collection
MENU ▼
Read by QxMD icon Read
search

Statistics and Nephrology

shared collection
227 papers 100 to 500 followers
By Isabel Acosta-Ochoa Nephrology senior staff. Valladolid. Spain
https://www.readbyqxmd.com/read/28418504/alternative-facts-have-no-place-in-science
#1
Christine Laine, Darren B Taichman
No abstract text is available yet for this article.
April 18, 2017: Annals of Internal Medicine
https://www.readbyqxmd.com/read/28403129/singing-a-new-song-outcomes-for-clinical-trials
#2
Andrew F Malone, Daniel C Brennan
No abstract text is available yet for this article.
April 11, 2017: Transplantation
https://www.readbyqxmd.com/read/28396115/improved-clinical-trial-enrollment-criterion-to-identify-patients-with-diabetes-at-risk-of-end-stage-renal-disease
#3
Masayuki Yamanouchi, Jan Skupien, Monika A Niewczas, Adam M Smiles, Alessandro Doria, Robert C Stanton, Andrzej T Galecki, Kevin L Duffin, Nick Pullen, Matthew D Breyer, Joseph V Bonventre, James H Warram, Andrzej S Krolewski
Design of Phase III trials for diabetic nephropathy currently requires patients at a high risk of progression defined as within three years of a hard end point (end-stage renal disease, 40% loss of estimated glomerular filtration rate, or death). To improve the design of these trials, we used natural history data from the Joslin Kidney Studies of chronic kidney disease in patients with diabetes to develop an improved criterion to identify such patients. This included a training cohort of 279 patients with type 1 diabetes and 134 end points within three years, and a validation cohort of 221 patients with type 2 diabetes and 88 end points...
April 7, 2017: Kidney International
https://www.readbyqxmd.com/read/28380638/when-is-a-meta-analysis-conclusive-a-guide-to-trial-sequential-analysis-with-an-example-of-remote-ischemic-preconditioning-for-renoprotection-in-patients-undergoing-cardiac-surgery
#4
Pavel S Roshanov, Brittany B Dennis, Nicholas Pasic, Amit X Garg, Michael Walsh
Regardless of whether a randomized trial finds a statistically significant effect for an intervention or not, readers often wonder if the trial was large enough to be conclusive. To answer this question, we can estimate the required sample size for a trial by considering how commonly the outcome occurs, the smallest effect of clinical importance and the acceptable risk of falsely detecting or rejecting that effect. But when is a meta-analysis conclusive? We explain and illustrate the interpretation of Trial Sequential Analysis (TSA), a method increasingly used to answer this question...
April 1, 2017: Nephrology, Dialysis, Transplantation
https://www.readbyqxmd.com/read/28355503/from-trial-to-target-populations-calibrating-real-world-data
#5
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/28340850/from-static-to-dynamic-risk-prediction-time-is-everything
#6
EDITORIAL
Tom Greene, Liang Li
No abstract text is available yet for this article.
April 2017: American Journal of Kidney Diseases: the Official Journal of the National Kidney Foundation
https://www.readbyqxmd.com/read/28339854/prediction-versus-aetiology-common-pitfalls-and-how-to-avoid-them
#7
Merel van Diepen, Chava L Ramspek, Kitty J Jager, Carmine Zoccali, Friedo W Dekker
Prediction research is a distinct field of epidemiologic research, which should be clearly separated from aetiological research. Both prediction and aetiology make use of multivariable modelling, but the underlying research aim and interpretation of results are very different. Aetiology aims at uncovering the causal effect of a specific risk factor on an outcome, adjusting for confounding factors that are selected based on pre-existing knowledge of causal relations. In contrast, prediction aims at accurately predicting the risk of an outcome using multiple predictors collectively, where the final prediction model is usually based on statistically significant, but not necessarily causal, associations in the data at hand...
April 1, 2017: Nephrology, Dialysis, Transplantation
https://www.readbyqxmd.com/read/28339913/relative-risk-versus-absolute-risk-one-cannot-be-interpreted-without-the-other
#8
Marlies Noordzij, Merel van Diepen, Fergus C Caskey, Kitty J Jager
For the presentation of risk, both relative and absolute measures can be used. The relative risk is most often used, especially in studies showing the effects of a treatment. Relative risks have the appealing feature of summarizing two numbers (the risk in one group and the risk in the other) into one. However, this feature also represents their major weakness, that the underlying absolute risks are concealed and readers tend to overestimate the effect when it is presented in relative terms. In many situations, the absolute risk gives a better representation of the actual situation and also from the patient's point of view absolute risks often give more relevant information...
April 1, 2017: Nephrology, Dialysis, Transplantation
https://www.readbyqxmd.com/read/28340135/modeling-longitudinal-data-and-its-impact-on-survival-in-observational-nephrology-studies-tools-and-considerations
#9
Elani Streja, Leanne Goldstein, Melissa Soohoo, Yoshitsugu Obi, Kamyar Kalantar-Zadeh, Connie M Rhee
Nephrologists and kidney disease researchers are often interested in monitoring how patients' clinical and laboratory measures change over time, what factors may impact these changes, and how these changes may lead to differences in morbidity, mortality, and other outcomes. When longitudinal data with repeated measures over time in the same patients are available, there are a number of analytical approaches that could be employed to describe the trends and changes in these measures, and to explore the associations of these changes with outcomes...
April 1, 2017: Nephrology, Dialysis, Transplantation
https://www.readbyqxmd.com/read/28339826/the-importance-of-considering-competing-treatment-affecting-prognosis-in-the-evaluation-of-therapy-in-trials-the-example-of-renal-transplantation-in-hemodialysis-trials
#10
C Marijn Hazelbag, Sanne A E Peters, Peter J Blankestijn, Michiel L Bots, Bernard Canaud, Andrew Davenport, Muriel P C Grooteman, Fatih Kircelli, Francesco Locatelli, Francisco Maduell, Marion Morena, Menso J Nubé, Ercan Ok, Ferran Torres, Arno W Hoes, Rolf H H Groenwold
Background.: During the follow-up in a randomized controlled trial (RCT), participants may receive additional (non-randomly allocated) treatment that affects the outcome. Typically such additional treatment is not taken into account in evaluation of the results. Two pivotal trials of the effects of hemodiafiltration (HDF) versus hemodialysis (HD) on mortality in patients with end-stage renal disease reported differing results. We set out to evaluate to what extent methods to take other treatments (i...
April 1, 2017: Nephrology, Dialysis, Transplantation
https://www.readbyqxmd.com/read/28291878/logistic-regression-diagnostics-understanding-how-well-a-model-predicts-outcomes
#11
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/28241362/adjusted-analyses-in-studies-addressing-therapy-and-harm-users-guides-to-the-medical-literature
#12
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/28242844/statistical-methods-for-cohort-studies-of-ckd-survival-analysis-in-the-setting-of-competing-risks
#13
Jesse Yenchih Hsu, Jason A Roy, Dawei Xie, Wei Yang, Haochang Shou, Amanda Hyre Anderson, J Richard Landis, Christopher Jepson, Myles Wolf, Tamara Isakova, Mahboob Rahman, Harold I Feldman
Survival analysis is commonly used to evaluate factors associated with time to an event of interest (e.g., ESRD, cardiovascular disease, and mortality) among CKD populations. Time to the event of interest is typically observed only for some participants. Other participants have their event time censored because of the end of the study, death, withdrawal from the study, or some other competing event. Classic survival analysis methods, such as Cox proportional hazards regression, rely on the assumption that any censoring is independent of the event of interest...
February 27, 2017: Clinical Journal of the American Society of Nephrology: CJASN
https://www.readbyqxmd.com/read/28199802/adjusting-risk-adjustment-accounting-for-variation-in-diagnostic-intensity
#14
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/28196236/clinical-practice-guidelines-expanded-use-and-misuse
#15
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/28196238/when-will-mendelian-randomization-become-relevant-for-clinical-practice-and-public-health
#16
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/28196262/language-for-actionable-recommendations-in-clinical-guidelines-avoiding-hedging-and-equivocation
#17
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/28195044/research-misconduct-and-the-intergrowth-21st-study
#18
Richard Horton, Sabine Kleinert, Sarah Linklater, Zoë Mullan
No abstract text is available yet for this article.
February 11, 2017: Lancet
https://www.readbyqxmd.com/read/28192565/acknowledging-and-overcoming-nonreproducibility-in-basic-and-preclinical-research
#19
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/28192563/evaluation-of-evidence-of-statistical-support-and-corroboration-of-subgroup-claims-in-randomized-clinical-trials
#20
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
label_collection
label_collection
1755
1
2
2017-02-15 14:57:25
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"