Routinely collected data and comparative effectiveness evidence: promises and limitations

Routinely collected data (RCD) are increasingly used for biomedical research. Extensive resources have been invested in this field: they include the set-up of disease registries and clinical databases at regional, national or international levels; the promotion of the use of electronic health

[1]  J. Gurwitz,et al.  Exclusion of Older Adults and Women from Recent Trials of Acute Coronary Syndromes , 2011, Journal of the American Geriatrics Society.

[2]  F. Rosner,et al.  The ethics of randomized clinical trials. , 1987, The American journal of medicine.

[3]  David Moher,et al.  The REporting of Studies Conducted Using Observational Routinely-Collected Health Data (RECORD) Statement: Methods for Arriving at Consensus and Developing Reporting Guidelines , 2015, PloS one.

[4]  P. Rothwell.  Subgroup analysis in randomised controlled trials: importance, indications, and interpretation , 2005, The Lancet.

[5]  J. Ioannidis.  The importance of potential studies that have not existed and registration of observational data sets. , 2012, JAMA.

[6]  Harlan M Krumholz,et al.  Representation of the elderly, women, and minorities in heart failure clinical trials. , 2002, Archives of internal medicine.

[7]  Kristian Thorlund,et al.  Demystifying trial networks and network meta-analysis , 2013, BMJ.

[8]  J. Ioannidis,et al.  Homophily and co-occurrence patterns shape randomized trials agendas: illustration in antifungal agents. , 2011, Journal of clinical epidemiology.

[9]  Clifford R. Mynatt,et al.  Confirmation Bias in a Simulated Research Environment: An Experimental Study of Scientific Inference , 1977 .

[10]  John P A Ioannidis,et al.  Informed Consent, Big Data, and the Oxymoron of Research That Is Not Research , 2013, The American journal of bioethics : AJOB.

[11]  S. Pocock,et al.  Strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies , 2007, BMJ : British Medical Journal.

[12]  V. Prasad,et al.  Prespecified falsification end points: can they validate true observational associations? , 2013, JAMA.

[13]  J. Ioannidis,et al.  Nationwide Population Science: Lessons From the Taiwan National Health Insurance Research Database. , 2015, JAMA internal medicine.

[14]  Timothy L Lash,et al.  Commentary: Should Preregistration of Epidemiologic Study Protocols Become Compulsory? Reflections and a Counterproposal , 2012, Epidemiology.

[15]  S. Geller,et al.  Adherence to federal guidelines for reporting of sex and race/ethnicity in clinical trials. , 2006, Journal of women's health.

[16]  R. Nickerson.  Confirmation Bias: A Ubiquitous Phenomenon in Many Guises , 1998 .

[17]  S. Stanley Young,et al.  Deming, data and observational studies , 2011 .

[18]  J. Ioannidis,et al.  Industry sponsorship and selection of comparators in randomized clinical trials , 2010, European journal of clinical investigation.

[19]  Alan F. Karr,et al.  Deming, data and observational studies: A process out of control and needing fixing , 2013 .

[20]  J. Ioannidis,et al.  Risk factors and interventions with statistically significant tiny effects. , 2011, International journal of epidemiology.

[21]  I. Hozo,et al.  At what degree of belief in a research hypothesis is a trial in humans justified? , 2002, Journal of evaluation in clinical practice.

[22]  Eric L Eisenstein,et al.  Reducing the costs of phase III cardiovascular clinical trials. , 2005, American heart journal.

[23]  L. Trinquart,et al.  Underrepresentation of Elderly People in Randomised Controlled Trials. The Example of Trials of 4 Widely Prescribed Drugs , 2012, PloS one.

[24]  J. Avorn,et al.  A review of uses of health care utilization databases for epidemiologic research on therapeutics. , 2005, Journal of clinical epidemiology.

[25]  David Moher,et al.  Reducing waste from incomplete or unusable reports of biomedical research , 2014, The Lancet.

[26]  Harlan M Krumholz,et al.  Increasing value and reducing waste: addressing inaccessible research , 2014, The Lancet.

[27]  Ian Scott,et al.  Data Linkage: A powerful research tool with potential problems , 2010, BMC health services research.

[28]  A. Vickers.  Clinical trials in crisis: Four simple methodologic fixes , 2014, Clinical trials.

[29]  J. Ioannidis,et al.  Comparison of evidence of treatment effects in randomized and nonrandomized studies. , 2001, JAMA.

[30]  J. Robins,et al.  Instruments for Causal Inference: An Epidemiologist's Dream? , 2006, Epidemiology.

[31]  J. Ioannidis,et al.  Comparative effectiveness of exercise and drug interventions on mortality outcomes: metaepidemiological study , 2015, British Journal of Sports Medicine.

[32]  Iain Chalmers,et al.  How to increase value and reduce waste when research priorities are set , 2014, The Lancet.

[33]  W. Richardson,et al.  The well-built clinical question: a key to evidence-based decisions. , 1995, ACP journal club.

[34]  B. Thiers,et al.  Eligibility Criteria of Randomized Controlled Trials Published in High-Impact General Medical Journals: A Systematic Sampling Review , 2008 .

[35]  K A McKibbon,et al.  Beyond ACP Journal Club: how to harness MEDLINE for prognosis problems. , 1995, ACP journal club.