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Research Synthesis Methods

Amy Y Tsou, Jonathan R Treadwell
BACKGROUND: Systematic review (SR) abstracts are important for disseminating evidence syntheses to inform medical decision making. We assess reporting quality in SR abstracts using PRISMA for Abstracts (PRISMA-A), Cochrane Handbook, and Agency for Healthcare Research & Quality guidance. METHODS: We evaluated a random sample of 200 SR abstracts (from 2014) comparing interventions in the general medical literature. We assessed adherence to PRISMA-A criteria, problematic wording in conclusions, and whether "positive" studies described clinical significance...
October 20, 2016: Research Synthesis Methods
Christopher H Schmid
No abstract text is available yet for this article.
October 17, 2016: Research Synthesis Methods
Perke Jacobs, Wolfgang Viechtbauer
Meta-analyses are often used to synthesize the findings of studies examining the correlational relationship between two continuous variables. When only dichotomous measurements are available for one of the two variables, the biserial correlation coefficient can be used to estimate the product-moment correlation between the two underlying continuous variables. Unlike the point-biserial correlation coefficient, biserial correlation coefficients can therefore be integrated with product-moment correlation coefficients in the same meta-analysis...
September 15, 2016: Research Synthesis Methods
Meg Bennetts, Ed Whalen, Sima Ahadieh, Joseph C Cappelleri
Although well developed to assess efficacy questions, meta-analyses and, more generally, systematic reviews, have received less attention in application to safety-related questions. As a result, many open questions remain on how best to apply meta-analyses in the safety setting. This appraisal attempts to: (i) summarize the current guidelines for assessing individual studies, systematic reviews, and network meta-analyses; (ii) describe several publications on safety meta-analytic approaches; and (iii) present some of the questions and issues that arise with safety data...
September 9, 2016: Research Synthesis Methods
Michael T Brannick, Mehmet Gültaş
We describe a meta-analytic scatterplot that indicates precision of points for two variables paired within studies; this is equivalent in form to a 'cross-hairs' plot used to portray specificity and sensitivity in diagnostic testing. At the user's discretion, the plot also displays boxplots for each of the X and Y variable distributions, means for each of the variables, and the correlation between the two. The cross-hairs may be suppressed for dense point clouds. The program is written in R, so it can be modified by the user and can serve as a companion to existing meta-analysis programs...
August 6, 2016: Research Synthesis Methods
Klea Panayidou, Sandro Gsteiger, Matthias Egger, Gablu Kilcher, Máximo Carreras, Orestis Efthimiou, Thomas P A Debray, Sven Trelle, Noemi Hummel
The performance of a drug in a clinical trial setting often does not reflect its effect in daily clinical practice. In this third of three reviews, we examine the applications that have been used in the literature to predict real-world effectiveness from randomized controlled trial efficacy data. We searched MEDLINE, EMBASE from inception to March 2014, the Cochrane Methodology Register, and websites of key journals and organisations and reference lists. We extracted data on the type of model and predictions, data sources, validation and sensitivity analyses, disease area and software...
September 2016: Research Synthesis Methods
Matthias Egger, Karel G M Moons, Christine Fletcher
The GetReal consortium ("incorporating real-life data into drug development") addresses the efficacy-effectiveness gap that opens between the data from well-controlled randomized trials in selected patient groups submitted to regulators and the real-world evidence on effectiveness and safety of drugs required by decision makers. Workpackage 4 of GetReal develops evidence synthesis and modelling approaches to generate the real-world evidence. In this commentary, we discuss how questions change when moving from the well-controlled randomized trial setting to real-life medical practice, the evidence required to answer these questions, the populations to which estimates will be applicable to and the methods and data sources used to produce these estimates...
September 2016: Research Synthesis Methods
Joshua R Polanin, Ryan T Williams
Individual participant data (IPD) is the backbone of scientific inquiry and important to a meta-analysis for a variety of reasons. It is therefore important to be able to access IPD, and yet, obstacles persist that make it difficult for meta-analysts, as well as interested primary study analysts, to obtain it. In this paper, we discuss the barriers to obtaining IPD via online repositories or contacting primary study authors and provide an example data sharing agreement that can be used to ameliorate a few of these issues...
September 2016: Research Synthesis Methods
Orestis Efthimiou, Thomas P A Debray, Gert van Valkenhoef, Sven Trelle, Klea Panayidou, Karel G M Moons, Johannes B Reitsma, Aijing Shang, Georgia Salanti
Pairwise meta-analysis is an established statistical tool for synthesizing evidence from multiple trials, but it is informative only about the relative efficacy of two specific interventions. The usefulness of pairwise meta-analysis is thus limited in real-life medical practice, where many competing interventions may be available for a certain condition and studies informing some of the pairwise comparisons may be lacking. This commonly encountered scenario has led to the development of network meta-analysis (NMA)...
September 2016: Research Synthesis Methods
Elena Kulinskaya, Richard Huggins, Samson Henry Dogo
Whilst it is common in clinical trials to use the results of tests at one phase to decide whether to continue to the next phase and to subsequently design the next phase, we show that this can lead to biased results in evidence synthesis. Two new kinds of bias associated with accumulating evidence, termed 'sequential decision bias' and 'sequential design bias', are identified. Both kinds of bias are the result of making decisions on the usefulness of a new study, or its design, based on the previous studies...
September 2016: Research Synthesis Methods
Rose Baker, Dan Jackson
An unobserved random effect is often used to describe the between-study variation that is apparent in meta-analysis datasets. A normally distributed random effect is conventionally used for this purpose. When outliers or other unusual estimates are included in the analysis, the use of alternative random effect distributions has previously been proposed. Instead of adopting the usual hierarchical approach to modelling between-study variation, and so directly modelling the study specific true underling effects, we propose two new marginal distributions for modelling heterogeneous datasets...
September 2016: Research Synthesis Methods
Neil Hawkins, David A Scott, Beth Woods
We present an alternative to the contrast-based parameterization used in a number of publications for network meta-analysis. This alternative "arm-based" parameterization offers a number of advantages: it allows for a "long" normalized data structure that remains constant regardless of the number of comparators; it can be used to directly incorporate individual patient data into the analysis; the incorporation of multi-arm trials is straightforward and avoids the need to generate a multivariate distribution describing treatment effects; there is a direct mapping between the parameterization and the analysis script in languages such as WinBUGS and finally, the arm-based parameterization allows simple extension to treatment-specific random treatment effect variances...
September 2016: Research Synthesis Methods
Dan Jackson, Paul Boddington, Ian R White
In this note, we clarify and prove the claim made Higgins et al. () that the design-by-treatment interaction model contains all possible loop inconsistency models. This claim provides a strong argument for using the design-by-treatment interaction model to describe loop inconsistencies in network meta-analysis. © 2015 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd.
September 2016: Research Synthesis Methods
Federico Bonofiglio, Jan Beyersmann, Martin Schumacher, Michael Koller, Guido Schwarzer
Meta-analysis of a survival endpoint is typically based on the pooling of hazard ratios (HRs). If competing risks occur, the HRs may lose translation into changes of survival probability. The cumulative incidence functions (CIFs), the expected proportion of cause-specific events over time, re-connect the cause-specific hazards (CSHs) to the probability of each event type. We use CIF ratios to measure treatment effect on each event type. To retrieve information on aggregated, typically poorly reported, competing risks data, we assume constant CSHs...
September 2016: Research Synthesis Methods
Lorenz Uhlmann, Katrin Jensen, Meinhard Kieser
Network meta-analysis is becoming a common approach to combine direct and indirect comparisons of several treatment arms. In recent research, there have been various developments and extensions of the standard methodology. Simultaneously, cluster randomized trials are experiencing an increased popularity, especially in the field of health services research, where, for example, medical practices are the units of randomization but the outcome is measured at the patient level. Combination of the results of cluster randomized trials is challenging...
July 7, 2016: Research Synthesis Methods
Tim Friede, Christian Röver, Simon Wandel, Beat Neuenschwander
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...
June 30, 2016: Research Synthesis Methods
T D Stanley, Hristos Doucouliagos
Our study revisits and challenges two core conventional meta-regression estimators: the prevalent use of 'mixed-effects' or random-effects meta-regression analysis and the correction of standard errors that defines fixed-effects meta-regression analysis (FE-MRA). We show how and explain why an unrestricted weighted least squares MRA (WLS-MRA) estimator is superior to conventional random-effects (or mixed-effects) meta-regression when there is publication (or small-sample) bias that is as good as FE-MRA in all cases and better than fixed effects in most practical applications...
June 20, 2016: Research Synthesis Methods
Mai T Pham, Lisa Waddell, Andrijana Rajić, Jan M Sargeant, Andrew Papadopoulos, Scott A McEwen
BACKGROUND: The rapid review is an approach to synthesizing research evidence when a shorter timeframe is required. The implications of what is lost in terms of rigour, increased bias and accuracy when conducting a rapid review have not yet been elucidated. METHODS: We assessed the potential implications of methodological shortcuts on the outcomes of three completed systematic reviews addressing agri-food public health topics. For each review, shortcuts were applied individually to assess the impact on the number of relevant studies included and whether omitted studies affected the direction, magnitude or precision of summary estimates from meta-analyses...
June 10, 2016: Research Synthesis Methods
Mi-Ok Kim, Xia Wang, Chunyan Liu, Kathleen Dorris, Maryam Fouladi, Seongho Song
Phase I trials aim to establish appropriate clinical and statistical parameters to guide future clinical trials. With individual trials typically underpowered, systematic reviews and meta-analysis are desired to assess the totality of evidence. A high percentage of zero or missing outcomes often complicate such efforts. We use a systematic review of pediatric phase I oncology trials as an example and illustrate the utility of advanced Bayesian analysis. Standard random-effects methods rely on the exchangeability of individual trial effects, typically assuming that a common normal distribution sufficiently describes random variation among the trial level effects...
June 10, 2016: Research Synthesis Methods
David Mawdsley, Julian P T Higgins, Alex J Sutton, Keith R Abrams
In meta-analysis, the random-effects model is often used to account for heterogeneity. The model assumes that heterogeneity has an additive effect on the variance of effect sizes. An alternative model, which assumes multiplicative heterogeneity, has been little used in the medical statistics community, but is widely used by particle physicists. In this paper, we compare the two models using a random sample of 448 meta-analyses drawn from the Cochrane Database of Systematic Reviews. In general, differences in goodness of fit are modest...
June 3, 2016: Research Synthesis Methods
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