1,721,099 research outputs found
An algorithm for the classification of study designs to assess diagnostic, prognostic and predictive test accuracy in systematic reviews
No full textResults of medical tests are the main source to inform clinical decision making. The main information to assess the usefulness of medical tests for correct discrimination of patients are accuracy measures. For the estimation of test accuracy measures, many different study designs can be used. The study design is related to the clinical question to be answered (diagnosis, prognosis, prediction), determines the accuracy measures that can be calculated and it might have an influence on risk of bias. Therefore, a clear and consistent distinction of the different study designs in systematic reviews on test accuracy studies is very important. In this paper, we propose an algorithm for the classification of study designs of test accuracy, that compare the results of an index test (the test to be evaluated) with the results of a reference test (the test whose results are considered as correct/the gold standard) studies in systematic reviews
Suture Versus Mesh Repair in Primary and Incisional Ventral Hernias: A Systematic Review and Meta-Analysis
No full textToday, ventral hernia repair is predominantly performed with meshes. There is no meta-analysis of high quality evidence that compares the results of suture to mesh repair. The objective of this systematic review with meta-analysis is to compare patient centred outcomes of suture versus mesh repair
Combining randomized controlled trials and real world data
No full textZusammenfassung Hintergrund Randomisierte kontrollierte Studien („randomized controlled trials“ [RCT]) sind der Goldstandard für die Evaluation von Interventionen. Allerdings wird oft angeführt, dass diese nur schwer durchzuführen seien und dass sie daher ggf. unter kleinen Fallzahlen leideten. Zudem wird häufig kritisiert, dass RCT aus diesem Grund sowie durch (zu) enge Einschlusskriterien und zu starke Standardisierung vielfach nicht den klinischen Routinebedingungen entsprechen. Beides kann zu Einschränkungen in der Aussagekraft von RCT führen. Fragestellung Der Artikel zeigt auf, wie RCT und Real World Data (RWD)-basierte Studien voneinander profitieren können. Methoden Es wurde eine selektive Übersicht der Literatur zur Verknüpfung von Daten aus RCT und RWD erstellt. Ergebnisse Die RCT-Daten und RWD können mit unterschiedlichen Zielen verknüpft werden. Zum einen kann die Verknüpfung dazu dienen, die Effizienz der Auswertung eines RCT zu erhöhen. So können hierarchische Modelle zur Evidenzsynthese RWD nutzen, um die Präzision der RCT-Effektschätzung maßgeblich zu erhöhen. Zum anderen können RWD genutzt werden, falls die Übertragbarkeit von RCT auf die Routineversorgung zweifelhaft ist. Zur Erhöhung der externen Validität können u. a. verschiedene Gewichtungsverfahren und Modellierungsmethoden verwendet werden. Umgekehrt können RCT-Daten genutzt werden, um eine systematische Verzerrung in RWD zu bereinigen. Bei der „comprehensive cohort study“ erfolgt die Durchführung der RCT- und der Kohortenstudie parallel. Sie erlaubt die Einschätzung der externen Validität eines RCT und kann zudem bei einer gemeinsamen Auswertung von RCT und Registern sehr effizient sein. Schlussfolgerungen Es bestehen diverse vielsprechende Möglichkeiten, Daten aus RCT und RWD zu verknüpfen. Es erscheint daher wünschenswert, dass Verknüpfungen vermehrt Anwendung finden. Hierbei ist wichtig, dass diese prospektiv geplant werden.Abstract Background Randomized controlled trials (RCTs) are the gold standard for evaluating interventions. However, they are often considered to be difficult to conduct and may therefore suffer from small case numbers. In addition, it is often claimed that RCTs do not represent clinical routine well. These may lead to limited significance or relevance of the results from RCTs. Objectives To show how RCTs and real world data (RWD)-based studies can benefit from each other. Methods This is a selective review of the literature on approaches for linking data from RCTs and RWD. Results RCTs and RWD can be linked with different aims. First, RWD can be used to increase the efficiency of the evaluation of RCTs. More specifically, hierarchical models for evidence synthesis can be utilized to combine RWD and RCT data to increase the precision of the RCT effect estimate. Second, RWD can be used if the applicability of RCTs results to routine care is doubtful. Here, various stratification methods and modelling methods are available that can increase the external validity of the RCT results. Conversely, information from RCTs can be utilized to adjust for bias in RWD. In the comprehensive cohort study design, the RCT and the cohort study are carried out in parallel. It allows to assess the external validity of an RCT and can also be very efficient when the RCT and registry are evaluated jointly. Conclusions There are various promising ways of linking data from RCTs and RWD. Therefore, a more routine joint consideration of RCT and RWD data appears desirable. It is important that this is planned prospectively
Adherence-enhancing interventions for active antiretroviral therapy in sub-Saharan Africa: a systematic review and meta-analysis
No full textBackground In sub-Saharan Africa, an estimated 23% of HIV-infected patients are nonadherent. The objective was to evaluate the effectiveness of adherence-enhancing interventions for active antiretroviral therapy (ART) in HIV-infected patients in sub-Saharan Africa
No differences were found between effect estimates from conventional and registry-based randomized controlled trials
No full textThe study aims to assess whether the results from registry-based randomized controlled trials (RRCTs) systematically differ from the results of conventional randomized controlled trials (CRCTs)
A systematic review of the impact of volume of surgery and specialization in Norwood procedure
No full textThe volume-outcome relationship is supposed to be stronger in high risk, low volume procedures. The aim of this systematic review is to examine the available literature on the effects of hospital and surgeon volume, specialization and regionalization on the outcomes of the Norwood procedure
Clarifying the distinction between case series and cohort studies in systematic reviews of comparative studies: potential impact on body of evidence and workload
No full textAbstract Distinguishing cohort studies from case series is difficult. We propose a conceptualization of cohort studies in systematic reviews of comparative studies. The main aim of this conceptualization is to clarify the distinction between cohort studies and case series. We discuss the potential impact of the proposed conceptualization on the body of evidence and workload. All studies with exposure-based sampling gather multiple exposures (with at least two different exposures or levels of exposure) and enable calculation of relative risks that should be considered cohort studies in systematic reviews, including non-randomized studies. The term “enables/can” means that a predefined analytic comparison is not a prerequisite (i.e., the absolute risks per group and/or a risk ratio are provided). Instead, all studies for which sufficient data are available for reanalysis to compare different exposures (e.g., sufficient data in the publication) are classified as cohort studies. There are possibly large numbers of studies without a comparison for the exposure of interest but that do provide the necessary data to calculate effect measures for a comparison. Consequently, more studies could be included in a systematic review. Therefore, on the one hand, the outlined approach can increase the confidence in effect estimates and the strengths of conclusions. On the other hand, the workload would increase (e.g., additional data extraction and risk of bias assessment, as well as reanalyses)
Effects of computer-aided clinical decision support systems in improving antibiotic prescribing by primary care providers: a systematic review
No full textTo assess the effectiveness of computer-aided clinical decision support systems (CDSS) in improving antibiotic prescribing in primary care
A comparison of methods for meta-analysis of small number of studies with binary outcomes
No full textMeta-analyses often include only a small number of studies (≤5). Estimating between-study heterogeneity is difficult in this situation. An inaccurate estimation of heterogeneity can result in biased effect estimates and too narrow confidence intervals. The beta-binominal model has shown good statistical properties for meta-analysis of sparse data. We compare the beta-binominal model with different inverse variance random (eg, DerSimonian-Laird, modified Hartung-Knapp, and Paule-Mandel) and fixed effects methods (Mantel-Haenszel and Peto) in a simulation study. The underlying true parameters were obtained from empirical data of actually performed meta-analyses to best mirror real-life situations. We show that valid methods for meta-analysis of a small number of studies are available. In fixed effects situations, the Mantel-Haenszel and Peto methods performed best. In random effects situations, the beta-binominal model performed best for meta-analysis of few studies considering the balance between coverage probability and power. We recommended the beta-binominal model for practical application. If very strong evidence is needed, using the Paule-Mandel heterogeneity variance estimator combined with modified Hartung-Knapp confidence intervals might be useful to confirm the results. Notable most inverse variance random effects models showed unsatisfactory statistical properties also if more studies (10-50) were included in the meta-analysis
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