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Understanding Hypotheses and Statistical Power in Clinical Research Management

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morna
21 diapositives · 359 Views · Jul 19, 2014

This document explores the critical aspects of formulating and testing hypotheses in clinical research. It discusses the significance of null and alternative hypotheses, emphasizing the need for specificity and falsifiability. It also addresses the difference between correlation and causation, using examples to illustrate common misconceptions in research conclusions. Additionally, the concept of statistical power is examined, highlighting its importance in determining the likelihood of successfully rejecting a false null hypothesis. This resource serves as a guide for researchers aiming to enhance their study design.

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  1. Clinical Research Management 512 Leslie McIntosh lmcintosh at path.wustl.edu

  2. Part I Tables

  3. Part II Hypotheses Revisited Exposure and Outcomes

  4. Notes about Hypotheses • A hypothesis is a specific conjecture (statement) about a property of population. • There is a null hypothesis and an alternative (or research) hypothesis. • Researchers often expect that evidence supports the alternative hypothesis.

  5. Hypotheses: Points to Remember • A hypothesis should be specific enough to be falsifiable • A hypothesis is a conjecture about a population (parameter), not about a sample (statistic). • A valid hypothesis is not based on the sample to be used to test the hypothesis. 2004 by Jeeshim and KUCC625

  6. Error Types H0 = Null Hypothesis

  7. Primary Interests • Exposures – what affected the person intentionally (intervention) or not • Outcomes – what happened to the person • Clinical measures • Non-clinical measures

  8. Activity Exposure Outcome

  9. Erroneous Conclusions Correlation is not equal to causation; it is only a requirement for it.

  10. Erroneous Conclusions • Young children who sleep with the light on are much more likely to develop myopia in later life. • Published from U of Pennsylvania Medical Center in the May 13, 1999 issue of Nature, the study received much coverage at the time in the popular press. • A later study at The Ohio State University did not find a link between infants sleeping with the light on and development of myopia. • It did find a strong link between parental myopia and the development of child myopia, also noting that myopic parents were more likely to leave a light on in their children's bedroom

  11. Erroneous Conclusions • Correlation does not prove causation

  12. Part III Power

  13. Definition of Power • The power of a statistical test is the probability that it will correctly lead to the rejection of a false null hypothesis (Greene 2000). • The statistical power is the ability of a test to detect an effect, if the effect actually exists (High 2000). • Statistical power is the probability that it will result in the conclusion that the phenomenon exists (Cohen 1988) .

  14. Analogy to Understand Power • You ask your child to find a tool in the basement. The child returns saying: “I can’t find it.” • What is the probability the tool is in the basement? • If the tool is really in the basement, what is the chance your child found it? Hartung, 2005

  15. Concerns of Power Statistics Analogy • Sample Size • Effect Size • Variability (Scatter) • Time in basement • Type of tool • Cleanliness of basement