137.149
## 11 C270 Sensitivity 0.757528 6.877e-01 0.761163 8.210e-01 273.236
## 12 C270 Specificity 0.803502 6.328e-01 0.811740 9.097e-01 1081.987
## 13 C270 Correlation -0.697493 -9.827e-01 -0.808717 -3.332e-06 40.012
## 14 C270 Var(Sens) 0.006662 2.667e-04 0.005293 2.027e-02 556.055
## 15 C270 Var(Spec) 0.044767 1.268e-02 0.037922 1.112e-01 1098.815
## 16 FGM Sensitivity 0.759407 6.891e-01 0.761931 8.174e-01 2475.208
## 17 FGM Specificity 0.802588 6.453e-01 0.812498 9.045e-01 2293.332
## 18 FGM Correlation -0.174538 -2.222e-01 -0.222221 2.222e-01 785.016
## 19 FGM Var(Sens) 0.005390 7.425e-07 0.004181 1.813e-02 1019.633
## 20 FGM Var(Spec) 0.041890 1.177e-02 0.036671 9.997e-02 2479.371
## 21 Frank Sensitivity 0.756683 6.855e-01 0.758340 8.152e-01 2686.631
## 22 Frank Specificity 0.808239 6.472e-01 0.818777 9.110e-01 1910.561
## 23 Frank Correlation -0.706819 -8.550e-01 -0.692019 1.000e+00 2700.000
## 24 Frank Var(Sens) 0.006678 5.896e-04 0.005280 2.140e-02 2699.766
## 25 Frank Var(Spec) 0.042067 1.201e-02 0.035908 1.039e-01 1937.653
## 26 BRMA Sensitivity 0.752531 6.323e-01 0.756181 8.415e-01 796.037
## 27 BRMA Specificity 0.790796 5.273e-01 0.809420 9.539e-01 1044.902
## 28 BRMA Correlation -0.822353 -9.824e-01 -0.876654 -3.804e-01 238.268
## 29 BRMA Var(lSens) 0.337556 5.792e-02 0.255387 9.851e-01 1025.609
## 30 BRMA Var(lSpec) 5.682692 1.472e+00 4.632967 1.690e+01 1070.481
## Rhat WAIC
## 1 1.0014 91.28
## 2 1.0044 91.28
## 3 1.0154 91.28
## 4 1.0072 91.28
## 5 1.0069 91.28
## 6 1.1047 91.40
## 7 1.0304 91.40
## 8 1.3707 91.40
## 9 1.1005 91.40
## 10 1.0311 91.40
## 11 1.0096 90.75
## 12 1.0001 90.75
## 13 1.0407 90.75
## 14 1.0024 90.75
## 15 0.9999 90.75
## 16 0.9998 97.37
## 17 0.9996 97.37
## 18 1.0070 97.37
## 19 1.0034 97.37
## 20 0.9999 97.37
## 21 0.9994 90.55
## 22 0.9997 90.55
## 23 NaN 90.55
## 24 0.9990 90.55
## 25 0.9992 90.55
## 26 0.9999 86.76
## 27 1.0008 86.76
## 28 1.0218 86.76
## 29 1.0023 86.76
## 30 1.0002 86.76
The results are presented graphically as follows
g1 <- ggplot(Table1[Table1$Parameter %in% c("Sensitivity", "Specificity"),],
aes(x=Model,
y= Mean)) +
geom_point(size=3) +
theme_bw() +
coord_flip() +
facet_grid(~ Parameter, switch="x") +
geom_errorbar(aes(ymin=Lower,
ymax=Upper),
size=.75,
width=0.15) +
theme(axis.text.x = element_text(size=13, colour='black'),
axis.text.y = element_text(size=13, colour='black'),
axis.title.x = element_text(size=13, colour='black'),
strip.text = element_text(size = 13, colour='black'),
axis.title.y= element_text(size=13, angle=0, colour='black'),
strip.text.y = element_text(size = 13, colour='black'),
strip.text.x = element_text(size = 13, colour='black'),
plot.background = element_rect(fill = "white", colour='white'),
panel.grid.major = element_blank(),
panel.background = element_blank(),
strip.background = element_blank(),
axis.line.x = element_line(color = 'black'),
axis.line.y = element_line(color = 'black')) +
scale_y_continuous(name="Mean [95% equal-tailed credible intervals]",
limits=c(0.45,1.1),
breaks=c(0.5, 0.75, 1),
labels = c("0.5", "0.75", "1"))
g1
Model comparison
Table1 above showed that the correlation as estimated by the BRMA model and the Gaussian copula bivariate beta are more extreme but comparable to the estimates from the Frank, \(90^{\circ}\)- and \(270^{\circ}\)- Clayton copula. On the other extreme is the estimate from the model FGM copula bivariate beta and this is due to the constraints on the association parameter in the FGM copula where values lie within |2/9|.
In the figure above g1, the marginal mean sensitivity and specificity from the five bivariate beta distributions are comparable with subtle differences in the 95 percent credible intervals despite differences in the correlation structure.
Glas et al. (2003) and Riley et al. (2007a) estimated the Pearson’s correlation parameter in the BRMA model \(\rho\) as -1 within the frequentist framework. Using maximum likelihood estimation, Riley et al. (2007b) showed that the between-study correlation from the BRMA is often estimated as +/-1. Without estimation difficulties, the table above shows an estimated Pearson’s correlation of -0.8224[-0.9824, -0.3804]. This is because Bayesian methods are not influenced by sample size and therefore able to handle cases of small sample sizes with less issues.
Essentially, all the six models are equivalent in the first level of hierarchy and differ in specifying the prior distributions for the “study-specific” sensitivity and specificity. As thus, the models should have the same number of parameters in which case it makes sense then to compare the log predictive densities. Upon inspection, the log predictive densities from the five copula-based models are practically equivalent (min=-38.77, max=-37.89) but the effective number of parameters differed a bit (min=7.25, max=9.92). The BRMA had 6 effective parameters but a lower log predictive density of -43.4. The last column of table above indicates that the BRMA fits the data best based on the WAIC despite its lower predictive density.
Meta-regression
The ascus dataset has Test} as a covariate. The covariate is used as it is of interest to study its effect on the joint distribution of sensitivity and specificity (including the correlation). The following code fits the copula based bivariate beta-binomial distribution to the data ascus data.
fgm.2 <- cdtamodel(copula = "fgm",
modelargs = list(formula.se = StudyID ~ Test + 0))
fitfgm.2 <- fit(fgm.2,
data=ascus,
SID="StudyID",
iter=19000,
warmup=1000,
thin=20,
seed=3)
gauss.2 <- cdtamodel(copula = "gauss",
modelargs = list(formula.se = StudyID ~ Test + 0))
fitgauss.2 <- fit(gauss.2,
data=ascus,
SID="StudyID",
iter=19000,
warmup=1000,
thin=20,
seed=3)
c90.2 <- cdtamodel(copula = "c90",
modelargs = list(formula.se = StudyID ~ Test + 0))
fitc90.2 <- fit(c90.2,
data=ascus,
SID="StudyID",
iter=19000,
warmup=1000,
thin=20,
seed=3)
c270.2 <- cdtamodel(copula = "c270",
modelargs = list(formula.se = StudyID ~ Test + 0))
fitc270.2 <- fit(c270.2,
data=ascus,
SID="StudyID",
iter=19000,
warmup=1000,
thin=20,
seed=3)
frank.2 <- cdtamodel(copula = "frank",
modelargs = list(formula.se = StudyID ~ Test + 0))
fitfrank.2 <- fit(frank.2,
data=ascus,
SID="StudyID",
iter=19000,
warmup=1000,
thin=20,
seed=3)
The BRMA is fitted by the code below
BRMA2 <- "
data{
int<lower=0> N;
int<lower=0> tp[N];
int<lower=0> dis[N];
int<lower=0> tn[N];
int<lower=0> nondis[N];
matrix<lower=0>[N,2] x;
}
parameters{
real etarho; // g(rho)
matrix[2,2] betamul;
vector<lower=0>[2] sigma;
vector[2] logitp[N];
vector[2] logitphat[N];
}
transformed parameters{
row_vector[2] mul_i[N];
vector[N] p[2];
vector[N] phat[2]; //expit transformation
matrix[2,2] mu;
real rho; //pearsons correlation
real ktau; //Kendall's tau
matrix[2,2] Sigma; //Variance-covariance matrix
matrix[2,2] MU;
row_vector[2] RR;
rho = tanh(etarho); //fisher z back transformation
ktau = (2/pi())*asin(rho);
for (j in 1:2){
for (k in 1:2){
mu[j, k] = inv_logit(betamul[j, k]);
}
}
for (a in 1:N){
mul_i[a] = row(x*betamul,a);
for (b in 1:2){
p[b][a] = inv_logit(logitp[a][b]);
phat[b][a] = inv_logit(logitphat[a][b]);
}
}
MU[1,1] = sum(col(x, 1).*phat[1])/sum(col(x, 1)); //sensitivity HC2
MU[1,2] = sum(col(x, 1).*phat[2])/sum(col(x, 1)); //specificity HC2
MU[2,1] = sum(col(x, 2).*phat[1])/sum(col(x, 2)); //sensitivity RepC
MU[2,2] = sum(col(x, 2).*phat[2])/sum(col(x, 2)); //specificity RepC
RR = row(MU, 2)./row(MU, 1); //RepC vs HC2
Sigma[1, 1] = sigma[1]^2;
Sigma[1, 2] = sigma[1]*sigma[2]*rho;
Sigma[2, 1] = sigma[1]*sigma[2]*rho;
Sigma[2, 2] = sigma[2]^2;
}
model{
#Priors
etarho ~ normal(0, 10);
sigma ~ cauchy(0, 2.5);
for (i in 1:2){
betamul[i] ~ normal(0, 10);
}
for (l in 1:N){
logitp[l] ~ multi_normal(mul_i[l], Sigma);
logitphat[l] ~ multi_normal(mul_i[l], Sigma);
}
//Likelihood
tp ~ binomial(dis,p[1]);
tn ~ binomial(nondis, p[2]);
}
generated quantities{
vector[N*2] loglik;
for (i in 1:N){
loglik[i] = binomial_lpmf(tp[i]| dis[i], p[1][i]);
}
for (i in (N+1):(2*N)){
loglik[i] = binomial_lpmf(tn[i-N]| nondis[i-N], p[2][i-N]);
}
}
"
datalist2 <- list(
tp = ascus$TP,
dis = ascus$TP + ascus$FN,
tn = ascus$TN,
nondis = ascus$TN + ascus$FP,
N = 20,
x = structure(.Data=c(2-as.numeric(as.factor(ascus$Test)),
rev(2-as.numeric(as.factor(ascus$Test)))),
.Dim=c(20, 2)))
brma.2 <- stan(model_code = BRMA2,
data = datalist2,
chains = 3,
iter = 5000,
warmup = 1000,
thin = 10,
seed = 3,
cores=3)
The figure below shows the trace plots for all the six models fitted to the ascus data where all parameters, including the correlation parameter(except the BRMA) are modeled as a function of the covariate. There is proper chains mixing and convergence except for the case of the Clayton copula based bivariate beta.
f2.1 <- traceplot(fitgauss.2)
f2.2 <- traceplot(fitc90.2)
f2.3 <- traceplot(fitc270.2)
f2.4 <- traceplot(fitfgm.2)
f2.5 <- traceplot(fitfrank.2)
#draws <- as.array(brma.2, pars=c('MU'))
f2.6 <- rstan::stan_trace(brma.2, pars=c('MU'))
multiplot(f2.1, f2.2, f2.3, f2.4, f2.5, f2.6, cols=2)
The n\_eff in table below indicate substantial autocorrelation in sampling the correlation parameters except in the Gaussian',FGM’ and `Frank’ models. From the copula based bivariate beta distributions, it is apparent that the correlation between sensitivity and specificity in HC2 and repeat cytology is different.
brma.summary2 <- data.frame(Parameter=c(rep(c("Sensitivity", "Specificity"), times=2), "RSE", "RSP", "Var(lSens)",
"Cov(lSens_Spec)", "Cov(lSens_Spec)", "Var(lSpec)", "Correlation"),
Test=c(rep(c("HC2", "Repc"), times=2), rep("Repc", 2), rep("Both", 5)),
summary(brma.2, pars=c('MU', "RR", 'Sigma', 'ktau'))$summary[,c(1, 4, 6, 8:10)])
names(brma.summary2)[3:6] <- c("Mean", "Lower", "Median", "Upper")
Table2 <- cbind(Model=rep(c("Gaussian", "C90", "C270", "FGM", "Frank"), each=14),
Test=rep(c("HC2", "Repc"), length.out=70),
rbind(summary(fitgauss.2)$Summary,
summary(fitc90.2)$Summary,
summary(fitc270.2)$Summary,
summary(fitfgm.2)$Summary,
summary(fitfrank.2)$Summary),
WAIC = t(data.frame(rep(c(summary(fitgauss.2)$WAIC[1],
summary(fitc90.2)$WAIC[1],
summary(fitc270.2)$WAIC[1],
summary(fitfgm.2)$WAIC[1],
summary(fitfrank.2)$WAIC[1]), each=14))))
Table2$Parameter <- rep(rep(c("Sensitivity", "Specificity", "RSE", "RSP", "Correlation", "Var(Sens)", "Var(Spec)"), each=2), times=5)
Table2 <- rbind(Table2, cbind(Model=rep("BRMA", 11),
brma.summary2,
WAIC=t(data.frame(rep(loo::waic(extract_log_lik(brma.2, parameter_name="loglik"))[3], 11)))))
## Warning: 19 (47.5%) p_waic estimates greater than 0.4.
## We recommend trying loo() instead.
rownames(Table2) <- NULL
print(Table2[Table2$Parameter=="Correlation",], digits=4)
## Model Test Parameter Mean Lower Median Upper
## 9 Gaussian HC2 Correlation -0.43812 -0.9984 -6.959e-01 9.847e-01
## 10 Gaussian Repc Correlation -0.91991 -0.9997 -9.643e-01 -6.103e-01
## 23 C90 HC2 Correlation -0.06588 -0.7610 -1.039e-17 -7.624e-19
## 24 C90 Repc Correlation -0.85157 -0.9804 -9.120e-01 -4.906e-01
## 37 C270 HC2 Correlation -0.03038 -0.6452 -7.000e-18 -1.782e-18
## 38 C270 Repc Correlation -0.77847 -0.9757 -7.058e-01 -5.394e-01
## 51 FGM HC2 Correlation -0.07618 -0.2222 -2.215e-01 2.222e-01
## 52 FGM Repc Correlation -0.19819 -0.2222 -2.222e-01 1.894e-01
## 65 Frank HC2 Correlation -0.48806 -0.8140 -4.497e-01 1.000e+00
## 66 Frank Repc Correlation -0.73784 -0.8627 -7.275e-01 1.000e+00
## 81 BRMA Both Correlation -0.84808 -0.9839 -8.980e-01 -4.497e-01
## n_eff Rhat WAIC
## 9 154.238 1.0066 236.4
## 10 24.342 1.0542 236.4
## 23 30.089 1.0690 235.7
## 24 8.532 1.1128 235.7
## 37 76.410 1.0326 227.5
## 38 2.945 1.4613 227.5
## 51 2422.935 1.0007 245.1
## 52 2550.145 0.9997 245.1
## 65 2700.000 NaN 238.3
## 66 2700.000 NaN 238.3
## 81 102.983 1.0254 233.7
The Clayton90 model has the lowest WAIC even though sampling from the posterior distribution was difficult as seen in their trace plots in figure abover and the n_eff and Rhat in the table above. The difficulty in sampling from the posterior could be signalling over-parameterisation of the correlation structure. It would thus be interesting to re-fit the models using only one correlation parameter and compare the models. WAIC is known to fail in certain settings and this examples shows that it is crucial to check the adequacy of the fit and plausibility of the model and not blindly rely on an information criterion to select the best fit to the data.
From the posterior relative sensitivity and specificity plotted below, all the models that converged generally agree that repeat cytology was less sensitive than HC2 without significant loss in specificity.
g2 <- ggplot(Table2[Table2$Parameter %in% c("RSE", "RSP") & (Table2$Test == "Repc") ,],
aes(x=Model, y= Mean, ymax=1.5)) +
geom_point(size=3) +
theme_bw() +
coord_flip() +
facet_grid(~ Parameter, switch="x") +
geom_errorbar(aes(ymin=Lower, ymax=Upper),size=.75, width=0.15) +
geom_hline(aes(yintercept = 1),
linetype = "longdash",
colour="blue") +
theme(axis.text.x = element_text(size=13, colour='black'),
axis.text.y = element_text(size=13, colour='black'),
axis.title.x = element_text(size=13, colour='black'),
strip.text = element_text(size = 13, colour='black'),
axis.title.y= element_text(size=13, angle=0, colour='black'),
strip.text.y = element_text(size = 13, colour='black'),
strip.text.x = element_text(size = 13, colour='black'),
panel.grid.major = element_blank(),
panel.background = element_blank(),
strip.background = element_blank(),
plot.background = element_rect(fill = "white", colour='white'),
axis.line.x = element_line(color = 'black'),
axis.line.y = element_line(color = 'black')) +
scale_y_continuous(name="Mean [95% equal-tailed credible intervals]",
limits=c(0.5, 2))
g2
