Rigor Alone Does Not Justify Causal Inference

Background: Differing opinions exist on whether associations obtained in observational studies can be reliable indicators of a causal effect if the observational study is sufficiently well controlled and executed. Materials and methods: To test this, we conducted two animal observational studies that were rigorously controlled and executed beyond what is achieved in studies of humans. In study 1, we randomized 332 genetically identical C57BL/6J mice into three diet groups with differing food energy allotments

[1]  A. Grey,et al.  Reporting of Limitations of Observational Research. , 2015, JAMA internal medicine.

[2]  D. Francis,et al.  Trials are best, ignore the rest: safety and efficacy of digoxin , 2015, BMJ : British Medical Journal.

[3]  R. Golub,et al.  Researchers, readers, and reporting guidelines: writing between the lines. , 2015, JAMA.

[4]  April Bleske-Rechek,et al.  Causal Inference from Descriptions of Experimental and Non-Experimental Research: Public Understanding of Correlation-Versus-Causation , 2015, The Journal of general psychology.

[5]  P. Miller,et al.  Low-calorie sweeteners and body weight and composition: a meta-analysis of randomized controlled trials and prospective cohort studies123 , 2014, The American journal of clinical nutrition.

[6]  D. Madigan,et al.  A Systematic Statistical Approach to Evaluating Evidence from Observational Studies , 2014 .

[7]  Sharon-Lise Normand,et al.  Prospective observational studies to assess comparative effectiveness: the ISPOR good research practices task force report. , 2012, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[8]  Su Golder,et al.  Meta-analyses of Adverse Effects Data Derived from Randomised Controlled Trials as Compared to Observational Studies: Methodological Overview , 2011, PLoS medicine.

[9]  D. Allison,et al.  Use of Causal Language in Observational Studies of Obesity and Nutrition , 2010, Obesity Facts.

[10]  M. Hernán,et al.  Does obesity shorten life? The importance of well-defined interventions to answer causal questions , 2008, International Journal of Obesity.

[11]  Aris Spanos,et al.  Revisiting the omitted variables argument: Substantive vs. statistical adequacy , 2006 .

[12]  Jan P Vandenbroucke,et al.  When are observational studies as credible as randomised trials? , 2004, The Lancet.

[13]  A. Hartz,et al.  A comparison of observational studies and randomized, controlled trials. , 2000, The New England journal of medicine.

[14]  J. Concato,et al.  Randomized, controlled trials, observational studies, and the hierarchy of research designs. , 2000, The New England journal of medicine.

[15]  B. Armstrong.  Effect of measurement error on epidemiological studies of environmental and occupational exposures. , 1998, Occupational and environmental medicine.

[16]  Donald Rubin,et al.  Estimating Causal Effects from Large Data Sets Using Propensity Scores , 1997, Annals of Internal Medicine.

[17]  D B Rubin,et al.  Practical implications of modes of statistical inference for causal effects and the critical role of the assignment mechanism. , 1991, Biometrics.

[18]  A. B. Hill.  The Environment and Disease: Association or Causation? , 1965, Proceedings of the Royal Society of Medicine.

[19]  L. Bero,et al.  Cochrane Database of Systematic Reviews Healthcare outcomes assessed with observational study designs compared with those assessed in randomized trials (Review) , 2017 .

[20]  P. Kirchhof,et al.  University of Birmingham Safety and efficacy of digoxin : systematic review and meta-analysis of observational and controlled trial data , 2015 .

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

[22]  T. Trikalinos,et al.  Concordance of randomized and nonrandomized studies was unrelated to translational patterns of two nutrient-disease associations. , 2012, Journal of clinical epidemiology.

[23]  H. Becher,et al.  The concept of residual confounding in regression models and some applications. , 1992, Statistics in medicine.