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Science is often not objective.1 The choice of research questions, the methods to collect and analyze data, and the interpretation of results all reflect the perspective of the investigator.2 Try as they may to be objective and impartial, investigators' intellectual and/or emotional investment in their own ideas and their personal interest in academic success and advancement may further compromise scientific objectivity. Investigators often overemphasize the importance of their findings and the quality of their work. Scrutiny of the work of the authors of this chapter will reveal we are not immune to these lapses.


In addition, conflicts of interest arise when for-profit organizations, such as device, biotechnology, and pharmaceutical companies, provide funds for research, consulting, and attending scientific meetings. In recent years, there has been a large increase in the number of trials for which authors declare industry affiliation.3 Investigators accepting industry funds may have conflicts of interest. Even more problematic, they may cede their right to directly supervise data collection, participate in or supervise data analysis, and write the research reports to which their names are attached.4-6 Finally, clinical studies funded by for-profit companies are more likely to report results and conclusions that favor the intervention being tested than are trials funded by nonprofit bodies.7-9


Extensive publicity highlighting these problems has caught the attention of many clinicians, who are therefore well aware of their vulnerability to biased and potentially misleading presentations of randomized clinical trial (RCT) results. This book describes, in some detail, guides to help recognize methodologic weaknesses that may introduce bias. These criteria, however, do not protect readers against misleading interpretations of apparently methodologically sound studies. Indeed, all of the studies we use as examples in this chapter satisfy minimal risk of bias criteria, and most are exceptionally strong. In this chapter, we go beyond issues of risk of bias to present a set of Users' Guides to address biased presentation and interpretation of data to aid clinicians in optimally applying research findings (Box 13.3-1). We illustrate these guides with real-world examples, not to adversely criticize investigators, but to raise awareness of the dangers that the medical literature currently presents to unwary clinicians.

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BOX 13.3-1

Users' Guides to Avoid Being Misled by Biased Presentation and Interpretation of Data

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