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Understanding Medical Literature

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16 papers 25 to 100 followers Papers related to explaining clinical trial methodology, statistics, and evidence-based medicine
By Aaron Guinn Canadian emergency medicine physician - clinical diagnosis, sepsis and tox
https://www.readbyqxmd.com/read/24701606/7-questions-to-ask-when-evaluating-a-noninferiority-trial
#1
COMPARATIVE STUDY
Anne Mounsey, Anthony J Viera, Rosalie Dominik
No abstract text is available yet for this article.
March 2014: Journal of Family Practice
https://www.readbyqxmd.com/read/25393702/coenrollment-in-a-randomized-trial-of-high-frequency-oscillation-prevalence-patterns-predictors-and-outcomes
#2
Deborah J Cook, Niall D Ferguson, Lori Hand, Peggy Austin, Qi Zhou, Neill K J Adhikari, Valerie Danesh, Yaseen Arabi, Andrea L Matte, France E Clarke, Sangeeta Mehta, Orla Smith, Matt P Wise, Jan O Friedrich, Sean P Keenan, Steven Hanna, Maureen O Meade
OBJECTIVE: Enrollment of individual patients into more than one study has been poorly evaluated. The objective of this study was to describe the characteristics of patients, researchers and centers involved in coenrollment, studies precluding coenrollment, and the prevalence, patterns, predictors, and outcomes of coenrollment in a randomized clinical trial. DESIGN, SETTING, METHODS: We conducted an observational study nested within the OSCILLation for Acute Respiratory Distress Syndrome Treated Early Trial, which compared high-frequency oscillatory ventilation to conventional ventilation...
February 2015: Critical Care Medicine
https://www.readbyqxmd.com/read/25156480/critically-appraising-noninferiority-randomized-controlled-trials-a-primer-for-emergency-physicians
#3
Mohammad Al Deeb, Aftab Azad, David Barbic
ABSTRACTNoninferiority (NI) trials aim to show that a new treatment or drug is not inferior to a standard, accepted treatment. The rapid proliferation of NI trials within the literature makes it imperative for emergency physicians to be able to read, interpret, and appraise critically this type of research study. Using several emergency medicine examples from the recent literature, this article outlines the key differences between traditional, superiority randomized controlled trials and NI trials. We summarize four important points that an emergency physician should consider when critically appraising an NI trial: 1) Does the new treatment have tangible benefits over the standard treatment? 2) Was the choice of the NI margin appropriate? 3) Was the effect of the standard treatment preserved? Does the trial have assay sensitivity? and 4) What type of analysis strategy was employed: intention-to-treat (ITT) or per protocol (PP)?...
August 2014: CJEM
https://www.readbyqxmd.com/read/25203082/reanalyses-of-randomized-clinical-trial-data
#4
REVIEW
Shanil Ebrahim, Zahra N Sohani, Luis Montoya, Arnav Agarwal, Kristian Thorlund, Edward J Mills, John P A Ioannidis
IMPORTANCE: Reanalyses of randomized clinical trial (RCT) data may help the scientific community assess the validity of reported trial results. OBJECTIVES: To identify published reanalyses of RCT data, to characterize methodological and other differences between the original trial and reanalysis, to evaluate the independence of authors performing the reanalyses, and to assess whether the reanalysis changed interpretations from the original article about the types or numbers of patients who should be treated...
September 10, 2014: JAMA: the Journal of the American Medical Association
https://www.readbyqxmd.com/read/22364798/evidence-based-practice-in-nephrology-critical-appraisal-of-nephrology-clinical-research-were-the-correct-statistical-tests-used
#5
REVIEW
Amber S Podoll, Cynthia S Bell, Donald A Molony
Nephrologists rely on valid clinical studies to inform their health care decisions. Knowledge of simple statistical principles equips the prudent nephrologist with the skills that allow him or her to critically evaluate clinical studies and to determine the validity of the results. Important in this process is knowing when certain statistical tests are used appropriately and if their application in interpreting research data will most likely lead to the most robust or valid conclusions. The research team bears the responsibility for determining the statistical analysis during the design phase of the study and subsequently for carrying out the appropriate analysis...
January 2012: Advances in Chronic Kidney Disease
https://www.readbyqxmd.com/read/3132649/analysis-of-data-in-nephrology-i-choosing-the-correct-statistical-test-dichotomous-variables
#6
D R Appleton
This is the first of six articles describing how to choose the correct statistical test to look for relationships between two variables. All examples relate to clinical or laboratory aspects of nephrology. In this article the overall strategy is outlined, and examples are given of analyses of data where each variable can take only two values.
1988: Nephrology, Dialysis, Transplantation
https://www.readbyqxmd.com/read/3140092/analysis-of-data-in-nephrology-ii-when-to-use-the-t-test
#7
D R Appleton
This is the second of a series of six articles describing how to choose the most appropriate statistical method to test for relationships between two variables. When the response variable is continuous and the explanatory variable is dichotomous, the t-test is often used. The article indicates when this is really appropriate and when the test should be modified or replaced by another.
1988: Nephrology, Dialysis, Transplantation
https://www.readbyqxmd.com/read/3140139/analysis-of-data-in-nephrology-iv-relationships-between-continuous-variables
#8
D R Appleton
In this article the subjects of linear regression, non-linear curve fitting, and correlation are covered. Examples, as in the other articles in the series, are all taken from clinical or laboratory investigations in nephrology.
1988: Nephrology, Dialysis, Transplantation
https://www.readbyqxmd.com/read/25058221/the-intention-to-treat-principle-how-to-assess-the-true-effect-of-choosing-a-medical-treatment
#9
COMMENT
Michelle A Detry, Roger J Lewis
No abstract text is available yet for this article.
July 2, 2014: JAMA: the Journal of the American Medical Association
https://www.readbyqxmd.com/read/25027390/nnt-is-highly-misleading-when-assessing-chronic-disease-prevention
#10
LETTER
Timo E Strandberg
No abstract text is available yet for this article.
2014: BMJ: British Medical Journal
https://www.readbyqxmd.com/read/25030633/subgroup-analyses-in-randomised-controlled-trials-cohort-study-on-trial-protocols-and-journal-publications
#11
Benjamin Kasenda, Stefan Schandelmaier, Xin Sun, Erik von Elm, John You, Anette Blümle, Yuki Tomonaga, Ramon Saccilotto, Alain Amstutz, Theresa Bengough, Joerg J Meerpohl, Mihaela Stegert, Kelechi K Olu, Kari A O Tikkinen, Ignacio Neumann, Alonso Carrasco-Labra, Markus Faulhaber, Sohail M Mulla, Dominik Mertz, Elie A Akl, Dirk Bassler, Jason W Busse, Ignacio Ferreira-González, Francois Lamontagne, Alain Nordmann, Viktoria Gloy, Heike Raatz, Lorenzo Moja, Rachel Rosenthal, Shanil Ebrahim, Per O Vandvik, Bradley C Johnston, Martin A Walter, Bernard Burnand, Matthias Schwenkglenks, Lars G Hemkens, Heiner C Bucher, Gordon H Guyatt, Matthias Briel
OBJECTIVE: To investigate the planning of subgroup analyses in protocols of randomised controlled trials and the agreement with corresponding full journal publications. DESIGN: Cohort of protocols of randomised controlled trial and subsequent full journal publications. SETTING: Six research ethics committees in Switzerland, Germany, and Canada. DATA SOURCES: 894 protocols of randomised controlled trial involving patients approved by participating research ethics committees between 2000 and 2003 and 515 subsequent full journal publications...
2014: BMJ: British Medical Journal
https://www.readbyqxmd.com/read/25023252/potential-bias-of-instrumental-variable-analyses-for-observational-comparative-effectiveness-research
#12
REVIEW
Laura Faden Garabedian, Paula Chu, Sengwee Toh, Alan M Zaslavsky, Stephen B Soumerai
Instrumental variable analysis is an increasingly popular method in comparative effectiveness research (CER). In theory, the instrument controls for unobserved and observed patient characteristics that affect the outcome. However, the results of instrumental variable analyses in observational settings may be biased if the instrument and outcome are related through an unadjusted third variable: an "instrument-outcome confounder." The authors identified published CER studies that used instrumental variable analysis and searched the literature for potential confounders of the most common instrument-outcome pairs...
July 15, 2014: Annals of Internal Medicine
https://www.readbyqxmd.com/read/25015369/understanding-p-values
#13
Philip Sedgwick
No abstract text is available yet for this article.
2014: BMJ: British Medical Journal
https://www.readbyqxmd.com/read/25003609/avoiding-nocebo-effects-to-optimize-treatment-outcome
#14
Ulrike Bingel
No abstract text is available yet for this article.
August 20, 2014: JAMA: the Journal of the American Medical Association
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
#15
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/25005655/sample-size-calculation-for-a-hypothesis-test
#16
Lynne Stokes
No abstract text is available yet for this article.
July 2014: JAMA: the Journal of the American Medical Association
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