P.Mean >> Category >> Extrapolation of research findings (created 2007-06-18).

These pages discuss some of the issues that you should consider when evaluating whether it is appropriate to extrapolate research finding to a different group of patients or to a different practice. Articles are arranged by date with the most recent entries at the top. You can find the theme and closely related categories and other resources at the bottom of this page.

2008

[[There is no material yet from my new site.]]

Outside resources:

  1. Journal article: Jose Sacristan. Exploratory trials, confirmatory observations: A new reasoning model in the era of patient-centered medicine BMC Medical Research Methodology. 2011;11(1):57. Abstract: "BACKGROUND: The prevailing view in therapeutic clinical research today is that observational studies are useful for generating new hypotheses and that controlled experiments (i.e., randomized clinical trials, RCTs) are the most appropriate method for assessing and confirming the efficacy of interventions. DISCUSSION: The current trend towards patient-centered medicine calls for alternative ways of reasoning, and in particular for a shift towards hypothetico-deductive logic, in which theory is adjusted in light of individual facts. A new model of this kind should change our approach to drug research and development, and regulation. The assessment of new therapeutic agents would be viewed as a continuous process, and regulatory approval would no longer be regarded as the final step in the testing of a hypothesis, but rather, as the hypothesis-generating step.The main role of RCTs in this patient-centered research paradigm would be to generate hypotheses, while observations would serve primarily to test their validity for different types of patients. Under hypothetico-deductive logic, RCTs are considered "exploratory" and observations, "confirmatory". SUMMARY: In this era of tailored therapeutics, the answers to therapeutic questions cannot come exclusively from methods that rely on data aggregation, the analysis of similarities, controlled experiments, and a search for the best outcome for the average patient; they must also come from methods based on data disaggregation, analysis of subgroups and individuals, an integration of research and clinical practice, systematic observations, and a search for the best outcome for the individual patient. We must look not only to evidence-based medicine, but also to medicine-based evidence, in seeking the knowledge that we need." [Accessed on May 24, 2011]. http://www.biomedcentral.com/1471-2288/11/57.
  2. Mary S Fewtrell, Kathy Kennedy, Atul Singhal, et al. How much loss to follow-up is acceptable in long-term randomised trials and prospective studies?. Archives of Disease in Childhood. 2008;93(6):458 -461. Description: This article reviews current literature recommendations on how low a drop-out should be in order to be acceptable. The general consensus is that 5% or less is good and that 20% or higher is bad (though some authors will say that 50% or more is bad). The authors point out that the statistical consequences of drop-outs vary from study to study and that rigid adherence to any fixed cut-off is inappropriate. [Accessed July 17, 2010]. Available at: http://adc.bmj.com/content/93/6/458.extract.

Creative Commons License All of the material above this paragraph is licensed under a Creative Commons Attribution 3.0 United States License. This page was written by Steve Simon and was last modified on 2010-07-16. The material below this paragraph links to my old website, StATS. Although I wrote all of the material listed below, my ex-employer, Children's Mercy Hospital, has claimed copyright ownership of this material. The brief excerpts shown here are included under the fair use provisions of U.S. Copyright laws.

2008

  1. Stats: Difficulties in generalizing research (February 15, 2006). I found this information thanks to an email in the Evidence-Based Health email discussion group. Someone asked if there was any empirical evidence that the setting of a study (e.g., primary versus secondary care) could influence the results of the research. Intuitively, you would suspect that this would be the case, because the types of patients who show up at a primary care clinic are quite different than those who show up at a secondary or tertiary care center.

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Creative Commons License This work is licensed under a Creative Commons Attribution 3.0 United States License. This page was written by Steve Simon and was last modified on 2010-07-16.