Christian Röver

Efficacy of treatments for anxiety disorders: a meta-analysis.

To our knowledge, no previous meta-analysis has attempted to compare the efficacy of pharmacological, psychological and combined treatments for the three main anxiety disorders (panic disorder, generalized anxiety disorder and social phobia). Pre–post and treated versus control effect sizes (ES) were calculated for all evaluable randomized-controlled studies (n=234), involving 37 333 patients. Medications were associated with a significantly higher average pre–post ES [Cohen’s d=2.02 (1.90–2.15); 28 051 patients] than psychotherapies [1.22 (1.14–1.30); 6992 patients; P<0.0001]. ES were 2.25 for serotonin–noradrenaline reuptake inhibitors (n=23 study arms), 2.15 for benzodiazepines (n=42), 2.09 for selective serotonin reuptake inhibitors (n=62) and 1.83 for tricyclic antidepressants (n=15). ES for psychotherapies were mindfulness therapies, 1.56 (n=4); relaxation, 1.36 (n=17); individual cognitive behavioural/exposure therapy (CBT), 1.30 (n=93); group CBT, 1.22 (n=18); psychodynamic therapy 1.17 (n=5); therapies without face-to-face contact (e.g. Internet therapies), 1.11 (n=34); eye movement desensitization reprocessing, 1.03 (n=3); and interpersonal therapy 0.78 (n=4). The ES was 2.12 (n=16) for CBT/drug combinations. Exercise had an ES of 1.23 (n=3). For control groups, ES were 1.29 for placebo pills (n=111), 0.83 for psychological placebos (n=16) and 0.20 for waitlists (n=50). In direct comparisons with control groups, all investigated drugs, except for citalopram, opipramol and moclobemide, were significantly more effective than placebo. Individual CBT was more effective than waiting list, psychological placebo and pill placebo. When looking at the average pre–post ES, medications were more effective than psychotherapies. Pre–post ES for psychotherapies did not differ from pill placebos; this finding cannot be explained by heterogeneity, publication bias or allegiance effects. However, the decision on whether to choose psychotherapy, medications or a combination of the two should be left to the patient as drugs may have side effects, interactions and contraindications.

Testing gravitational-wave searches with numerical relativity waveforms: results from the first Numerical INJection Analysis (NINJA) project

The Numerical INJection Analysis (NINJA) project is a collaborative effort between members of the numerical relativity and gravitational-wave data analysis communities. The purpose of NINJA is to study the sensitivity of existing gravitational-wave search algorithms using numerically generated waveforms and to foster closer collaboration between the numerical relativity and data analysis communities. We describe the results of the first NINJA analysis which focused on gravitational waveforms from binary black hole coalescence. Ten numerical relativity groups contributed numerical data which were used to generate a set of gravitational-wave signals. These signals were injected into a simulated data set, designed to mimic the response of the initial LIGO and Virgo gravitational-wave detectors. Nine groups analysed this data using search and parameter-estimation pipelines. Matched filter algorithms, un-modelled-burst searches and Bayesian parameter estimation and model-selection algorithms were applied to the data. We report the efficiency of these search methods in detecting the numerical waveforms and measuring their parameters. We describe preliminary comparisons between the different search methods and suggest improvements for future NINJA analyses.

Parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library

The Advanced LIGO and Advanced Virgo gravitational-wave (GW) detectors will begin operation in the coming years, with compact binary coalescence events a likely source for the first detections. The gravitational waveforms emitted directly encode information about the sources, including the masses and spins of the compact objects. Recovering the physical parameters of the sources from the GW observations is a key analysis task. This work describes the LALInference software library for Bayesian parameter estimation of compact binary signals, which builds on several previous methods to provide a well-tested toolkit which has already been used for several studies. We show that our implementation is able to correctly recover the parameters of compact binary signals from simulated data from the advanced GW detectors. We demonstrate this with a detailed comparison on three compact binary systems: a binary neutron star, a neutron star–black hole binary and a binary black hole, where we show a cross comparison of results obtained using three independent sampling algorithms. These systems were analyzed with nonspinning, aligned spin and generic spin configurations respectively, showing that consistent results can be obtained even with the full 15-dimensional parameter space of the generic spin configurations. We also demonstrate statistically that the Bayesian credible intervals we recover correspond to frequentist confidence intervals under correct prior assumptions by analyzing a set of 100 signals drawn from the prior. We discuss the computational cost of these algorithms, and describe the general and problem-specific sampling techniques we have used to improve the efficiency of sampling the compact binary coalescence parameter space.

Parameter estimation of spinning binary inspirals using Markov-chain Monte Carlo

We present a Markov chain Monte Carlo (MCMC) technique to study the source parameters of gravitational-wave signals from the inspirals of stellar-mass compact binaries detected with ground-based gravitational-wave detectors such as LIGO and Virgo, for the case where spin is present in the more massive compact object in the binary. We discuss the aspects of the MCMC algorithm that allow us to sample the parameter space in an efficient way. We show sample runs that illustrate the possibilities of our MCMC code and the difficulties that we encounter.

Coherent Bayesian inference on compact binary inspirals using a network of interferometric gravitational wave detectors

Presented in this paper is the description of a Markov chain Monte Carlo (MCMC) routine for conducting coherent parameter estimation for interferometric gravitational wave observations of an inspiral of binary compact objects using multiple detectors. Data from several interferometers are processed, and all nine parameters (ignoring spin) associated with the binary system are inferred, including the distance to the source, the masses, and the location on the sky. The data is matched with time-domain inspiral templates that are 2.5 post-Newtonian (PN) in phase and 2.0 PN in amplitude. We designed and tuned an MCMC sampler so that it is able to efficiently find the posterior mode(s) in the parameter space and perform the stochastic integration necessary for inference within a Bayesian framework. Our routine could be implemented as part of an inspiral detection pipeline for a world-wide network of detectors. Examples are given for simulated signals and data as seen by the LIGO and Virgo detectors operating at their design sensitivity.

Extensive acute axonal damage in pediatric multiple sclerosis lesions

Axonal damage occurs early in multiple sclerosis (MS) and contributes to the degree of clinical disability. Children with MS more often show disabling and polyfocal neurological symptoms at disease onset than adults with MS. Thus, axonal damage may differ between pediatric and adult MS patients.

Meta-analysis of few small studies in orphan diseases

Meta‐analyses in orphan diseases and small populations generally face particular problems, including small numbers of studies, small study sizes and heterogeneity of results. However, the heterogeneity is difficult to estimate if only very few studies are included. Motivated by a systematic review in immunosuppression following liver transplantation in children, we investigate the properties of a range of commonly used frequentist and Bayesian procedures in simulation studies. Furthermore, the consequences for interval estimation of the common treatment effect in random‐effects meta‐analysis are assessed. The Bayesian credibility intervals using weakly informative priors for the between‐trial heterogeneity exhibited coverage probabilities in excess of the nominal level for a range of scenarios considered. However, they tended to be shorter than those obtained by the Knapp–Hartung method, which were also conservative. In contrast, methods based on normal quantiles exhibited coverages well below the nominal levels in many scenarios. With very few studies, the performance of the Bayesian credibility intervals is of course sensitive to the specification of the prior for the between‐trial heterogeneity. In conclusion, the use of weakly informative priors as exemplified by half‐normal priors (with a scale of 0.5 or 1.0) for log odds ratios is recommended for applications in rare diseases.

Status of NINJA: the Numerical INJection Analysis project

The 2008 NRDA conference introduced the Numerical INJection Analysis project (NINJA), a new collaborative effort between the numerical relativity community and the data analysis community. NINJA focuses on modeling and searching for gravitational wave signatures from the coalescence of binary system of compact objects. We review the scope of this collaboration and the components of the first NINJA project, where numerical relativity groups, shared waveforms and data analysis teams applied various techniques to detect them when embedded in colored Gaussian noise.

Degeneracies in sky localization determination from a spinning coalescing binary through gravitational wave observations: a Markov-chain Monte Carlo analysis for two detectors

Gravitational-wave signals from inspirals of binary compact objects (black holes and neutron stars) are primary targets of the ongoing searches by groundbased gravitational-wave interferometers (LIGO, Virgo and GEO-600). We present parameter-estimation simulations for inspirals of black-hole–neutronstar binaries using Markov-chain Monte Carlo methods. As a specific example of the power of these methods, we consider source localization in the sky and analyze the degeneracy in it when data from only two detectors are used. We focus on the effect that the black-hole spin has on the localization estimation. We also report on a comparative Markov-chain Monte Carlo analysis with two different waveform families, at 1.5 and 3.5 post-Newtonian orders.

Gravitational-wave astronomy with inspiral signals of spinning compact-object binaries

Inspiral signals from binary compact objects (black holes and neutron stars) are primary targets of the ongoing searches by ground-based gravitational-wave interferometers (LIGO, Virgo, and GEO-600). We present parameter-estimation simulations for inspirals of black hole-neutron star binaries using Markov Chain Monte Carlo methods. For the first time, we both estimated the parameters of a binary inspiral source with a spinning, precessing component and determined the accuracy of the parameter estimation, for simulated observations with ground-based gravitational-wave detectors. We demonstrate that we can obtain the distance, sky position, and binary orientation at a higher accuracy than previously suggested in the literature. For an observation of an inspiral with sufficient spin and two or three detectors we find an accuracy in the determination of the sky position of the order of tens of square degrees.