76 research outputs found
Analysing the course of multiple sclerosis with segmented regression models
No full textMultiple sclerosis (MS) is a demyelinating disease of the central nervous system whose cause is still unknown. The disease course shows great inter- and intra-individual variability and this results in insecurity of diagnosis and prognosis. A well-founded knowledge of the natural history of MS, however, is an important prerequisite for developing adequate strategies for therapy and research. In order to increase our understanding we developed a segmented regression model which extracts three main characteristics of the time course of this complex disease from natural history data. For each individual patient this model determines baseline disability (as measured by the Expanded Disability Status Scale = EDSS), the time point where the disease starts to progress and the slope of this progression. The model is applied to data of patient registries from all over the world that are pooled in the database of the Sylvia Lawry Centre for Multiple Sclerosis Research (SLCMSR). The analyses used a random subsample of the entire database and were restricted to patients seen from onset of MS with time series of at least three years. Thereby we were able to avoid some of the problems related to missing data. Our results revealed a weak negative correlation between time to progression (change point) and slope of progression for this group of patients, i.e. those patients who do progressed later and remained stable for a longer time developed disability more slowly than those who progressed earlier. For the two parameters and their interaction we did not find an influence of basic covariates like gender, disease course and mono- or poly-symptomatic disease onset. According to the SLCMSR Policy these results will be subjected to a validation using an independent "validation dataset". This remains to be done
Reducing the Probability of False Positive Research Findings by Pre-Publication Validation - Experience with a Large Multiple Sclerosis Database
No full text*Objective*
We have assessed the utility of a pre-publication validation policy in reducing the probability of publishing false positive research findings. 
*Study design and setting*
The large database of the Sylvia Lawry Centre for Multiple Sclerosis Research was split in two parts: one for hypothesis generation and a validation part for confirmation of selected results. We present case studies from 5 finalized projects that have used the validation policy and results from a simulation study.
*Results*
In one project, the "relapse and disability" project as described in section II (example 3), findings could not be confirmed in the validation part of the database. The simulation study showed that the percentage of false positive findings can exceed 20% depending on variable selection. 
*Conclusion*
We conclude that the validation policy has prevented the publication of at least one research finding that could not be validated in an independent data set (and probably would have been a "true" false-positive finding) over the past three years, and has led to improved data analysis, statistical programming, and selection of hypotheses. The advantages outweigh the lost statistical power inherent in the process
Evaluating microarray-based classifiers: an overview
No full textFor the last eight years, microarray-based class prediction has been the subject of numerous publications in medicine, bioinformatics and statistics journals. However, in many articles, the assessment of classification accuracy is carried out using suboptimal procedures and is not paid much attention. In this paper, we carefully review various statistical aspects of classifier evaluation and validation from a practical point of view. The main topics addressed are accuracy measures, error rate estimation procedures, variable selection, choice of classifiers and validation strategy
Treating Systematic Errors in Multiple Sclerosis Data
No full textMultiple sclerosis (MS) is characterized by high variability between patients and, more importantly here, within an individual over time. This makes categorization and prognosis difficult. Moreover, it is unclear to what degree this intra-individual variation reflects the long-term course of irreversible disability and what is attributable to short-term processes such as relapses, to interrater variability and to measurement error. Any investigation and prediction of the medium or long term evolution of irreversible disability in individual patients is therefore confronted with the problem of systematic error in addition to random fluctuations. The approach described in this article aims to assist in detecting relapses in disease curves and in identifying the underlying disease course. To this end neurological knowledge was transformed into simple rules which were then implemented into computer algorithms for pre-editing disease curves. Based on simulations it is shown that pre-editing time series of disability measured with the Expanded Disability Status Scale (EDSS) can lead to more robust and less biased estimates for important disease characteristics, such as baseline EDSS and time to reach certain EDSS levels or sustained progression
CMA - A comprehensive Bioconductor package for supervised classification with high dimensional data
No full textFor the last eight years, microarray-based class prediction has been a major topic in statistics, bioinformatics and biomedicine research. Traditional methods often yield unsatisfactory results or may even be inapplicable in the p > n setting where the number of predictors by far exceeds the number of observations, hence the term “ill-posed-problem”. Careful model selection and evaluation satisfying accepted good-practice standards is a very complex task for inexperienced users with limited statistical background or for statisticians without experience in this area. The multiplicity of available methods for class prediction based on high-dimensional data
is an additional practical challenge for inexperienced researchers. In this article, we introduce a new Bioconductor package called CMA (standing for “Classification for MicroArrays”) for automatically performing variable selection, parameter tuning, classifier construction, and unbiased evaluation of the constructed classifiers using a large number of usual methods. Without much time and effort, users are provided with an overview of the unbiased accuracy of most top-performing classifiers. Furthermore, the standardized evaluation framework underlying CMA can also be beneficial in statistical research for comparison purposes, for instance if a new classifier has to be compared to existing approaches. CMA is a user-friendly comprehensive package for classifier construction and evaluation implementing most usual approaches. It is freely available from the Bioconductor website at http://bioconductor.org/packages/2.3/bioc/html/CMA.html
Algorithm ranking (outdoor ecological validity).
No full text<p>Individual coverage probability with a maximum difference of 0.1 m/s (CP1) to 0.3 m/s (CP3) as well as concordance correlation coefficient (CCC) for each algorithm across all speed levels.</p
Algorithm ranking (indoor ecological validation including running).
No full text<p>Individual coverage probability with a maximum difference of 0.1 m/s (CP1) to 0.3 m/s (CP3) as well as concordance correlation coefficient (CCC) including 95% confidence intervals (95% CI) for each algorithm across all speed levels.</p
On-line monitoring using Multi-Process Kalman Filtering
No full textOn-line monitoring of time series becomes more and more important in different areas of application like medicine, biometry and finance. In medicine, on-line monitoring of patients after transplantation of renals (Smith83) is an easy and prominent example. In finance, fast end reliable recognition of changes in level and trend of intra-daily stock market prices is of obvious interest for ordering and purchasing. In this project, we currently consider monitoring of surgical data like heart-rate, blood pressure and oxygenation. From a statistical point of view, on-line monitoring can be considered as on-line detection of changepoints in time series. That means, changepoints have to be detected in real time as new observations come in, usually in short time intervals. Retrospective detection of changepoints, after the whole batch of observations has been recorded, is nice but useless in monitoring patients during an operation.
There are various statistical approaches conceivable for on-line detection of changepoints in time series. Dynamic or state space models seem particularly well suited because ``filtering'' has historically been developed exactly for on-line estimation of the ``state'' of some system. Our approach is based on a recent extension of the so-called multi-process Kalman filter for changepoint detection (Schnatter94). It turned out, however, that some important issues for adequate and reliable application have to be considered, in particular the (appropriate) handling of outliers and, as a central point, adaptive on-line estimation of control- or hyper-parameters. In this paper, we describe a filter model that has this features and can be implemented in such a way that it is useful for real time applications with high frequency time series data.
Recently, simulation based methods for estimation of non-Gaussian dynamic models have been proposed that may also be adapted and generalized for the purpose of changepoint detection. Most of them solve the smoothing problem, but very recently some proposals have been made that could be useful also for filtering and, thus, for on-line monitoring (Kitagawa96a,Kitagawa96b,Shephard96). If these approaches are a useful alternative to our development needs a careful comparison in future and is beyond the scope of this paper
Development and validation of a new method to measure walking speed in free-living environments using the actibelt® platform.
No full textWalking speed is a fundamental indicator for human well-being. In a clinical setting, walking speed is typically measured by means of walking tests using different protocols. However, walking speed obtained in this way is unlikely to be representative of the conditions in a free-living environment. Recently, mobile accelerometry has opened up the possibility to extract walking speed from long-time observations in free-living individuals, but the validity of these measurements needs to be determined. In this investigation, we have developed algorithms for walking speed prediction based on 3D accelerometry data (actibelt®) and created a framework using a standardized data set with gold standard annotations to facilitate the validation and comparison of these algorithms. For this purpose 17 healthy subjects operated a newly developed mobile gold standard while walking/running on an indoor track. Subsequently, the validity of 12 candidate algorithms for walking speed prediction ranging from well-known simple approaches like combining step length with frequency to more sophisticated algorithms such as linear and non-linear models was assessed using statistical measures. As a result, a novel algorithm employing support vector regression was found to perform best with a concordance correlation coefficient of 0.93 (95%CI 0.92-0.94) and a coverage probability CP1 of 0.46 (95%CI 0.12-0.70) for a deviation of 0.1 m/s (CP2 0.78, CP3 0.94) when compared to the mobile gold standard while walking indoors. A smaller outdoor experiment confirmed those results with even better coverage probability. We conclude that walking speed thus obtained has the potential to help establish walking speed in free-living environments as a patient-oriented outcome measure
Speed levels.
No full text<p>Mean gait speed in m/s and standard deviation (sd) for each speed level as measured by the mobile gold standard.</p
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