Statistical Analysis In Case Study

The present paper discusses aspects of the statistical analysis of toxicokinetic data by reference to 102 case studies. In toxicokinetic studies, dose dependence of exposure is the most important subject. For this purpose, and because variability in concentration is closely related to the mean value, before beginning the standard procedures of analysis of variance and regression analysis concentration data should be transformed into logarithms. This data transformation also assists assessment of nonlinear kinetics. The paper discusses a special issue in the analysis of experiments for which measurement on each subject has been repeated. A simple procedure is proposed for estimating the daily exposure level in rodent studies when test chemicals have been mixed in diet and exposure level is measured on different animals at each time point. Experimental designs are classified into three types, and these are illustrated by data from representative case studies. Conclusions from 102 case studies are summarized, with attention to how often sex differences, change in exposure level during repeated dosing, and how often evidence is strong for nonlinear kinetics. Various key points of study design are discussed.

Design of Experiments > Case Studies

What are Case Studies?




Case studies are in-depth studies of a phenomenon, like a person, group, or situation. The phenomenon is studied in detail, cases are analyzed and solutions or interpretations are presented. It can provide a deeper understanding of a complex topic or assist a person in gaining experience about a certain historical situation. Although case studies are used across a wide variety of disciplines, they are more frequently found in the social sciences.

Case studies are a type of qualitative research. This method does not involve statistical hypothesis testing. The method has been criticized as being unreliable, too general, and open to bias. To avoid some of these problems, studies should be carefully planned and implemented. The University of Texas suggest the following six steps for case studies to ensure the best possible outcome:

  1. Determine the research question and carefully define it. The research question for case studies generally starts with a “How” or “Why.”
  2. Choose the cases and state how data is to be gathered and which techniques for analysis you’ll be using. Well designed studies consider all available options for cases and for ways to analyze those cases. Multiple sources and data analysis methods are recommended.
  3. Prepare to collect the data. Consider how you will deal with large sets of data in order to avoid becoming overwhelmed once the study is underway. You should formulate good questions and anticipate how you will interpret answers. Multiple collection methods will strengthen the study. See: Data Collection Methods.
  4. Collect the data in the field (or, less frequently, in the lab). Collect and organize the data, keep good field notes and maintain an organized database.
  5. Analyze the data.
  6. Prepare your report.

Text books are including more real-life studies to veer away from the “clean” data sets that are found in traditional books. These data sets do little to prepare students for applying statistical concepts to their ultimate careers in industry or the social sciences. You can find many examples of several real-life statistics case studies on the UCLA website.

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If you prefer an online interactive environment to learn R and statistics, this free R Tutorial by Datacamp is a great way to get started. If you're are somewhat comfortable with R and are interested in going deeper into Statistics, try this Statistics with R track.

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