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https://www.readbyqxmd.com/read/28143814/what-is-propensity-score-modelling
#1
EDITORIAL
Michael J Campbell
Propensity score methodology is being increasingly used to try and make inferences about treatments when randomised trials are either impossible or not conducted and the only data are from observational studies. This paper reviews the basis of propensity scores and the current state of knowledge about them. It uses and critiques a current paper in the Emergency Medicine Journal to illustrate the methodology.
March 2017: Emergency Medicine Journal: EMJ
https://www.readbyqxmd.com/read/28179372/graphics-and-statistics-for-cardiology-clinical-prediction-rules
#2
REVIEW
Mark Woodward, Hugh Tunstall-Pedoe, Sanne Ae Peters
Graphs and tables are indispensable aids to quantitative research. When developing a clinical prediction rule that is based on a cardiovascular risk score, there are many visual displays that can assist in developing the underlying statistical model, testing the assumptions made in this model, evaluating and presenting the resultant score. All too often, researchers in this field follow formulaic recipes without exploring the issues of model selection and data presentation in a meaningful and thoughtful way...
April 2017: Heart: Official Journal of the British Cardiac Society
https://www.readbyqxmd.com/read/28099837/cost-effectiveness-analysis-2-0
#3
Peter J Neumann, Gillian D Sanders
Cost-effectiveness analysis in U.S. health care seems poised for a second act of sorts. Although it has never actually gone away, efforts to apply it have encountered resistance, and the federal government and some health care organizations have sometimes prohibited its use or relegated it to a..
January 19, 2017: New England Journal of Medicine
https://www.readbyqxmd.com/read/28098639/why-so-few-randomized-trials-are-useful
#4
Michael J Lanspa, Alan H Morris
No abstract text is available yet for this article.
February 2017: Critical Care Medicine
https://www.readbyqxmd.com/read/28039123/interpretation-of-surrogate-endpoints-in-the-era-of-the-21st-century-cures-act
#5
Kevin Knopf, Michael Baum, William S Shimp, Charles L Bennett, Dinah Faith, Marc L Fishman, William J M Hrushesky
No abstract text is available yet for this article.
December 30, 2016: BMJ: British Medical Journal
https://www.readbyqxmd.com/read/28057641/a-guide-to-systematic-review-and-meta-analysis-of-prediction-model-performance
#6
Thomas P A Debray, Johanna A A G Damen, Kym I E Snell, Joie Ensor, Lotty Hooft, Johannes B Reitsma, Richard D Riley, Karel G M Moons
No abstract text is available yet for this article.
January 5, 2017: BMJ: British Medical Journal
https://www.readbyqxmd.com/read/28082309/graphics-and-statistics-for-cardiology-survival-analysis
#7
REVIEW
Susanne May, Barbara McKnight
Reports of data in the medical literature frequently lack information needed to assess the validity and generalisability of study results. Some recommendations and standards for reporting have been developed over the last two decades, but few are available specifically for survival data. We provide recommendations for tabular and graphical representations of survival data. We argue that data and analytic software should be made available to promote reproducible research.
March 2017: Heart: Official Journal of the British Cardiac Society
https://www.readbyqxmd.com/read/28027342/trust-but-verify-ideally-with-a-randomized-clinical-trial
#8
Mitchell H Katz
No abstract text is available yet for this article.
February 1, 2017: JAMA Internal Medicine
https://www.readbyqxmd.com/read/28055041/a-tale-of-two-trials-reconciling-differences-in-results-by-exploring-heterogeneous-treatment-effects
#9
Sanjay Kaul
No abstract text is available yet for this article.
January 3, 2017: Annals of Internal Medicine
https://www.readbyqxmd.com/read/28064161/statistical-significance-versus-clinical-relevance
#10
REVIEW
Marieke H C van Rijn, Anneke Bech, Jean Bouyer, Jan A J G van den Brand
In March this year, the American Statistical Association (ASA) posted a statement on the correct use of P-values, in response to a growing concern that the P-value is commonly misused and misinterpreted. We aim to translate these warnings given by the ASA into a language more easily understood by clinicians and researchers without a deep background in statistics. Moreover, we intend to illustrate the limitations of P-values, even when used and interpreted correctly, and bring more attention to the clinical relevance of study findings using two recently reported studies as examples...
January 7, 2017: Nephrology, Dialysis, Transplantation
https://www.readbyqxmd.com/read/25005654/how-to-read-a-systematic-review-and-meta-analysis-and-apply-the-results-to-patient-care-users-guides-to-the-medical-literature
#11
Mohammad Hassan Murad, Victor M Montori, John P A Ioannidis, Roman Jaeschke, P J Devereaux, Kameshwar Prasad, Ignacio Neumann, Alonso Carrasco-Labra, Thomas Agoritsas, Rose Hatala, Maureen O Meade, Peter Wyer, Deborah J Cook, Gordon Guyatt
Clinical decisions should be based on the totality of the best evidence and not the results of individual studies. When clinicians apply the results of a systematic review or meta-analysis to patient care, they should start by evaluating the credibility of the methods of the systematic review, ie, the extent to which these methods have likely protected against misleading results. Credibility depends on whether the review addressed a sensible clinical question; included an exhaustive literature search; demonstrated reproducibility of the selection and assessment of studies; and presented results in a useful manner...
July 2014: JAMA: the Journal of the American Medical Association
https://www.readbyqxmd.com/read/27733354/robins-i-a-tool-for-assessing-risk-of-bias-in-non-randomised-studies-of-interventions
#12
Jonathan Ac Sterne, Miguel A Hernán, Barnaby C Reeves, Jelena Savović, Nancy D Berkman, Meera Viswanathan, David Henry, Douglas G Altman, Mohammed T Ansari, Isabelle Boutron, James R Carpenter, An-Wen Chan, Rachel Churchill, Jonathan J Deeks, Asbjørn Hróbjartsson, Jamie Kirkham, Peter Jüni, Yoon K Loke, Theresa D Pigott, Craig R Ramsay, Deborah Regidor, Hannah R Rothstein, Lakhbir Sandhu, Pasqualina L Santaguida, Holger J Schünemann, Beverly Shea, Ian Shrier, Peter Tugwell, Lucy Turner, Jeffrey C Valentine, Hugh Waddington, Elizabeth Waters, George A Wells, Penny F Whiting, Julian Pt Higgins
No abstract text is available yet for this article.
October 12, 2016: BMJ: British Medical Journal
https://www.readbyqxmd.com/read/27758792/beyond-open-data-realising-the-health-benefits-of-sharing-data
#13
Elizabeth Pisani, Peter Aaby, J Gabrielle Breugelmans, David Carr, Trish Groves, Michelle Helinski, Dorcas Kamuya, Steven Kern, Katherine Littler, Vicki Marsh, Souleymane Mboup, Laura Merson, Osman Sankoh, Micaela Serafini, Martin Schneider, Vreni Schoenenberger, Philippe J Guerin
No abstract text is available yet for this article.
October 10, 2016: BMJ: British Medical Journal
https://www.readbyqxmd.com/read/27777221/is-this-trial-misreported-truth-seeking-in-the-burgeoning-age-of-trial-transparency
#14
Peter Doshi
No abstract text is available yet for this article.
October 24, 2016: BMJ: British Medical Journal
https://www.readbyqxmd.com/read/27777223/consort-2010-statement-extension-to-randomised-pilot-and-feasibility-trials
#15
Sandra M Eldridge, Claire L Chan, Michael J Campbell, Christine M Bond, Sally Hopewell, Lehana Thabane, Gillian A Lancaster
No abstract text is available yet for this article.
October 24, 2016: BMJ: British Medical Journal
https://www.readbyqxmd.com/read/27789483/very-large-treatment-effects-in-randomised-trials-as-an-empirical-marker-to-indicate-whether-subsequent-trials-are-necessary-meta-epidemiological-assessment
#16
Myura Nagendran, Tiago V Pereira, Grace Kiew, Douglas G Altman, Mahiben Maruthappu, John P A Ioannidis, Peter McCulloch
OBJECTIVE:  To examine whether a very large effect (VLE; defined as a relative risk of ≤0.2 or ≥5) in a randomised trial could be an empirical marker that subsequent trials are unnecessary. DESIGN:  Meta-epidemiological assessment of existing published data on randomised trials. DATA SOURCES:  Cochrane Database of Systematic Reviews (2010, issue 7) with data on subsequent large trials updated to 2015, issue 12. ELIGIBILITY CRITERIA:  All binary outcome forest plots were selected, which contained an index randomised trial with a VLE that was nominally statistically significant (P<0...
October 27, 2016: BMJ: British Medical Journal
https://www.readbyqxmd.com/read/27797786/towards-evidence-based-research
#17
Hans Lund, Klara Brunnhuber, Carsten Juhl, Karen Robinson, Marlies Leenaars, Bertil F Dorch, Gro Jamtvedt, Monica W Nortvedt, Robin Christensen, Iain Chalmers
No abstract text is available yet for this article.
October 21, 2016: BMJ: British Medical Journal
https://www.readbyqxmd.com/read/27815253/nearly-half-of-all-trials-run-by-major-sponsors-in-past-decade-are-unpublished
#18
Gareth Iacobucci
No abstract text is available yet for this article.
November 4, 2016: BMJ: British Medical Journal
https://www.readbyqxmd.com/read/27779514/patient-eligibility-for-randomized-controlled-trials-in-critical-care-medicine-an-international-two-center-observational-study
#19
Ryan M J Ivie, Emily A Vail, Hannah Wunsch, Monica P Goldklang, Robert Fowler, Vivek K Moitra
OBJECTIVE: We conducted this study to determine the generalizability of information gained from randomized controlled trials in critically ill patients by assessing the incidence of eligibility for each trial. DESIGN: Prospective, observational cohort study. We identified the 15 most highly cited randomized controlled trials in critical care medicine published between 1998 and 2008. We examined the inclusion and exclusion criteria for each randomized controlled trial and then assessed the eligibility of each patient admitted to a study ICU for each randomized controlled trial and calculated rates of potential trial eligibility in the cohort...
February 2017: Critical Care Medicine
https://www.readbyqxmd.com/read/27742808/how-robust-are-clinical-trials-in-heart-failure
#20
Kieran F Docherty, Ross T Campbell, Pardeep S Jhund, Mark C Petrie, John J V McMurray
AIMS: Guidelines for the management of chronic heart failure (CHF) cite the results of randomized controlled trials (RCTs) to support treatment recommendations. The significance of an observed treatment-effect relies on the use of a boundary P-value, most commonly P < 0.05. There is concern about relying on arbitrary threshold P-values to report results as 'statistically significant'. The 'fragility index' (FI) has been proposed as an additional measure of the robustness of trial findings...
October 14, 2016: European Heart Journal
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