Literature DB >> 7203778

Control of confounding in the assessment of medical technology.

S Greenland, R Neutra.   

Abstract

Separation of the effects of extraneous variables from the effects of a factor under study (often termed control of confounding) is one of the key prerequisites for validly estimating the magnitude of the study factor's effects. Because of the phenomenon of confounding by indication, confounding of effects of different factors is a common problem in the assessment of medical technology. We give several examples illustrating that the decision of whether a recorded variable is a confounder in a data-set must be decided on the basis of subject-matter knowledge and clinical judgement. There is no alternative to use of such judgement; statistical selection procedures based on significant tests, such as stepwise regression, can be particularly misleading.

Mesh:

Year:  1980        PMID: 7203778     DOI: 10.1093/ije/9.4.361

Source DB:  PubMed          Journal:  Int J Epidemiol        ISSN: 0300-5771            Impact factor:   7.196


  38 in total

Review 1.  Confounding by indication in non-experimental evaluation of vaccine effectiveness: the example of prevention of influenza complications.

Authors:  E Hak; Th J M Verheij; D E Grobbee; K L Nichol; A W Hoes
Journal:  J Epidemiol Community Health       Date:  2002-12       Impact factor: 3.710

2.  The prevalence of genital HPV and factors associated with oncogenic HPV among men having sex with men and men having sex with women and men: the HIM study.

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Journal:  Sex Transm Dis       Date:  2011-10       Impact factor: 2.830

Review 3.  Bias in occupational epidemiology studies.

Authors:  Neil Pearce; Harvey Checkoway; David Kriebel
Journal:  Occup Environ Med       Date:  2006-10-19       Impact factor: 4.402

4.  Statistical pitfalls in medical research.

Authors:  V B Nyirongo; M M Mukaka; L V Kalilani-Phiri
Journal:  Malawi Med J       Date:  2008-03       Impact factor: 0.875

5.  John Henryism and Perceived Health among Hemodialysis Patients in a Multiracial Brazilian Population: the PROHEMO.

Authors:  Gildete Barreto Lopes; Sherman A James; Marcelo Barreto Lopes; Carolina Cartaxo Penalva; Camila Tavares Joau E Silva; Cacia Mendes Matos; Márcia Tereza Silva Martins; Antonio Alberto Lopes
Journal:  Ethn Dis       Date:  2018-10-18       Impact factor: 1.847

6.  Matching Weights to Simultaneously Compare Three Treatment Groups: Comparison to Three-way Matching.

Authors:  Kazuki Yoshida; Sonia Hernández-Díaz; Daniel H Solomon; John W Jackson; Joshua J Gagne; Robert J Glynn; Jessica M Franklin
Journal:  Epidemiology       Date:  2017-05       Impact factor: 4.822

7.  Marginal structural models for sufficient cause interactions.

Authors:  Tyler J Vanderweele; Stijn Vansteelandt; James M Robins
Journal:  Am J Epidemiol       Date:  2010-01-11       Impact factor: 4.897

8.  Episiotomy: Differences in practice between family physicians and obstetricians.

Authors:  J Ruderman; J C Carroli; A J Reid; M A Murray
Journal:  Can Fam Physician       Date:  1992-11       Impact factor: 3.275

9.  Prevalence of and Risk Factors for Anal Human Papillomavirus Infection in a Sample of Young, Predominantly Black Men Who Have Sex With Men, Houston, Texas.

Authors:  Alan G Nyitray; Kayo Fujimoto; Jing Zhao; Anna R Giuliano; John A Schneider; Lu-Yu Hwang
Journal:  J Infect Dis       Date:  2018-02-14       Impact factor: 5.226

10.  Signalling paediatric side effects using an ensemble of simple study designs.

Authors:  Jenna M Reps; Jonathan M Garibaldi; Uwe Aickelin; Daniele Soria; Jack E Gibson; Richard B Hubbard
Journal:  Drug Saf       Date:  2014-03       Impact factor: 5.606

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