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Clinical Research Management 512

Clinical Research Management 512. Leslie McIntosh l mcintosh at path.wustl.edu. Part I. Tables. Part II. Hypotheses Revisited Exposure and Outcomes. Notes about Hypotheses. A hypothesis is a specific conjecture (statement) about a property of population.

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Clinical Research Management 512

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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

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