title: Effect identification in comparative effectiveness research.
abstract: The widespread adoption of electronic medical records means there are now vast data resources available for comparative effectiveness research (CER). In concert with conventional randomized controlled trials, CER holds great promise for advancing our understanding of how different therapeutic treatments yield different health outcomes in different settings and with different populations. But in a research culture fixated on estimating correlations and p-values, the threat of misinterpretation of results and improper CER inferences is troubling. Accordingly, this paper aims to shore up the inferential foundations of CER by introducing the fundamentals of effect identification, which is the process of identifying or teasing out empirically defensible causal effects from competing explanations. Three primary requirements of effect identification-positivity, exchangeability, and consistency- are explained and simple exampled are given. The take home message is that so-called big data from medical records may not yield better or more useful results. Advances will come only when the right question is addressed with the appropriate data and methods.
authors.affiliation: University of Minnesota.
authors.forename: J Michael
authors.initials: JM
authors.lastname: Oakes
references.citation: JAMA. 2011 Jan 26;305(4):400-1
references.pmid: 21266687
references.citation: Health Technol Assess. 1997;1(14):i-vi, 1-149
references.pmid: 9483162
references.citation: Int J Epidemiol. 2002 Apr;31(2):422-9
references.pmid: 11980807
references.citation: Int J Epidemiol. 1986 Sep;15(3):413-9
references.pmid: 3771081
references.citation: Epidemiology. 2011 May;22(3):298-301
references.pmid: 21464649
references.citation: Epidemiology. 2008 Nov;19(6):766-79
references.pmid: 18854702
references.citation: Epidemiology. 2001 May;12(3):313-20
references.pmid: 11338312
references.citation: Med Care. 2012 Jul;50 Suppl:S11-8
references.pmid: 22692252
references.citation: Epidemiology. 2000 Sep;11(5):550-60
references.pmid: 10955408
references.citation: Stat Methods Med Res. 2012 Feb;21(1):55-75
references.pmid: 21068053
references.citation: Epidemiology. 1991 Sep;2(5):367-9
references.pmid: 1742387
references.citation: Eval Rev. 2010 Aug;34(4):334-61
references.pmid: 20647500
references.citation: J Epidemiol Community Health. 2004 Apr;58(4):265-71
references.pmid: 15026432
references.citation: Stat Med. 1992 Sep 30;11(13):1747-58
references.pmid: 1485057
references.citation: Health Serv Res. 2011 Apr;46(2):397-420
references.pmid: 21105867
references.citation: Control Clin Trials. 1990 Apr;11(2):77-9
references.pmid: 2161313
references.citation: Med Care. 2012 Jul;50 Suppl:ii
references.pmid: 22692250
references.citation: Am J Epidemiol. 2010 Mar 15;171(6):664-73
references.pmid: 20139129
references.citation: Epidemiology. 2009 Nov;20(6):880-3
references.pmid: 19829187
references.citation: Med Care. 2010 Jun;48(6 Suppl):S114-20
references.pmid: 20473199
references.citation: Health Aff (Millwood). 2001 Sep-Oct;20(5):11-29
references.pmid: 11558696
references.citation: Soc Sci Med. 2004 May;58(10):1929-52
references.pmid: 15020009
references.citation: Clin Trials. 2011 Apr;8(2):183-95
references.pmid: 21478329
references.citation: BMJ. 2000 Jan 1;320(7226):43-6
references.pmid: 10617532
references.citation: Demography. 2005 Feb;42(1):91-108
references.pmid: 15782897