23 papers · ranked by Valyu relevance
Lehana Thabane, Lawrence Mbuagbaw, Shiyuan Zhang, Zainab Samaan + 13 more
'Maura Marcucci' 'Chenglin Ye' 'Marroon Thabane' 'Lora Giangregorio' 'Brittany Dennis' 'Daisy Kosa' 'Victoria Borg Debono' 'Rejane Dillenburg' 'Vincent Fruci' 'Monica Bawor' 'Juneyoung Lee' 'George Wells' 'Charles H Goldsmith'] Background Sensitivity analyses play a crucial role in assessing the robustness of the…
Fabrice I. Mowbray, Donna Manlongat, Meghna Shukla
Nursing and health researchers may be presented with uncertainty regarding the utilization or legitimacy of methodological or analytic decisions. Sensitivity analyses are purposed to gain insight and certainty about the validity of research findings reported. Reporting guidelines and health research methodologists have…
Juan Cabral, Alvaro Roy Schachner
- 1 Grupo de Innovación y Desarrollo Tecnológico, Comisión Nacional de Actividades Espaciales (GVT-CONAE), Córdoba, Argentina - 2 Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Córdoba, Argentina 3 Facultad de Matemática Astronomía y Física, Universidad Nacional de Córdoba (FAMAF-UNC), Córdoba…
Lin Cheng, Sourabh Balgi, José M. Peña, Adel Daoud
Explaining social events is a primary objective of applied data-driven sociology. To achieve that objective, many sociologists use statistical causal inference to identify causality using observational studies—research context where the analyst does not control the data generating process. However, it is often…
Margaret Jane Moore, Chris Rorden, Gail A. Robinson, Jason B. Mattingley + 1 more
Lesion mapping results can vary substantially as a function of specific analysis parameters, but the extent to which individual methodological choices interact to modulate the sensitivity and specificity of results is not clear. Here, we employed a large-scale simulation approach to inform practical recommendations for…
Nyan Min Aung, Ivan Jurak, Seemab Mehmood, Emma Axon
This tutorial explains when systematic review authors may consider performing a sensitivity analysis in a meta-analysis. Such scenarios include removing studies at high risk of bias, exploring the effect of outliers and examining differences in study characteristics (e.g., participants’ age, study design). In addition…
Marija Franka Marušić, Mahir Fidahić, Cristina Mihaela Cepeha, Loredana Gabriela Farcaș + 2 more
'Loredana Gabriela Farcaș' 'Alexandra Tseke' 'Livia Puljak'] Background A crucial element in the systematic review (SR) methodology is the appraisal of included primary studies, using tools for assessment of methodological quality or risk of bias (RoB). SR authors can conduct sensitivity analyses to explore whether…
Peng Ding, Tyler J. VanderWeele
Unmeasured confounding may undermine the validity of causal inference with observational studies. Sensitivity analysis provides an attractive way to partially circumvent this issue by assessing the potential influence of unmeasured confounding on the causal conclusions. However, previous sensitivity analysis approaches…
Peng Ding, Yixin Fang, D Faries, Susan Gruber + 7 more
'Joo-Yeon Lee' 'Pallavi S. Mishra‐Kalyani' 'Mingyang Shan' 'Mark van der Laan' 'Shu Yang' 'Xiang Zhang'] The American Statistical Association Biopharmaceutical Section (ASA BIOP) working group on real-world evidence (RWE) has been making continuous, extended effort towards a goal of supporting and advancing regulatory…
Rachel Mester, Alfonso Landeros, Chris Rackauckas, Kenneth Lange
Differential sensitivity analysis is indispensable in fitting parameters, understanding uncertainty, and forecasting the results of both thought and lab experiments. Although there are many methods currently available for performing differential sensitivity analysis of biological models, it can be difficult to…
Alexander Franks, Alexander D’Amour, Avi Feller
A fundamental challenge in observational causal inference is that assumptions about unconfoundedness are not testable from data. Assessing sensitivity to such assumptions is therefore important in practice. Unfortunately, some existing sensitivity analysis approaches inadvertently impose restrictions that are at odds…
Wen Wei Loh, Stijn Vansteelandt
Inferring the causal effect of a non-randomly assigned exposure on an outcome requires adjusting for common causes of the exposure and outcome to avoid biased conclusions. Notwithstanding the efforts investigators routinely make to measure and adjust for such common causes (or confounders), some confounders typically…
Chao Xu, Emily Mazeau, Richard West
Mean-field micro-kinetic modeling is a powerful tool for catalyst design and the simulation of catalytic processes. The reaction enthalpies in a micro-kinetic model often need to be adjusted when changing species' binding energies to model different catalysts, when performing thermodynamic sensitivity analyses, and…
Joanna Emerson, Ari Panzer, Joshua T. Cohen, Kalipso Chalkidou + 6 more
The iDSI reference case, originally published in 2014, aims to improve the quality and comparability of cost-effectiveness analyses (CEAs). This study assesses whether the development of the guideline has improved the reporting and methodology for CEAs using disability-adjusted life-years (DALYs). We analyzed the Tufts…
Veeren Chauhan, Mohamed M Elsutohy, C Patrick McClure, Will Irving + 2 more
Enteroviruses are a ubiquitous mammalian pathogen that can produce mild to life-threatening disease. Bearing this in mind, we have developed a rapid, accurate and economical point-of-care biosensor that can detect a nucleic acid sequences conserved amongst 96% of all known enteroviruses. The biosensor harnesses the…
Neal R. Haddaway, Jos T.A. Verhoeven
Despite the scientific method's central tenets of reproducibility (the ability to obtain similar results when repeated) and repeatability (the ability to replicate an experiment based on methods described), published ecological research continues to fail to provide sufficient methodological detail to allow either…
Berna Devezer, Danielle J. Navarro, Joachim Vandekerckhove, Erkan Ozge Buzbas
Current attempts at methodological reform in sciences come in response to an overall lack of rigor in methodological and scientific practices in experimental sciences. However, some of these reform attempts suffer from the same mistakes and over-generalizations they purport to address. Considering the costs of allowing…
Salvador Chacón-Moscoso, Susana Sanduvete-Chaves, Milagrosa Sánchez-Martín
'Milagrosa Sánchez-Martín'] The methodological quality of primary studies is an important issue when performing meta-analyses or systematic reviews. Nevertheless, there are no clear criteria for how methodological quality should be analyzed. Controversies emerge when considering the various theoretical and empirical…
Katharina Felicitas Mueller, Joerg J Meerpohl, Matthias Briel, Gerd Antes + 6 more
'Gerd Antes' 'Erik von Elm' 'Britta Lang' 'Viktoria Gloy' 'Edith Motschall' 'Guido Schwarzer' 'Dirk Bassler'] Background Health professionals and policymakers aspire to make healthcare decisions based on the entire relevant research evidence. This, however, can rarely be achieved because a considerable amount of…
Authors not listed
The rapid growth of worldwide computing power has transformed in silico chemistry into a discipline that is integrated into the daily work of many chemists. Nowadays, researchers find it increasingly straightforward to predict a wide range of molecular properties and chemi- cal processes at reasonable computational…
Authors not listed
Ensuring the trustworthiness of machine learning (ML) models in high-stake applications is crucial. One such application is predicting anti-cancer drug sensitivity, where ML models are built with the final goal of integrating them into treatment recommendation systems for personalized medicine. Here, we propose a…
Yefeng Yang, Coralie Williams, Alistair M. Senior, Kyle Morrison + 4 more
Meta-analytic modelling plays a pivotal role in synthesizing research and informing relevant policies. Yet researchers face many analytical challenges. In environmental and biological sciences, one of the most common yet unrecognised issues is the selection between two common effect size metrics, log response ratio…
Justus T. Metternich, Sujit K. Patjoshi, Tanuja Kistwal, Sebastian Kruss
Optical sensors/probes are powerful tools to identify and image (biological) molecules. Because of their optoelectronic properties, nanomaterials are often used as building blocks. Such nanosensors are assembled from an optically sensitive nanomaterial, a (biological) recognition unit, and linker chemistry that…