collection
MENU ▼
Read by QxMD icon Read
search

statistica

shared collection
129 papers 25 to 100 followers
https://www.readbyqxmd.com/read/28476137/understanding-the-null-hypothesis-h0-in-non-inferiority-trials
#1
LETTER
Jihad Mallat
No abstract text is available yet for this article.
May 6, 2017: Critical Care: the Official Journal of the Critical Care Forum
https://www.readbyqxmd.com/read/28464123/the-complex-and-multifaceted-aspects-of-conflicts-of-interest
#2
EDITORIAL
William W Stead
No abstract text is available yet for this article.
May 2, 2017: JAMA: the Journal of the American Medical Association
https://www.readbyqxmd.com/read/28437532/the-value-of-using-registries-to-evaluate-randomized-clinical-trial-study-populations
#3
Adam J Schoenfeld, Rita F Redberg
No abstract text is available yet for this article.
April 24, 2017: JAMA Internal Medicine
https://www.readbyqxmd.com/read/28166709/biomarker-validation-with-an-imperfect-reference-issues-and-bounds
#4
Sarah C Emerson, Sushrut S Waikar, Claudio Fuentes, Joseph V Bonventre, Rebecca A Betensky
Motivated by the goal of evaluating a biomarker for acute kidney injury, we consider the problem of assessing operating characteristics for a new biomarker when a true gold standard for disease status is unavailable. In this case, the biomarker is typically compared to another imperfect reference test, and this comparison is used to estimate the performance of the new biomarker. However, errors made by the reference test can bias assessment of the new test. Analysis methods like latent class analysis have been proposed to address this issue, generally employing some strong and unverifiable assumptions regarding the relationship between the new biomarker and the reference test...
January 1, 2017: Statistical Methods in Medical Research
https://www.readbyqxmd.com/read/28192563/evaluation-of-evidence-of-statistical-support-and-corroboration-of-subgroup-claims-in-randomized-clinical-trials
#5
Joshua D Wallach, Patrick G Sullivan, John F Trepanowski, Kristin L Sainani, Ewout W Steyerberg, John P A Ioannidis
Importance: Many published randomized clinical trials (RCTs) make claims for subgroup differences. Objective: To evaluate how often subgroup claims reported in the abstracts of RCTs are actually supported by statistical evidence (P < .05 from an interaction test) and corroborated by subsequent RCTs and meta-analyses. Data Sources: This meta-epidemiological survey examines data sets of trials with at least 1 subgroup claim, including Subgroup Analysis of Trials Is Rarely Easy (SATIRE) articles and Discontinuation of Randomized Trials (DISCO) articles...
April 1, 2017: JAMA Internal Medicine
https://www.readbyqxmd.com/read/28192565/acknowledging-and-overcoming-nonreproducibility-in-basic-and-preclinical-research
#6
John P A Ioannidis
No abstract text is available yet for this article.
March 14, 2017: JAMA: the Journal of the American Medical Association
https://www.readbyqxmd.com/read/28196262/language-for-actionable-recommendations-in-clinical-guidelines-avoiding-hedging-and-equivocation
#7
Richard S Klasco, Lewis H Glinert
No abstract text is available yet for this article.
February 14, 2017: JAMA: the Journal of the American Medical Association
https://www.readbyqxmd.com/read/28196238/when-will-mendelian-randomization-become-relevant-for-clinical-practice-and-public-health
#8
EDITORIAL
George Davey Smith, Lavinia Paternoster, Caroline Relton
No abstract text is available yet for this article.
February 14, 2017: JAMA: the Journal of the American Medical Association
https://www.readbyqxmd.com/read/28196236/clinical-practice-guidelines-expanded-use-and-misuse
#9
EDITORIAL
Sheldon Greenfield
No abstract text is available yet for this article.
February 14, 2017: JAMA: the Journal of the American Medical Association
https://www.readbyqxmd.com/read/28199802/adjusting-risk-adjustment-accounting-for-variation-in-diagnostic-intensity
#10
Amy Finkelstein, Matthew Gentzkow, Peter Hull, Heidi Williams
In the U.S. health care system, payments and performance measures are often adjusted to account for differences in patients’ baseline health and demographic characteristics. The idea behind such risk adjustments is to create a level playing field, so that providers aren’t penalized for serving..
February 16, 2017: New England Journal of Medicine
https://www.readbyqxmd.com/read/28241362/adjusted-analyses-in-studies-addressing-therapy-and-harm-users-guides-to-the-medical-literature
#11
Thomas Agoritsas, Arnaud Merglen, Nilay D Shah, Martin O'Donnell, Gordon H Guyatt
Observational studies almost always have bias because prognostic factors are unequally distributed between patients exposed or not exposed to an intervention. The standard approach to dealing with this problem is adjusted or stratified analysis. Its principle is to use measurement of risk factors to create prognostically homogeneous groups and to combine effect estimates across groups.The purpose of this Users' Guide is to introduce readers to fundamental concepts underlying adjustment as a way of dealing with prognostic imbalance and to the basic principles and relative trustworthiness of various adjustment strategies...
February 21, 2017: JAMA: the Journal of the American Medical Association
https://www.readbyqxmd.com/read/28291878/logistic-regression-diagnostics-understanding-how-well-a-model-predicts-outcomes
#12
COMMENT
William J Meurer, Juliana Tolles
No abstract text is available yet for this article.
March 14, 2017: JAMA: the Journal of the American Medical Association
https://www.readbyqxmd.com/read/28355503/from-trial-to-target-populations-calibrating-real-world-data
#13
Mehdi Najafzadeh, Sebastian Schneeweiss
Patient risk factors modify the outcomes of many treatments. Patients with a high risk of stroke may benefit more from anticoagulation therapy than those with minor risk. So it is often unclear to what extent results of clinical trials that were conducted in selected populations are applicable to..
March 30, 2017: New England Journal of Medicine
https://www.readbyqxmd.com/read/28143814/what-is-propensity-score-modelling
#14
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
#15
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
#16
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
#17
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
#18
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
#19
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
#20
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
label_collection
label_collection
5245
1
2
2017-01-17 00:12:19
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read
×

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"