Literature DB >> 14758869

The limitations of using hospital controls in cancer etiology--one more example for Berkson's bias.

Siegal Sadetzki1, David Bensal, Ilya Novikov, Baruch Modan.   

Abstract

The aim of this report was to present an example in which Berkson's bias, most probably, affected the results of a study by overriding the influence of a well-established risk factor (smoking) in the etiology of bladder cancer. The results of a study of 140 male patients with bladder cancer and 280 matched hospital controls confirmed the etiological role of industrial occupation in bladder cancer but failed to confirm the role of smoking. We reanalyzed the proportion of chronic related morbidity as well as the rate of smoking in patients with lung disease in cases and controls. A similar distribution of some chronic diseases known to be highly associated with smoking was found among cases and controls. Highest smoking rates (91%) were found among patients with bladder cancer who also reported a concomitant lung disease, and the lowest rate (67%) was noted among controls without lung disease (p = 0.009). Using the prevalence of smoking in the general Israeli male population (50%), significant odds ratio for bladder cancer among ever smokers compared to never smokers was observed. Our conclusion is that a possibility of Berkson's bias should be considered whenever hospital controls are used. Information on diseases related to the risk factor under consideration and on the prevalence of the risk factor in the general population, may demonstrate the existence of such a bias.

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Year:  2003        PMID: 14758869     DOI: 10.1023/b:ejep.0000006634.49205.c5

Source DB:  PubMed          Journal:  Eur J Epidemiol        ISSN: 0393-2990            Impact factor:   8.082


  7 in total

1.  Computing disease incidence, prevalence and comorbidity from electronic medical records.

Authors:  Steven C Bagley; Russ B Altman
Journal:  J Biomed Inform       Date:  2016-08-04       Impact factor: 6.317

2.  Commentary: A structural approach to Berkson's fallacy and a guide to a history of opinions about it.

Authors:  Jaapjan D Snoep; Alfredo Morabia; Sonia Hernández-Díaz; Miguel A Hernán; Jan P Vandenbroucke
Journal:  Int J Epidemiol       Date:  2014-02-28       Impact factor: 7.196

3.  Glutathione S-transferase M1, T1, and P1 polymorphisms and risk of glioma: a meta-analysis.

Authors:  Zuoxu Fan; Yaoyao Wu; Jian Shen; Renya Zhan
Journal:  Mol Biol Rep       Date:  2012-10-20       Impact factor: 2.316

4.  Is opium a real risk factor for esophageal cancer or just a methodological artifact? Hospital and neighborhood controls in case-control studies.

Authors:  Ramin Shakeri; Farin Kamangar; Dariush Nasrollahzadeh; Mehdi Nouraie; Hooman Khademi; Arash Etemadi; Farhad Islami; Hajiamin Marjani; Saman Fahimi; Alireza Sepehr; Atieh Rahmati; Christian C Abnet; Sanford M Dawsey; Paul Brennan; Paolo Boffetta; Reza Malekzadeh; Reza Majdzadeh
Journal:  PLoS One       Date:  2012-03-01       Impact factor: 3.240

5.  A critical appraisal of epidemiological studies comes from basic knowledge: a reader's guide to assess potential for biases.

Authors:  Stefania Boccia; Giuseppe La Torre; Roberto Persiani; Domenico D'Ugo; Cornelia M van Duijn; Gualtiero Ricciardi
Journal:  World J Emerg Surg       Date:  2007-03-15       Impact factor: 5.469

6.  Berkson's bias in biobank sampling in a specialised mental health care setting: a comparative cross-sectional study.

Authors:  Vincent Laliberté; Charles-Edouard Giguère; Stéphane Potvin; Alain Lesage
Journal:  BMJ Open       Date:  2020-07-23       Impact factor: 2.692

7.  Role of aldehyde dehydrogenases, alcohol dehydrogenase 1B genotype, alcohol consumption, and their combination in breast cancer in East-Asian women.

Authors:  Boyoung Park; Ji-Hyun Kim; Eun Sook Lee; So-Youn Jung; See Youn Lee; Han-Sung Kang; Eun-Gyeong Lee; Jai Hong Han
Journal:  Sci Rep       Date:  2020-04-16       Impact factor: 4.379

  7 in total

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