This tutorial on advanced statistical methods for meta-analysis can be seen as a sequel to the recent Tutorial in Biostatistics on meta-analysis by Normand, which focused on elementary methods. Within the framework of the general linear mixed model using approximate likelihood, we discuss methods to analyse univariate as well as bivariate treatment effects in meta-analyses as well as meta-regression methods. Several extensions of the models are discussed, like exact likelihood, non-normal mixtures and multiple endpoints. We end with a discussion about the use of Bayesian methods in meta-analysis. All methods are illustrated by a meta-analysis concerning the efficacy of BCG vaccine against tuberculosis. All analyses that use approximate likelihood can be carried out by standard software. We demonstrate how the models can be fitted using SAS Proc Mixed.

In the absence of large, comparative randomized clinical trials, the minimally invasive techniques appear to be at least as effective as surgery in the treatment of lower extremity varicose veins.

The reported prevalence of depression in psoriasis varies substantially. This study aims to determine the prevalence and odds of depressive symptoms and clinical depression in psoriasis. A systematic literature search was conducted. Mean questionnaire values and proportions for depressive symptoms and clinical depression were pooled according to different assessment methods. In controlled studies, standardized mean differences (SMDs) and odds ratio (OR) compared depression in psoriasis patients with controls using the random-effect model. The majority of the 98 eligible studies were conducted in tertiary centers without a control group. The prevalence of depressive symptoms was 28% using questionnaires and the prevalence of clinical depression was 12% using International Classification of Diseases codes, 19% using Diagnostic and Statistical Manual of Mental Disorders IV, and 9% for antidepressant use. Psoriasis patients had significantly more depressive symptoms (SMD 1.16; 95% confidence interval (CI) 0.67-1.66), and population-based studies showed that they were at least one and a half times more likely to experience depression (OR 1.57; 95% CI 1.40-1.76) and used more antidepressants than did controls (OR 4.24, 95% CI 1.53-11.76). More than 10% of psoriasis patients suffer from clinical depression, and twice as many have depressive symptoms. The high prevalence of these symptoms is likely to be affected by the tertiary study populations and differential misclassification using questionnaires, where psoriasis-related symptoms may be detected as depressive symptoms.

Meta-analysis of receiver operating characteristic (ROC)-curve data is often done with fixed-effects models, which suffer many shortcomings. Some random-effects models have been proposed to execute a meta-analysis of ROC-curve data, but these models are not often used in practice. Straightforward modeling techniques for multivariate random-effects meta-analysis of ROC-curve data are needed. The 1st aim of this article is to present a practical method that addresses the drawbacks of the fixed-effects summary ROC (SROC) method of Littenberg and Moses. Sensitivities and specificities are analyzed simultaneously using a bivariate random-effects model. The 2nd aim is to show that other SROC curves can also be derived from the bivariate model through different characterizations of the estimated bivariate normal distribution. Thereby the authors show that the bivariate random-effects approach not only extends the SROC approach but also provides a unifying framework for other approaches. The authors bring the statistical meta-analysis of ROC-curve data back into a framework of relatively standard multivariate meta-analysis with random effects. The analyses were carried out using the software package SAS (Proc NLMIXED).