...we may consider more direct tests for heterogeneity in predictor effects by place or time. ...fully independent external validation with data not available at the time of prediction model ...development can be important (Fig. 2).
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We convened an ad hoc International Working Group for Antibody Validation in order to formulate the best approaches for validating antibodies used in common research applications and to provide ...guidelines that ensure antibody reproducibility. We recommend five conceptual pillars for antibody validation to be used in an application-specific manner. Antibodies are among the most frequently used tools in basic science research and in clinical assays. Despite their widespread use, as well as extensive and valuable discourse in the literature16, a comprehensive scientific framework for antibody validation across research applications is lacking. As a result, the quality and consistency of data generated through the use of antibodies vary greatly. This poses an impediment to the rigor and reproducibility that are the cornerstones of the advancement of science.
The Exploratory Factor Analysis (EFA) procedure is one of the most commonly used in social and behavioral sciences. However, it is also one of the most criticized due to the poor management ...researchers usually display. The main goal is to examine the relationship between practices usually considered more appropriate and actual decisions made by researchers.
The use of exploratory factor analysis is examined in 117 papers published between 2011 and 2012 in 3 Spanish psychological journals with the highest impact within the previous five years.
RESULTS show significant rates of questionable decisions in conducting EFA, based on unjustified or mistaken decisions regarding the method of extraction, retention, and rotation of factors.
Overall, the current review provides support for some improvement guidelines regarding how to apply and report an EFA.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Before considering whether to use a multivariable (diagnostic or prognostic) prediction model, it is essential that its performance be evaluated in data that were not used to develop the model ...(referred to as external validation). We critically appraised the methodological conduct and reporting of external validation studies of multivariable prediction models.
We conducted a systematic review of articles describing some form of external validation of one or more multivariable prediction models indexed in PubMed core clinical journals published in 2010. Study data were extracted in duplicate on design, sample size, handling of missing data, reference to the original study developing the prediction models and predictive performance measures.
11,826 articles were identified and 78 were included for full review, which described the evaluation of 120 prediction models. in participant data that were not used to develop the model. Thirty-three articles described both the development of a prediction model and an evaluation of its performance on a separate dataset, and 45 articles described only the evaluation of an existing published prediction model on another dataset. Fifty-seven percent of the prediction models were presented and evaluated as simplified scoring systems. Sixteen percent of articles failed to report the number of outcome events in the validation datasets. Fifty-four percent of studies made no explicit mention of missing data. Sixty-seven percent did not report evaluating model calibration whilst most studies evaluated model discrimination. It was often unclear whether the reported performance measures were for the full regression model or for the simplified models.
The vast majority of studies describing some form of external validation of a multivariable prediction model were poorly reported with key details frequently not presented. The validation studies were characterised by poor design, inappropriate handling and acknowledgement of missing data and one of the most key performance measures of prediction models i.e. calibration often omitted from the publication. It may therefore not be surprising that an overwhelming majority of developed prediction models are not used in practice, when there is a dearth of well-conducted and clearly reported (external validation) studies describing their performance on independent participant data.
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Background
Prediction models are developed to aid healthcare providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific ...event will occur in the future (prognostic models), to inform their decision‐making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed.
Methods
An extensive list of items based on a review of the literature was created, which was reduced after a web‐based survey and revised during a 3‐day meeting in June 2011 with methodologists, healthcare professionals and journal editors. The list was refined during several meetings of the steering group and in e‐mail discussions with the wider group of TRIPOD contributors.
Results
The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study.
Conclusion
The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. A complete checklist is available at http://www.tripod‐statement.org.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
This article describes the implementation of real‐space refinement in the phenix.real_space_refine program from the PHENIX suite. The use of a simplified refinement target function enables very fast ...calculation, which in turn makes it possible to identify optimal data‐restraint weights as part of routine refinements with little runtime cost. Refinement of atomic models against low‐resolution data benefits from the inclusion of as much additional information as is available. In addition to standard restraints on covalent geometry, phenix.real_space_refine makes use of extra information such as secondary‐structure and rotamer‐specific restraints, as well as restraints or constraints on internal molecular symmetry. The re‐refinement of 385 cryo‐EM‐derived models available in the Protein Data Bank at resolutions of 6 Å or better shows significant improvement of the models and of the fit of these models to the target maps.
A description is provided of the implementation of real‐space refinement in the phenix.real_space_refine program from the PHENIX suite and its application to the re‐refinement of cryo‐EM‐derived models.
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This paper introduces ISOLDE, a new software package designed to provide an intuitive environment for high‐fidelity interactive remodelling/refinement of macromolecular models into electron‐density ...maps. ISOLDE combines interactive molecular‐dynamics flexible fitting with modern molecular‐graphics visualization and established structural biology libraries to provide an immersive interface wherein the model constantly acts to maintain physically realistic conformations as the user interacts with it by directly tugging atoms with a mouse or haptic interface or applying/removing restraints. In addition, common validation tasks are accelerated and visualized in real time. Using the recently described 3.8 Å resolution cryo‐EM structure of the eukaryotic minichromosome maintenance (MCM) helicase complex as a case study, it is demonstrated how ISOLDE can be used alongside other modern refinement tools to avoid common pitfalls of low‐resolution modelling and improve the quality of the final model. A detailed analysis of changes between the initial and final model provides a somewhat sobering insight into the dangers of relying on a small number of validation metrics to judge the quality of a low‐resolution model.
ISOLDE is an interactive molecular‐dynamics environment for rebuilding models against experimental cryo‐EM or crystallographic maps. Analysis of its results reinforces the need for great care when validating models built into low‐resolution data.
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We describe a framework for defining pilot and feasibility studies focusing on studies conducted in preparation for a randomised controlled trial. To develop the framework, we undertook a Delphi ...survey; ran an open meeting at a trial methodology conference; conducted a review of definitions outside the health research context; consulted experts at an international consensus meeting; and reviewed 27 empirical pilot or feasibility studies. We initially adopted mutually exclusive definitions of pilot and feasibility studies. However, some Delphi survey respondents and the majority of open meeting attendees disagreed with the idea of mutually exclusive definitions. Their viewpoint was supported by definitions outside the health research context, the use of the terms 'pilot' and 'feasibility' in the literature, and participants at the international consensus meeting. In our framework, pilot studies are a subset of feasibility studies, rather than the two being mutually exclusive. A feasibility study asks whether something can be done, should we proceed with it, and if so, how. A pilot study asks the same questions but also has a specific design feature: in a pilot study a future study, or part of a future study, is conducted on a smaller scale. We suggest that to facilitate their identification, these studies should be clearly identified using the terms 'feasibility' or 'pilot' as appropriate. This should include feasibility studies that are largely qualitative; we found these difficult to identify in electronic searches because researchers rarely used the term 'feasibility' in the title or abstract of such studies. Investigators should also report appropriate objectives and methods related to feasibility; and give clear confirmation that their study is in preparation for a future randomised controlled trial designed to assess the effect of an intervention.
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Cognition and behavior emerge from brain network interactions, such that investigating causal interactions should be central to the study of brain function. Approaches that characterize statistical ...associations among neural time series-functional connectivity (FC) methods-are likely a good starting point for estimating brain network interactions. Yet only a subset of FC methods ('effective connectivity') is explicitly designed to infer causal interactions from statistical associations. Here we incorporate best practices from diverse areas of FC research to illustrate how FC methods can be refined to improve inferences about neural mechanisms, with properties of causal neural interactions as a common ontology to facilitate cumulative progress across FC approaches. We further demonstrate how the most common FC measures (correlation and coherence) reduce the set of likely causal models, facilitating causal inferences despite major limitations. Alternative FC measures are suggested to immediately start improving causal inferences beyond these common FC measures.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Context
Assessment is central to medical education and the validation of assessments is vital to their use. Earlier validity frameworks suffer from a multiplicity of types of validity or failure to ...prioritise among sources of validity evidence. Kane's framework addresses both concerns by emphasising key inferences as the assessment progresses from a single observation to a final decision. Evidence evaluating these inferences is planned and presented as a validity argument.
Objectives
We aim to offer a practical introduction to the key concepts of Kane's framework that educators will find accessible and applicable to a wide range of assessment tools and activities.
Results
All assessments are ultimately intended to facilitate a defensible decision about the person being assessed. Validation is the process of collecting and interpreting evidence to support that decision. Rigorous validation involves articulating the claims and assumptions associated with the proposed decision (the interpretation/use argument), empirically testing these assumptions, and organising evidence into a coherent validity argument. Kane identifies four inferences in the validity argument: Scoring (translating an observation into one or more scores); Generalisation (using the scores as a reflection of performance in a test setting); Extrapolation (using the scores as a reflection of real‐world performance), and Implications (applying the scores to inform a decision or action). Evidence should be collected to support each of these inferences and should focus on the most questionable assumptions in the chain of inference. Key assumptions (and needed evidence) vary depending on the assessment's intended use or associated decision. Kane's framework applies to quantitative and qualitative assessments, and to individual tests and programmes of assessment.
Conclusions
Validation focuses on evaluating the key claims, assumptions and inferences that link assessment scores with their intended interpretations and uses. The Implications and associated decisions are the most important inferences in the validity argument.
Discuss ideas arising from the article at www.meduedu.com ‘discuss’.
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BFBNIB, DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, SIK, UILJ, UKNU, UL, UM, UPUK, VSZLJ
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