Literature DB >> 8023035

Selection bias in observational and experimental studies.

J H Ellenberg1.   

Abstract

There has been a heightened awareness of the dangers of selection bias over the past two decades. Certainly coverage in statistical and 'statistics for medicine', and epidemiology textbooks have allocated pages to warn investigators and readers of investigations to be aware of its presence. The scientific community has not, however, yet accepted the necessity for critical assessment of the method of sample selection in the planning and execution of studies as a fundamental underpinning of observational and experimental studies. To wit, we are faced with a plethora of research studies receiving funding, being published in peer-reviewed journals and influencing future studies, that may be reporting entirely spurious associations. It is the intent of this paper to present examples of selection bias in a variety of areas which have resulted in misleading or entirely incorrect results. We hope to help make such research scientifically 'politically incorrect' to the degree that the scientific community 'just says no' to such studies, either proposed or reported.

Mesh:

Year:  1994        PMID: 8023035     DOI: 10.1002/sim.4780130518

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  33 in total

1.  Bias.

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Journal:  J Epidemiol Community Health       Date:  2004-08       Impact factor: 3.710

Review 2.  Propensity scores in intensive care and anaesthesiology literature: a systematic review.

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Journal:  Intensive Care Med       Date:  2010-08-06       Impact factor: 17.440

3.  Statistics in brief: the importance of sample size in the planning and interpretation of medical research.

Authors:  David Jean Biau; Solen Kernéis; Raphaël Porcher
Journal:  Clin Orthop Relat Res       Date:  2008-06-20       Impact factor: 4.176

Review 4.  Generalisation and extrapolation.

Authors:  D G Altman; J M Bland
Journal:  BMJ       Date:  1998-08-08

5.  Assessing the effectiveness of health interventions for cost-effectiveness analysis. Panel on Cost-Effectiveness in Health and Medicine.

Authors:  J S Mandelblatt; D G Fryback; M C Weinstein; L B Russell; M R Gold
Journal:  J Gen Intern Med       Date:  1997-09       Impact factor: 5.128

6.  A questionnaire to assess the relevance and credibility of observational studies to inform health care decision making: an ISPOR-AMCP-NPC Good Practice Task Force report.

Authors:  Marc L Berger; Bradley C Martin; Don Husereau; Karen Worley; J Daniel Allen; Winnie Yang; Nicole C Quon; C Daniel Mullins; Kristijan H Kahler; William Crown
Journal:  Value Health       Date:  2014-03       Impact factor: 5.725

7.  Quantifying the bias due to observed individual confounders in causal treatment effect estimates.

Authors:  Layla Parast; Beth Ann Griffin
Journal:  Stat Med       Date:  2020-05-10       Impact factor: 2.373

8.  Determining cystic fibrosis-affected lung microbiology: comparison of spontaneous and serially induced sputum samples by use of terminal restriction fragment length polymorphism profiling.

Authors:  Geraint B Rogers; Stuart Skelton; David J Serisier; Christopher J van der Gast; Kenneth D Bruce
Journal:  J Clin Microbiol       Date:  2009-11-11       Impact factor: 5.948

9.  De-escalation versus continuation of empirical antimicrobial treatment in severe sepsis: a multicenter non-blinded randomized noninferiority trial.

Authors:  Marc Leone; Carole Bechis; Karine Baumstarck; Jean-Yves Lefrant; Jacques Albanèse; Samir Jaber; Alain Lepape; Jean-Michel Constantin; Laurent Papazian; Nicolas Bruder; Bernard Allaouchiche; Karine Bézulier; François Antonini; Julien Textoris; Claude Martin
Journal:  Intensive Care Med       Date:  2014-08-05       Impact factor: 17.440

Review 10.  Aspirin as adjuvant therapy for colorectal cancer--reinterpreting paradigms.

Authors:  Whay Kuang Chia; Raghib Ali; Han Chong Toh
Journal:  Nat Rev Clin Oncol       Date:  2012-08-21       Impact factor: 66.675

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