Literature DB >> 17653136

Measures of effect: relative risks, odds ratios, risk difference, and 'number needed to treat'.

G Tripepi1, K J Jager, F W Dekker, C Wanner, C Zoccali.   

Abstract

Epidemiological studies aim at assessing the relationship between exposures and outcomes. Clinicians are interested in knowing not only whether a link between a given exposure (e.g. smoking) and a certain outcome (e.g. myocardial infarction) is statistically significant, but also the magnitude of this relationship. The 'measures of effect' are indexes that summarize the strength of the link between exposures and outcomes and can help the clinician in taking decisions in every day clinical practice. In epidemiological studies, the effect of exposure can be measured both in relative and absolute terms. The risk ratio, the incidence rate ratio, and the odds ratio are relative measures of effect. Risk difference is an absolute measure of effect and it is calculated by subtracting the risk of the outcome in exposed individuals from that of unexposed.

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Year:  2007        PMID: 17653136     DOI: 10.1038/sj.ki.5002432

Source DB:  PubMed          Journal:  Kidney Int        ISSN: 0085-2538            Impact factor:   10.612


  15 in total

1.  Preemptive kidney transplantation: a propensity score matched cohort study.

Authors:  Masayoshi Okumi; Yasuyuki Sato; Kohei Unagami; Toshihito Hirai; Hideki Ishida; Kazunari Tanabe
Journal:  Clin Exp Nephrol       Date:  2016-10-19       Impact factor: 2.801

2.  An inadvertent but explicable error in calculating number needed to treat for reporting survival data.

Authors:  Sudhir K Bowry; Volker Schoder; Christian Apel
Journal:  J Am Soc Nephrol       Date:  2014-04-10       Impact factor: 10.121

Review 3.  Perspectives on the 2 × 2 Matrix: Solving Semantically Distinct Problems Based on a Shared Structure of Binary Contingencies.

Authors:  Hansjörg Neth; Nico Gradwohl; Dirk Streeb; Daniel A Keim; Wolfgang Gaissmaier
Journal:  Front Psychol       Date:  2021-02-09

4.  Patients 80 + have similar medication initiation rates to those aged 50-79 in Ontario FLS.

Authors:  J E M Sale; A Yang; V Elliot-Gibson; R Jain; R Sujic; D Linton; J Weldon; L Frankel; E Bogoch
Journal:  Osteoporos Int       Date:  2021-01-20       Impact factor: 4.507

5.  Closing the gap: A novel metric of change in performance.

Authors:  Richard Katuramu; Jeanna Wallenta; Fred C Semitala; Gideon Amanyire; Leatitia Kampiire; Jennifer Namusobya; Moses R Kamya; Diane Havlir; David V Glidden; Elvin Geng
Journal:  East Afr J Appl Health Monitor Eval       Date:  2019-02

6.  Modeling the disruption of respiratory disease clinical trials by non-pharmaceutical COVID-19 interventions.

Authors:  Claire Couty; Igor Faddeenkov; Natacha Go; Solène Granjeon-Noriot; Simon Arsène; Daniel Šmít; Riad Kahoul; Ben Illigens; Jean-Pierre Boissel; Aude Chevalier; Lorenz Lehr; Christian Pasquali; Alexander Kulesza
Journal:  Nat Commun       Date:  2022-04-13       Impact factor: 17.694

7.  Risk prediction of acute kidney injury in cardiac surgery and prevention using aminophylline.

Authors:  A R Mahaldar; K Sampathkumar; A R Raghuram; S Kumar; M Ramakrishnan; D A C Mahaldar
Journal:  Indian J Nephrol       Date:  2012-05

8.  A reversal coarse-grained analysis with application to an altered functional circuit in depression.

Authors:  Shuixia Guo; Yun Yu; Jie Zhang; Jianfeng Feng
Journal:  Brain Behav       Date:  2013-09-22       Impact factor: 2.708

9.  Predicting future weight status from measurements made in early childhood: a novel longitudinal approach applied to Millennium Cohort Study data.

Authors:  E Mead; A M Batterham; G Atkinson; L J Ells
Journal:  Nutr Diabetes       Date:  2016-03-07       Impact factor: 5.097

Review 10.  Statistical Primer on Biosimilar Clinical Development.

Authors:  Leah Isakov; Bo Jin; Ira Allen Jacobs
Journal:  Am J Ther       Date:  2016 Nov/Dec       Impact factor: 2.688

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