1,720,981 research outputs found
Subgroup identification using individual participant data from multiple trials: An application in low back pain
No full textAbstract Model-based recursive partitioning (MOB) and its extension, metaMOB, are tools for identifying subgroups with differential treatment effects. When pooling data from various trials the metaMOB approach uses random effects to model the heterogeneity of treatment effects. In situations where interventions offer only small overall benefits and require extensive, costly trials with a large participant enrollment, leveraging individual-participant data (IPD) from multiple trials can help identify individuals who are most likely to benefit from the intervention. We explore the application of MOB and metaMOB in the context of non-specific low back pain treatment, using synthetic data based on a subset of the individual participant data meta-analysis by Patel et al. 1 Our study underscores the need to explore heterogeneity in intercepts and treatment effects to identify subgroups with differential treatment effects in IPD meta-analyses
Model‐Based Recursive Partitioning for Discrete Event Times
No full textABSTRACT Model‐based recursive partitioning (MOB) is used to identify subgroups based on various outcome measures, including time‐to‐event outcomes. Discrete time‐to‐event data are typically fitted using the generalized linear model (GLM) framework with binary outcome. However, direct application of MOB with GLMs for binary outcomes needs an augmented data matrix, which violates the assumption of independent observations required for MOB's splitting criterion. We propose a permutation approach tailored to discrete time‐to‐event data that controls the error rate of MOB's splitting criterion, avoiding spurious subgroup identification. In simulations, we compare this permutation approach to the naïve approach applying MOB with regression models for binary outcomes directly to the augmented data and MOB using the sum of the score contributions. Our experiments showed that MOB using the permutation approach controls the Type I error rate and accurately identifies splitting variables. To illustrate the various MOB approaches for discrete time‐to‐event data, we apply them to an example data set on unemployment duration
Methodological Insights on Biomarker‐Based Patient Selection: A Review of Scientific Advice Procedures at the European Medicines Agency
No full textBiomarkers play a pivotal role in the selection and enrollment of trial participants. Particularly, predictive biomarkers help tailor medical care to individual patients; however, also prognostic biomarkers require consideration at the design stage. At the time of initiating a clinical trial, there may be uncertainty about whether a biomarker is predictive or prognostic, and the trial design may need to account for this. Relevant discussions between drug developers and regulators on the role of a biomarker in a specific drug development program are expected to take place during Scientific Advice (SA) procedures. SA procedures at the European Medicines Agency from January 1, 2018, to December 31, 2020, were systematically searched for methodological discussions around the use of predictive or prognostic biomarkers. The final analysis included 45 SA procedures for which key characteristics were summarized quantitatively. Selected methodological issues such as the cutoff selection of continuous biomarkers or study design considerations were elaborated in a qualitative summary. Our results identify commonly encountered points for discussion between drug developers and the European Medicines Agency for biomarker‐informed patient selection and enrollment. Identified topics addressed during SA procedures include cutoff selection, study design, multiplicity control, and data‐driven subgroup selection. The majority of the identified 45 SA procedures concern development programs in oncology. Addressing these issues upfront will allow for an improved dialogue between drug developers and regulators and support the drug development program and ultimately patient‐centered access to medicines
A comparison of subgroup identification methods in clinical drug development: Simulation study and regulatory considerations
No full textWith advancement of technologies such as genomic sequencing, predictive biomarkers have become a useful tool for the development of personalized medicine. Predictive biomarkers can be used to select subsets of patients, which are most likely to benefit from a treatment. A number of approaches for subgroup identification were proposed over the last years. Although overviews of subgroup identification methods are available, systematic comparisons of their performance in simulation studies are rare. Interaction trees (IT), model-based recursive partitioning, subgroup identification based on differential effect, simultaneous threshold interaction modeling algorithm (STIMA), and adaptive refinement by directed peeling were proposed for subgroup identification. We compared these methods in a simulation study using a structured approach. In order to identify a target population for subsequent trials, a selection of the identified subgroups is needed. Therefore, we propose a subgroup criterion leading to a target subgroup consisting of the identified subgroups with an estimated treatment difference no less than a pre-specified threshold. In our simulation study, we evaluated these methods by considering measures for binary classification, like sensitivity and specificity. In settings with large effects or huge sample sizes, most methods perform well. For more realistic settings in drug development involving data from a single trial only, however, none of the methods seems suitable for selecting a target population. Using the subgroup criterion as alternative to the proposed pruning procedures, STIMA and IT can improve their performance in some settings. The methods and the subgroup criterion are illustrated by an application in amyotrophic lateral sclerosis
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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