Interpreting Clinical Trial Data

Effective interpretation of clinical trial data allows for the integration of high-quality clinical research into clinical practice. Evidence from clinical trials directly impacts clinical decision making, which ultimately guides patient management (Page, Int J Sports Phys Ther 9: 726, 2014). The purpose of this chapter is to provide an overview of fundamental aspects of clinical trials, such as study designs, analyses, and methods of handling missing data along with potential sources of bias and uncertainty. Subject withdrawal can make statistical analysis difficult as studies are aimed to determine the impact of an intervention from beginning to end. The different approaches to account for missing data are presented: last observation carried forward (LOCF), nonresponder imputation (NRI), as-observed data analysis, and anytime analysis (Langley and Reich, Br J Dermatol 169:1198–1206, 2013). Identifying sources of error and understanding limitations of these methods helps clinicians evaluate the validity of clinical data, ultimately enhancing clinical decision making and patient care.