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Critical Appraisal Toolkit (CAT) for assessing multiple types of evidence

D Moralejo, T Ogunremi, K Dunn
2017 Canada Communicable Disease Report  
Although the toolkit was created to assist in the development of national guidelines related to infection prevention and control, clinicians, policy makers and students can use it to guide appraisal of  ...  Healthcare professionals are often expected to critically appraise research evidence in order to make recommendations for practice and policy development.  ...  versions of the toolkit, Frédéric Bergeron for technical support with the algorithms in the toolkit and the Centre for Communicable Diseases and Infection Control of the Public Health Agency of Canada  ... 
pmid:29770086 pmcid:PMC5764727 fatcat:4o6pfyrdmbat7fkdtotn2mebou

Causal Inference in medicine and in health policy, a summary [article]

Wenhao Zhang, Ramin Ramezani, Arash Naeim
2022 arXiv   pre-print
Moreover, we will demonstrate the applications of causal inference in tackling some common machine learning issues such as missing data and model transportability.  ...  We will discuss causal inference and ways to discover the cause-effect from observational studies in healthcare domain.  ...  Literature shows that causal inference can be adopted in deep learning modeling to reduce selection bias in recommender systems [61, 62] .  ... 
arXiv:2105.04655v4 fatcat:x5ud7t4tdbho7jqbqgwxsu4rme

Genetic Fine-mapping with Dense Linkage Disequilibrium Blocks: genetics of nicotine dependence [article]

Chen Mo, Zhenyao Ye, Kathryn Hatch, Yuan Zhang, Qiong Wu, Song Liu, Peter Kochunov, L. Elliot Hong, TIANZHOU MA, Shuo Chen
2020 bioRxiv   pre-print
Simulations were used to evaluate and compare the performance of our method and existing fine-mapping algorithms.  ...  We extract dense LD blocks and perform regression shrinkage to calculate a prioritization score to select a parsimonious set of causal variants.  ...  Bayesian 17 fine-mapping methods select causal variants by estimating the probability that an SNP is included as causal 18 in the model according to the posterior inclusion probability (PIP) [11, 12]  ... 
doi:10.1101/2020.12.10.420216 fatcat:bqvu2i6alngwtk75thtkue2tje

Risk of Bias Assessments and Evidence Syntheses for Observational Epidemiologic Studies of Environmental and Occupational Exposures: Strengths and Limitations

Kyle Steenland, M.K. Schubauer-Berigan, R. Vermeulen, R.M. Lunn, K. Straif, S. Zahm, P. Stewart, W.D. Arroyave, S.S. Mehta, N. Pearce
2020 Environmental Health Perspectives  
It should include the use of classical considerations for judging causality in human studies, as well as triangulation and integration of animal and mechanistic data.  ...  Bias assessments are important in evidence synthesis, but we argue they can and should be improved to address the concerns we raise here.  ...  The authors alone are responsible for the views expressed in this article, and they do not necessarily represent the views, decisions, or policies of the institutions with which they are affiliated.  ... 
doi:10.1289/ehp6980 pmid:32924579 fatcat:nvfxmewrinfftgaenhhrwgf4nq

Acute coronary syndrome quality improvement in Kerala (ACS QUIK): Rationale and design for a cluster-randomized stepped-wedge trial

Mark D. Huffman, Padinhare Purayil Mohanan, Raji Devarajan, Abigail S. Baldridge, Dimple Kondal, Lihui Zhao, Mumtaj Ali, Donald M. Lloyd-Jones, Dorairaj Prabhakaran
2017 American Heart Journal  
Cardiological Society of India -Kerala chapter sought to develop, implement, and evaluate a quality improvement intervention to improve process of care measures and clinical outcomes for these patients  ...  To date, the majority of participants are men (76%) and has ST-segment  ...  The funders were not involved in the development of the study design; collection, management, analysis, and interpretation of data; writing of the report; and the decision to submit the report for publication  ... 
doi:10.1016/j.ahj.2016.10.026 pmid:28267469 pmcid:PMC5341136 fatcat:b2ht4tle7vctleisqa6h6eyd6m

A Conditional Inference Tree Model for Predicting Sleep-Related Breathing Disorders in Patients With Chiari Malformation Type 1: Description and External Validation

Álex Ferré, María A. Poca, María Dolores de la Calzada, Dulce Moncho, Aintzane Urbizu, Odile Romero, Gabriel Sampol, Juan Sahuquillo
2019 Journal of Clinical Sleep Medicine (JCSM)  
The aim of this study is to generate and validate supervised machine learning algorithms to detect patients with Chiari malformation (CM) 1 or 1.5 at high risk of the development of sleep-related breathing  ...  disorders (SRBD) using clinical and neuroradiological parameters.  ...  As in the training cohort, both MLR and URP-CTREE models were accurate in predicting patients with an RDI ≥ 10 events/h.  ... 
doi:10.5664/jcsm.7578 pmid:30621833 pmcid:PMC6329533 fatcat:og6vmlatcjddha3q63ir4m5j2y

Introduction to Rare-Event Predictive Modeling for Inferential Statisticians – A Hands-On Application in the Prediction of Breakthrough Patents [article]

Daniel Hain, Roman Jurowetzki
2020 arXiv   pre-print
We discuss fundamental concepts in predictive modeling, such as out-of-sample model validation, variable and model selection, generalization and hyperparameter tuning procedures.  ...  We use the example of high-quality patent identification guiding the reader through various model classes and procedures for data pre-processing, modelling and validation.  ...  but does not allow to make causal inference.  ... 
arXiv:2003.13441v1 fatcat:3rslvy7kkbdmlool4vgpnhlhwu

AIMD - a validated, simplified framework of interventions to promote and integrate evidence into health practices, systems, and policies

Peter Bragge, Jeremy M. Grimshaw, Cynthia Lokker, Heather Colquhoun
2017 BMC Medical Research Methodology  
To address this, an international Terminology Working Group developed and published a simplified framework of interventions to promote and integrate evidence into health practices, systems, and policies  ...  Analysis of primary studies revealed that representativeness of this concept lowered from 92 to 68% if only explicit, rather than explicit and non-explicit references to causal mechanisms were included  ...  Acknowledgements The authors would like to acknowledge the contributions of: • Stuart Nelson, who participated in the development of the simplified framework validated within this manuscript; and  ... 
doi:10.1186/s12874-017-0314-8 pmid:28259155 pmcid:PMC5336675 fatcat:v6pma5oiqrf5njj2nphp7qxlby

Integrating Evolutionary, Cultural, and Computational Psychiatry: A Multilevel Systemic Approach

Axel Constant, Paul Badcock, Karl Friston, Laurence J. Kirmayer
2022 Frontiers in Psychiatry  
We apply the resulting Evolutionary, Cultural and Computational (ECC) model to Major Depressive Disorder (MDD) to illustrate how this integrative approach can guide research and practice in psychiatry.  ...  This paper proposes an integrative perspective on evolutionary, cultural and computational approaches to psychiatry.  ...  PB, LK, and KF assisted each in turn in the revision and modification of the first draft. All authors contributed to the article and approved the submitted version.  ... 
doi:10.3389/fpsyt.2022.763380 pmid:35444580 pmcid:PMC9013887 fatcat:sfhqacq4y5ci7izh7ol7llbg6u

Identification of Causal Genetic Drivers of Human Disease through Systems-Level Analysis of Regulatory Networks

James C. Chen, Mariano J. Alvarez, Flaminia Talos, Harshil Dhruv, Gabrielle E. Rieckhof, Archana Iyer, Kristin L. Diefes, Kenneth Aldape, Michael Berens, Michael M. Shen, Andrea Califano
2014 Cell  
Identification of driver mutations in human diseases is often limited by cohort size and availability of appropriate statistical models.  ...  Deletions of KLHL9 were confirmed in > 50% of mesenchymal cases in an independent cohort, thus representing the most frequent genetic determinant of the subtype.  ...  of regulatory clues to guide mechanistic validation.  ... 
doi:10.1016/j.cell.2014.09.021 pmid:25303533 pmcid:PMC4194029 fatcat:42vuas3lwjfspjjqtd4efar4ee

Identification of Causal Genetic Drivers of Human Disease through Systems-Level Analysis of Regulatory Networks

James C. Chen, Mariano J. Alvarez, Flaminia Talos, Harshil Dhruv, Gabrielle E. Rieckhof, Archana Iyer, Kristin L. Diefes, Kenneth Aldape, Michael Berens, Michael M. Shen, Andrea Califano
2016 Cell  
Identification of driver mutations in human diseases is often limited by cohort size and availability of appropriate statistical models.  ...  Deletions of KLHL9 were confirmed in > 50% of mesenchymal cases in an independent cohort, thus representing the most frequent genetic determinant of the subtype.  ...  of regulatory clues to guide mechanistic validation.  ... 
doi:10.1016/j.cell.2016.07.036 pmid:27518566 fatcat:dr6mm3p7bjbl7lzs5js3pze5qi

Using computable knowledge mined from the literature to elucidate confounders for EHR-based pharmacovigilance [article]

Scott Alexander Malec, Elmer Victor Bernstam, Peng Wei, Trevor Cohen, Richard David Boyce
2020 medRxiv   pre-print
data to predict and estimate causal effects in light of the literature-derived confounders.  ...  For evaluation, we attempt to rediscover associations in a publicly available reference dataset containing expected pairwise relationships between drugs and adverse events from empirical data derived from  ...  Components of a causal inference toolkit In the next section, we introduce background material vital to understanding our methods in terms of the essential components and procedures.  ... 
doi:10.1101/2020.07.08.20113035 fatcat:symhqu3l25ayjdoxy457del6jq

The role of causal criteria in causal inferences: Bradford Hill's "aspects of association"

Andrew C Ward
2009 Epidemiologic Perspectives & Innovations  
I argue that while the use of causal criteria is not appropriate for either deductive or inductive inferences, they do have an important role to play in inferences to the best explanation.  ...  As such, causal criteria, exemplified by what Bradford Hill referred to as "aspects of [statistical] associations", have an indispensible part to play in the goal of making justified causal claims.  ...  Acknowledgements I acknowledge and thank Professor George Maldonado and three anonymous referees for their useful comments on earlier versions of the manuscript.  ... 
doi:10.1186/1742-5573-6-2 pmid:19534788 pmcid:PMC2706236 fatcat:poqc2psfnvanncrw3rqwzo65wa

High-Dimensional Feature Selection for Sample Efficient Treatment Effect Estimation [article]

Kristjan Greenewald, Dmitriy Katz-Rogozhnikov, Karthik Shanmugam
2020 arXiv   pre-print
model for Y and subgaussian covariates for each of the treatment cohort.  ...  The estimation of causal treatment effects from observational data is a fundamental problem in causal inference. To avoid bias, the effect estimator must control for all confounders.  ...  H., Meade, D., and Goldschmidt, Y. (2019). An evaluation toolkit to guide model selection and cohort definition in causal inference. arXiv preprint arXiv:1906.00442.  ... 
arXiv:2011.01979v1 fatcat:aofp5vl7tvdw5hwccloisjt2yu

Draft for internal testing Scientific Committee guidance on appraising and integrating evidence from epidemiological studies for use in EFSA's scientific assessments

EFSA Scientific Committee, Simon More, Vasileos Bambidis, Diane Benford, Claude Bragard, Antonio Hernandez-Jerez, Susanne Hougaard Bennekou, Kostas Koutsoumanis, Kyriaki Machera, Hanspeter Naegeli, Soren Saxmose Nielsen, Josef R Schlatter (+13 others)
2020 EFSA Journal  
A decision tree is developed to assist in the selection of the appropriate Risk of Bias tool, depending on study question, population and design.  ...  The guidance document provides an introduction to epidemiological studies and illustrates the typical biases of the different epidemiological study designs.  ...  Causality and causal inference in epidemiology: the need for a pluralistic approach.  ... 
doi:10.2903/j.efsa.2020.6221 pmid:32831946 pmcid:PMC7433401 fatcat:3hkvrha2qzbyvexpqww5pkz7qi
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