arXiv subjects

Christian Röver

Publications and source records attributed to Christian Röver.

54 records · Page 3Linked to original sources

arXiv2015

Changing EDSS progression in placebo cohorts in relapsing MS: A systematic review and meta-regression

Background: Recent systematic reviews of randomised controlled trials (RCTs) in relapsing multiple sclerosis (RMS) revealed a decrease in placebo annualized relapse rates (ARR) over the past two decades. Furthermore, regression to the mean effects were observed in ARR and MRI lesion counts. It is unclear whether disease progression measured by the expanded disability status scale (EDSS) exhibits similar features. Methods: A systematic review of RCTs in RMS was conducted extracting data on EDSS and baseline characteristics. The logarithmic odds of disease progression were modelled to investigate time trends. Random-effects models were used to account for between-study variability; all investigated models included trial duration as a predictor to correct for unequal study durations. Meta-regressions were conducted to assess the prognostic value of a number of baseline variables. Results: The systematic literature search identified 39 studies, including a total of 19,714 patients. The proportion of patients in placebo controls experiencing a disease progression decreased over the years (p<0.001). Meta regression identified associated covariates including the size of the study and its duration that in part explained the time trend. Progression probabilities tended to be lower in the second year compared to the first year with a reduction of 24% in progression probability from year 1 to year 2 (p=0.014). Conclusion: EDSS disease progression exhibits similar behaviour over time as the ARR and point to changes in trial characteristics over the years, questioning comparisons between historical and recent trials.

stat.AP↗

arXiv2015

Robust 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 analysed with non-spinning, 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 analysing 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.

gr-qc↗

arXiv2014

Evidence synthesis for count distributions based on heterogeneous and incomplete aggregated data

The analysis of count data is commonly done using Poisson models. Negative binomial models are a straightforward and readily motivated generalization for the case of overdispersed data, i.e., when the observed variance is greater than expected under a Poissonian model. Rate and overdispersion parameters then need to be considered jointly, which in general is not trivial. Here we are concerned with evidence synthesis in the case where the reporting of data is rather heterogeneous, i.e., events are reported either in terms of mean event counts, the proportion of event-free patients, or rate estimates and standard errors. Either figure carries some information about the relevant parameters, and it is the joint modeling that allows for coherent inference on the parameters of interest. The methods are motivated and illustrated by a systematic review in chronic obstructive pulmonary disease.

stat.ME↗

arXiv2014

Interleukin-2 receptor antagonists for pediatric liver transplant recipients: A systematic review and meta-analysis of controlled studies

Interleukin-2 receptor antagonists (IL-2RA) are frequently used as induction therapy in liver transplant recipients to decrease the risk of acute rejection while allowing the reduction of concomitant immunosuppression. The exact association with the use of IL-2RA however is uncertain. We performed a systematic literature search for relevant studies. Random effects models were used to assess the incidence of acute rejection, steroid-resistant rejection, graft loss, patient death, and adverse drug reaction, with or without IL-2RA. Six studies (2 randomized and 4 nonrandomized) met the elegibility criteria. Acute rejection at 6 months or later favored the use of IL-2RA significantly (relative risk (RR) 0.38; 95% confidence interval (CI) 0.22-0.66, p = 0.0005). Although not statistically significant, IL-2RA showed a substantial reduction of the risk of steroid-resistant rejection (RR 0.32; CI 0.19-1.03, p = 0.0594). Graft loss and patient death showed a reductive tendency through the use of IL-2RA. The use of IL-2RA is safe and is associated with a statistically significantly lower incidence of acute rejection after transplantation and substantial reduction of steroid-resistant rejection, graft loss and patient death.

q-bio.QM↗

arXiv2014

Predictability of IL-28B-polymorphism on protease-inhibitor-based triple-therapy in chronic HCV-genotype-1 patients: A meta-analysis

AIM: To investigate the predictability of interleukin-28B single nucleotide polymorphism rs12979860 with respect to sustained virological response (SVR) in chronically hepatitis C virus (HCV) genotype-1 patients treated with a protease-inhibitor and pegylated interferon-$α$ (Peg-INF-$α$) based triple-therapy. METHODS: We searched PubMed, the Cochrane Library and Web of Knowledge for studies regarding the interleukin 28B (IL-28B)-genotype and protease-inhibitor based triple-therapy. Ten studies with 2707 patients were included into this meta-analysis. We used regression methods in order to investigate determinants of SVR. RESULTS: IL-28B-CC-genotype patients achieved higher SVR rates (odds 5.34, CI: 3.81-7.49) than IL-28B-non-CC-genotype patients (1.88, CI: 1.43-2.48) receiving triple-therapy. The line of therapy (treatment-naïve or -experienced for Peg-INF-$α$) did not affect the predictive value of IL-28B (p=0.1). IL-28B-CC-genotype patients treated with protease inhibitor-based triple-therapy consisting of Boceprevir, Simeprevir, Telaprevir or Vaniprevir showed odds of 1.86, 9.77, 4.51 and 0.89, respectively. The odds for CC genotype patients treated with Faldaprevir cannot be quantified, as only a single study with a 100% SVR rate was available. CONCLUSION: IL-28B-SNP predicts the outcome for chronic HCV genotype-1 patients receiving protease inhibitor-based triple-therapy. The predictive value varies between the different protease inhibitors.

q-bio.QM↗

arXiv2014

Explaining temporal trends in annualized relapse rates in placebo groups of randomized controlled trials in relapsing multiple sclerosis: systematic review and meta-regression

Background: Recent studies have shown a decrease in annualised relapse rates (ARRs) in placebo groups of randomised controlled trials (RCTs) in relapsing multiple sclerosis (RMS). Methods: We conducted a systematic literature search of RCTs in RMS. Data on eligibility criteria and baseline characteristics were extracted and tested for significant trends over time. A meta-regression was conducted to estimate their contribution to the decrease of trial ARRs over time. Results: We identified 56 studies. Patient age at baseline (p < 0.001), mean duration of multiple sclerosis (MS) at baseline (p = 0.048), size of treatment groups (p = 0.003), Oxford Quality Scale scores (p = 0.021), and the number of eligibility criteria (p<0.001) increased significantly, whereas pre-trial ARR (p = 0.001), the time span over which pre-trial ARR was calculated (p < 0.001), and the duration of placebo-controlled follow-up (p = 0.006) decreased significantly over time. In meta-regression of trial placebo ARR, the temporal trend was found to be insignificant, with major factors explaining the variation: pre-trial ARR, the number of years used to calculate pre-trial ARR and study duration. Conclusion: The observed decline in trial ARRs may result from decreasing pre-trial ARRs and a shorter time period over which pre-trial ARRs were calculated. Increasing patient age and duration of illness may also contribute.

stat.AP↗

arXiv2013

Bayesian inference on EMRI signals using low frequency approximations

Extreme mass ratio inspirals (EMRIs) are thought to be one of the most exciting gravitational wave sources to be detected with LISA. Due to their complicated nature and weak amplitudes the detection and parameter estimation of such sources is a challenging task. In this paper we present a statistical methodology based on Bayesian inference in which the estimation of parameters is carried out by advanced Markov chain Monte Carlo (MCMC) algorithms such as parallel tempering MCMC. We analysed high and medium mass EMRI systems that fall well inside the low frequency range of LISA. In the context of the Mock LISA Data Challenges, our investigation and results are also the first instance in which a fully Markovian algorithm is applied for EMRI searches. Results show that our algorithm worked well in recovering EMRI signals from different (simulated) LISA data sets having single and multiple EMRI sources and holds great promise for posterior computation under more realistic conditions. The search and estimation methods presented in this paper are general in their nature, and can be applied in any other scenario such as AdLIGO, AdVIRGO and Einstein Telescope with their respective response functions.

gr-qc↗

arXiv2011

A Student-t based filter for robust signal detection

The search for gravitational-wave signals in detector data is often hampered by the fact that many data analysis methods are based on the theory of stationary Gaussian noise, while actual measurement data frequently exhibit clear departures from these assumptions. Deriving methods from models more closely reflecting the data's properties promises to yield more sensitive procedures. The commonly used matched filter is such a detection method that may be derived via a Gaussian model. In this paper we propose a generalized matched-filtering technique based on a Student-t distribution that is able to account for heavier-tailed noise and is robust against outliers in the data. On the technical side, it generalizes the matched filter's least-squares method to an iterative, or adaptive, variation. In a simplified Monte Carlo study we show that when applied to simulated signals buried in actual interferometer noise it leads to a higher detection rate than the usual ("Gaussian") matched filter.

physics.data-an↗

arXiv2011

Bayesian versus frequentist upper limits

While gravitational waves have not yet been measured directly, data analysis from detection experiments commonly includes an upper limit statement. Such upper limits may be derived via a frequentist or Bayesian approach; the theoretical implications are very different, and on the technical side, one notable difference is that one case requires maximization of the likelihood function over parameter space, while the other requires integration. Using a simple example (detection of a sinusoidal signal in white Gaussian noise), we investigate the differences in performance and interpretation, and the effect of the "trials factor", or "look-elsewhere effect".

physics.data-an↗

arXiv2010

Modelling coloured residual noise in gravitational-wave signal processing

We introduce a signal processing model for signals in non-white noise, where the exact noise spectrum is a priori unknown. The model is based on a Student's t distribution and constitutes a natural generalization of the widely used normal (Gaussian) model. This way, it allows for uncertainty in the noise spectrum, or more generally is also able to accommodate outliers (heavy-tailed noise) in the data. Examples are given pertaining to data from gravitational wave detectors.

stat.ME↗

arXiv2009

Random template placement and prior information

In signal detection problems, one is usually faced with the task of searching a parameter space for peaks in the likelihood function which indicate the presence of a signal. Random searches have proven to be very efficient as well as easy to implement, compared e.g. to searches along regular grids in parameter space. Knowledge of the parameterised shape of the signal searched for adds structure to the parameter space, i.e., there are usually regions requiring to be densely searched while in other regions a coarser search is sufficient. On the other hand, prior information identifies the regions in which a search will actually be promising or may likely be in vain. Defining specific figures of merit allows one to combine both template metric and prior distribution and devise optimal sampling schemes over the parameter space. We show an example related to the gravitational wave signal from a binary inspiral event. Here the template metric and prior information are particularly contradictory, since signals from low-mass systems tolerate the least mismatch in parameter space while high-mass systems are far more likely, as they imply a greater signal-to-noise ratio (SNR) and hence are detectable to greater distances. The derived sampling strategy is implemented in a Markov chain Monte Carlo (MCMC) algorithm where it improves convergence.

gr-qc↗

arXiv2009

Bayesian reconstruction of gravitational wave burst signals from simulations of rotating stellar core collapse and bounce

Presented in this paper is a technique that we propose for extracting the physical parameters of a rotating stellar core collapse from the observation of the associated gravitational wave signal from the collapse and core bounce. Data from interferometric gravitational wave detectors can be used to provide information on the mass of the progenitor model, precollapse rotation and the nuclear equation of state. We use waveform libraries provided by the latest numerical simulations of rotating stellar core collapse models in general relativity, and from them create an orthogonal set of eigenvectors using principal component analysis. Bayesian inference techniques are then used to reconstruct the associated gravitational wave signal that is assumed to be detected by an interferometric detector. Posterior probability distribution functions are derived for the amplitudes of the principal component analysis eigenvectors, and the pulse arrival time. We show how the reconstructed signal and the principal component analysis eigenvector amplitude estimates may provide information on the physical parameters associated with the core collapse event.

gr-qc↗

arXiv2009

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.

gr-qc↗

arXiv2009

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.

gr-qc↗

arXiv2007

Inference on inspiral signals using LISA MLDC data

In this paper we describe a Bayesian inference framework for analysis of data obtained by LISA. We set up a model for binary inspiral signals as defined for the Mock LISA Data Challenge 1.2 (MLDC), and implemented a Markov chain Monte Carlo (MCMC) algorithm to facilitate exploration and integration of the posterior distribution over the 9-dimensional parameter space. Here we present intermediate results showing how, using this method, information about the 9 parameters can be extracted from the data.

gr-qc↗

arXiv2007

Coherent Bayesian analysis of inspiral signals

We present in this paper a Bayesian parameter estimation method for the analysis of interferometric gravitational wave observations of an inspiral of binary compact objects using data recorded simultaneously by a network of several interferometers at different sites. We consider neutron star or black hole inspirals that are modeled to 3.5 post-Newtonian (PN) order in phase and 2.5 PN in amplitude. Inference is facilitated using Markov chain Monte Carlo methods that are adapted in order to efficiently explore the particular parameter space. Examples are shown to illustrate how and what information about the different parameters can be derived from the data. This study uses simulated signals and data with noise characteristics that are assumed to be defined by the LIGO and Virgo detectors operating at their design sensitivities. Nine parameters are estimated, including those associated with the binary system, plus its location on the sky. We explain how this technique will be part of a detection pipeline for binary systems of compact objects with masses up to $20 \sunmass$, including cases where the ratio of the individual masses can be extreme.

gr-qc↗

arXiv2007

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

Presented in this paper is 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 data from multiple detectors. The MCMC technique uses data from several interferometers and infers all nine of the parameters (ignoring spin) associated with the binary system, including the distance to the source, the masses, and the location on the sky. The Metropolis-algorithm utilises advanced MCMC techniques, such as importance resampling and parallel tempering. The data is compared with time-domain inspiral templates that are 2.5 post-Newtonian (PN) in phase and 2.0 PN in amplitude. 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.

gr-qc↗

arXiv2006

Bayesian inference on compact binary inspiral gravitational radiation signals in interferometric data

Presented is a description of a Markov chain Monte Carlo (MCMC) parameter estimation routine for use with interferometric gravitational radiational data in searches for binary neutron star inspiral signals. Five parameters associated with the inspiral can be estimated, and summary statistics are produced. Advanced MCMC methods were implemented, including importance resampling and prior distributions based on detection probability, in order to increase the efficiency of the code. An example is presented from an application using realistic, albeit fictitious, data.

gr-qc↗