Literature DB >> 26680297

A Note on the Noncollapsibility of Rate Differences and Rate Ratios.

Arvid Sjölander1, Elisabeth Dahlqwist, Johan Zetterqvist.   

Abstract

It is well known that the odds ratio is noncollapsible, in the sense that conditioning on a covariate that is related to the outcome typically changes the size of the odds ratio, even if this covariate is unrelated to the exposure. The risk difference and risk ratio do not have this peculiar property; we say that the risk difference and risk ratio are collapsible. However, noncollapsibility is not unique for the odds ratio; the rate difference and rate ratio are generally noncollapsible as well. This may seem paradoxical, since the rate can be viewed as a risk per unit time, and thus one would naively suspect that the rate difference/ratio should inherit collapsibility from the risk difference/ratio. Adding to the confusion, it was recently shown that the exposure coefficient in the Aalen additive hazards model is collapsible. This may seem to contradict the fact that the rate difference is generally noncollapsible, since the exposure coefficient in the Aalen additive hazards model is a rate difference. In this article, we use graphical arguments to explain why the rate difference/ratio does not inherit collapsibility from the risk difference/ratio. We also explain when and why the exposure coefficient in the Aalen additive hazards model is collapsible.

Mesh:

Year:  2016        PMID: 26680297     DOI: 10.1097/EDE.0000000000000433

Source DB:  PubMed          Journal:  Epidemiology        ISSN: 1044-3983            Impact factor:   4.822


  8 in total

1.  Oncology Drug Effectiveness from Electronic Health Record Data Calibrated Against RCT Evidence: The PARSIFAL Trial Emulation.

Authors:  David Merola; Jessica Young; Deborah Schrag; Kueiyu Joshua Lin; Nicholas Robert; Sebastian Schneeweiss
Journal:  Clin Epidemiol       Date:  2022-10-10       Impact factor: 5.814

2.  The NUDGE trial pragmatic trial to enhance cardiovascular medication adherence: study protocol for a randomized controlled trial.

Authors:  Russell E Glasgow; Christopher E Knoepke; David Magid; Gary K Grunwald; Thomas J Glorioso; Joy Waughtal; Joel C Marrs; Sheana Bull; P Michael Ho
Journal:  Trials       Date:  2021-08-11       Impact factor: 2.728

3.  Genetically Predicted Circulating C-Reactive Protein Concentration and Colorectal Cancer Survival: A Mendelian Randomization Consortium Study.

Authors:  Xinwei Hua; James Y Dai; Sara Lindström; Tabitha A Harrison; Yi Lin; Steven R Alberts; Elizabeth Alwers; Sonja I Berndt; Hermann Brenner; Daniel D Buchanan; Peter T Campbell; Graham Casey; Jenny Chang-Claude; Steven Gallinger; Graham G Giles; Richard M Goldberg; Marc J Gunter; Michael Hoffmeister; Mark A Jenkins; Amit D Joshi; Wenjie Ma; Roger L Milne; Neil Murphy; Rish K Pai; Lori C Sakoda; Robert E Schoen; Qian Shi; Martha L Slattery; Mingyang Song; Emily White; Loic Le Marchand; Andrew T Chan; Ulrike Peters; Polly A Newcomb
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2021-05-10       Impact factor: 4.090

4.  A novel approach for identifying and addressing case-mix heterogeneity in individual participant data meta-analysis.

Authors:  Tat-Thang Vo; Raphael Porcher; Anna Chaimani; Stijn Vansteelandt
Journal:  Res Synth Methods       Date:  2019-12-02       Impact factor: 5.273

5.  Mediation analysis with a time-to-event outcome: a review of use and reporting in healthcare research.

Authors:  Lauren Lapointe-Shaw; Zachary Bouck; Nicholas A Howell; Theis Lange; Ani Orchanian-Cheff; Peter C Austin; Noah M Ivers; Donald A Redelmeier; Chaim M Bell
Journal:  BMC Med Res Methodol       Date:  2018-10-29       Impact factor: 4.615

6.  Effects of Omitting Non-confounding Predictors From General Relative-Risk Models for Binary Outcomes.

Authors:  John Cologne; Kyoji Furukawa; Eric J Grant; Robert D Abbott
Journal:  J Epidemiol       Date:  2018-08-11       Impact factor: 3.211

7.  Estimating dose-response for time to remission with instrumental variable adjustment: the obscuring effects of drug titration in Genome Based Therapeutic Drugs for Depression Trial (GENDEP): clinical trial data.

Authors:  Jennifer Hellier; Richard Emsley; Andrew Pickles
Journal:  Trials       Date:  2020-01-03       Impact factor: 2.279

8.  Making apples from oranges: Comparing noncollapsible effect estimators and their standard errors after adjustment for different covariate sets.

Authors:  Rhian Daniel; Jingjing Zhang; Daniel Farewell
Journal:  Biom J       Date:  2020-12-14       Impact factor: 1.715

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.