Robust online signal extraction from multivariate time series

We introduce robust regression-based online filters for multivariate time series and discuss their performance in real time signal extraction settings. We focus on methods that can deal with time series exhibiting patterns such as trends, level changes, outliers and a high level of noise as well as periods of a rather steady state. In particular, the data may be measured on a discrete scale which often occurs in practice. Our new filter is based on a robust two-step online procedure. We investigate its relevant properties and its performance by means of simulations and a medical application. --Multivariate time series,signal extraction,robust regression,online methods

Robust detail-preserving signal extraction

We discuss robust filtering procedures for signal extraction from noisy time series. Particular attention is paid to the preservation of relevant signal details like abrupt shifts. moving averages and running medians are widely used but have shortcomings when large spikes (outliers) or trends occur. Modifications like modified trimmed means and linear median hybrid filters combine advantages of both approaches, but they do not completely overcome the difficulties. Better solutions can be based on robust regression techniques, which even work in real time because of increased computational power and faster algorithms. Reviewing previous work we present filters for robust signal extraction and discuss their merits for preserving trends, abrupt shifts and local extremes as well as for the removal of outliers. --

Dimension reduction for time series from intensive care

A modified version of principal component analysis (PCA) for time series is investigated. The approach is in the frequency domains as in Brillinger (1975). Available knowledge on the subject matter can be incorporated via rotational methods. This eases the interpretation of the obtained component series. An application to the hemodynamic data from intensive care yields clinically meaningful component series of low dimension. We describe the results from this application and compare them with those obtained standard PCA. --

Statistische Extraktion relevanter Information aus multivariaten Online-Monitoring-Daten der Intensivmedizin

In dieser Dissertation wird mit Hilfe multivariater statistischer Verfahren untersucht, wie im Online-Monitoring in der Intensivmedizin klinisch relevante Information aus Beobachtungen für Variablen des Herz-Kreislaufsystems extrahiert werden kann. Insbesondere ist eine Reduktion der erhobenen Daten auf wesentliche Informationen angestrebt. Zur Bearbeitung der Fragestellung werden zunächst dimensionsreduzierende Verfahren für multivariate Zeitreihen, wie Techniken der statischen und dynamischen Faktoranalyse bzw. Hauptkomponentenanalyse, herangezogen und hinsichtlich einer Anwendung im Intensivmonitoring diskutiert. Es wird gezeigt, dass sich die intensivmedizinischen Zeitreihen aufgrund von ausgeprägten strukturellen Mustern, wie Trends, spontanen Niveauänderungen und Ausreißern, global nicht durch ein einziges Modell beschreiben lassen. Faktoranalytische Ansätze sind nicht geeignet, da eine vernünftige Modellanpassung an die Daten nur lokal möglich ist und dabei meist sehr aufwendig ist. Für die Überwachung hochkomplexer physiologischer Vorgänge in Echtzeit werden möglichst einfache Methoden benötigt, die interpretierbare Rückschlüsse auf den Zustand der Patienten ermöglichen. Wenige gemeinsame statische Hauptkomponenten der hämodynamischen Beobachtungen erfassen bei einer guten Interpretierbarkeit einen größeren Anteil der Gesamtvarianz als eine subjektive Variablenselektion, enthalten jedoch nicht notwendigerweise kontinuierlich sämtliche klinisch relevante Information. Einen nützlichen Ansatz zur Informationsextraktion aus multivariaten hämodynamischen Zeitreihen bieten multivariate Signalextraktionsverfahren. In einem ersten Schritt werden hierbei glatte Signale, die die klinisch relevanten Strukturänderungen der Zeitreihen enthalten, von Beobachtungsrauschen und Artefakten getrennt. So werden die aufgezeichneten Monitor-Daten auf wesentliche Informationen reduziert. Aufgrund der diskreten Messung der klinischen Variablen ist eine lokale Anwendung von bekannten, affin äquivarianten und hochrobusten Regressionstechniken auf kleine Stichproben in jedem Zeitfenster im Online-Monitoring nicht möglich. Daher wird eine neue multivariate Signalextraktionsprozedur vorgeschlagen, die für die vorliegenden Daten eine praktikable Lösung darstellt. Die extrahierten Signale geben kontinuierlich Aufschluss über das lokale Niveau der betrachteten Vitalparameter, eine klinisch relevante Information. Zusätzlich können die in den Zeitreihen gefundenen lokalen Strukturen weiter genutzt werden, um schließlich mit Hilfe medizinischen Wissens Aussagen über den Zustand eines Intensivpatienten zu treffen.This doctoral thesis is concerned with online monitoring data from intensive care medicine. The problem under investigation is the extraction of clinically relevant information from haemodynamic vital parameters by means of multivariate statistical methods. First, statistical methods for dimension reduction of multivariate time series, such as static and dynamic factor analysis or principal components analysis, are used. These are discussed with respect to application in intensive care monitoring. Time series in intensive care are found to show strong patterns, like periods of relative constancy, monotonic trends, abrupt level shifts and many measurement artefacts. Thus, global modelling of the data with time-invariant models or parameters is not feasible. Local modelling of the data by dynamic factor models is a complex task that involves a large number of unknown parameters. However, online monitoring of highly complex physiologic processes requires relatively simple methods that allow to draw interpretable conclusions about a patient's health state. Only a few common static principal components are required in order to explain the largest amount of total variability for the haemodynamic observations. Compared to a subjective variable selection these components give a better description of the data while being interpretable to the physician. A drawback is that, locally, these principal components do not necessarily contain all clinically relevant information. A useful approach for the extraction of relevant information from haemodynamic time series is given by methods of multivariate signal extraction. Here, smooth signals that contain the clinically relevant structure are separated from observational noise and artefacts. Thus, the recorded monitoring data are reduced to the relevant information. Robust multivariate regression functionals are needed for local trend approximation in a short moving time window. As the vital parameters are measured on a discrete scale with many patterns of relative constancy, local application of known, affine equivariant and highly robust techniques of regression on small samples is not possible. Therefore, a new procedure is proposed that offers a fast, highly robust and reliable solution for multivariate online signal extraction in intensive care. The resulting signals continuously present the local level of the vital parameters. Additionally, the local patterns found in the time series can be used to decide on the patient's health state by means of clinical knowledge

Rationale, Design and Baseline Characteristics of Participants in the Cardiovascular Outcomes for People Using Anticoagulation Strategies (COMPASS) Trial

Long-term aspirin prevents vascular events but is only modestly effective. Rivaroxaban alone or in combination with aspirin might be more effective than aspirin alone for vascular prevention in patients with stable coronary artery disease (CAD) or peripheral artery disease (PAD). Rivaroxaban as well as aspirin increase upper gastrointestinal (GI) bleeding and this might be prevented by proton pump inhibitor therapy. Cardiovascular Outcomes for People Using Anticoagulation Strategies (COMPASS) is a double-blind superiority trial comparing rivaroxaban 2.5 mg twice daily combined with aspirin 100 mg once daily or rivaroxaban 5 mg twice daily vs aspirin 100 mg once daily for prevention of myocardial infarction, stroke, or cardiovascular death in patients with stable CAD or PAD. Patients not taking a proton pump inhibitor were also randomized, using a partial factorial design, to pantoprazole 40 mg once daily or placebo. The trial was designed to have at least 90% power to detect a 20% reduction in each of the rivaroxaban treatment arms compared with aspirin and to detect a 50% reduction in upper GI complications with pantoprazole compared with placebo. Between February 2013 and May 2016, we recruited 27,395 participants from 602 centres in 33 countries; 17,598 participants were included in the pantoprazole vs placebo comparison. At baseline, the mean age was 68.2 years, 22.0% were female, 90.6% had CAD, and 27.3% had PAD. COMPASS will provide information on the efficacy and safety of rivaroxaban, alone or in combination with aspirin, in the long-term management of patients with stable CAD or PAD, and on the efficacy and safety of pantoprazole in preventing upper GI complications in patients receiving antithrombotic therapy

Online monitoring of high-dimensional physiological time series - a case-study

In modern statistical process control, intelligent alarm systems have to be constructed which extract the important information from multivariate time series and detect critical "out-of control " states of the underlying mechanism quickly and reliably. Regarding high-dimensional time series, statistical methods for dimension reduction can help to compress the data into a few relevant variables before characteristic patterns in the data are searched for. In this paper we apply graphical models as a preliminary step preceding a factor analysis of the vital signs of critically ill patients in intensive care. Then a procedure for the online-detection of change points in univariate time series is applied to the original series and to each of the factors and the results are compared to the judgment of an experienced physician

Why estimands are needed to define treatment effects in clinical trials

Abstract Background The estimand for a clinical trial is a precise definition of the treatment effect to be estimated. Traditionally, estimates of treatment effects are based on either an ITT analysis or a per-protocol analysis. However, there are important clinical questions which are not addressed by either of these analyses. For example, consider a trial where patients take a rescue medication. The ITT analysis includes data after use of rescue, while the per-protocol analysis excludes these patients altogether. Neither of these analyses addresses the important question of what the treatment effect would have been if patients did not take rescue medication. Main text Trial estimands provide a broader perspective compared to the limitations of ITT and per-protocol analysis. Trial treatment effects depend on how events occurring after treatment initiation such as use of alternative medication or discontinuation of the intervention are included in the definition. These events can be accounted for in different ways, depending on the clinical question of interest. Conclusion The estimand framework is an important step forward in improving the clarity and transparency of clinical trials. The centrality of estimands to clinical trials is currently not reflected in methods recommended by the Cochrane group or the CONSORT statement, the current standard for reporting clinical trials in medical journals. We encourage revisions to these guidelines

Patterns of missing data in the use of the endometriosis symptom diary

Abstract Background Endometriosis is a common, chronic condition in women of reproductive age that is characterized by the presence of functional endometriotic lesions outside the uterus. The Endometriosis Symptom Diary (ESD) is an electronic patient-reported outcome (ePRO) instrument that assesses women’s experience of endometriosis symptoms, with pain scored using a 0–10 numeric rating scale. This study investigated patterns of data missing from the ESD in the VALEPRO study. Methods Post hoc analyses of missing data were conducted. Results Of 272 participants using the ESD, 26.5% had no missing diary entries, 46.7% had > 0–5% of entries missing, 13.2% had > 5–10% of entries missing and 13.6% had > 10% of entries missing over the entire study period. The duration of missing episodes (defined as ≥1 consecutive days with missing diary entries) was generally short; most (81.4%) were 1 day. The difference in mean worst pain scores between missing and complete episodes per participant was − 0.1, suggesting that missing episodes were not related to severity of pain. Entries were significantly more likely to be missing on Fridays (18.5%) and Saturdays (22.9%) compared with other days of the week (p < 0.0001). Participants in the USA had significantly more long missing episodes than those in Germany (proportions of missing episodes longer than 1 day, 22.6 and 10.5%, respectively; p < 0.0001). The proportions of women with ≥1 missing entry were 50.0, 70.2 and 79.8% for women with elementary education, secondary education, and a college or university education, respectively. The proportions of women with ≥1 missing entry were similar for those with and without children (72.2 and 74.3%, respectively). Conclusions Most participants were highly compliant with entering data in the ESD and the amount of missing data was low. Entries were significantly more likely to be missing on Fridays and Saturdays compared with other days of the week, and participants in the USA had significantly more long missing episodes than participants in Germany. Trial registration Clinicaltrials.gov, NCT01643122, registered 4 July 2012