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Deborah H. Glueck, Sarah M. Kreidler, Brandy M. Ringham, Keith E. Muller

Selecting a Valid Sample Size for Longitudinal and Multilevel Studies in Cancer Research: Software and Methods . Deborah H. Glueck, Sarah M. Kreidler, Brandy M. Ringham, Keith E. Muller. Outline. Determining sample size and power for complex designs

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Deborah H. Glueck, Sarah M. Kreidler, Brandy M. Ringham, Keith E. Muller

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  1. Selecting a Valid Sample Size for Longitudinal and Multilevel Studies in Cancer Research: Software and Methods Deborah H. Glueck, Sarah M. Kreidler, Brandy M. Ringham, Keith E. Muller

  2. Outline • Determining sample size and power for complex designs • Calculating power with our free, web-based software • Writing the grant • Novel results for missing data • Questions

  3. Outline • Determining sample size and power for complex designs • Calculating power with our free, web-based software • Writing the grant • Novel results for missing data • Questions

  4. Ethics of Sample Size • If the sample size is too small, the study may be inconclusive and waste resources • If the sample size is too large, then the study may expose too many participants to possible harms due to research

  5. Previous Study on Sensory Focus to Alleviate Pain • Participants categorized into four coping styles • Randomized to one of two treatment arms: sensory focus standard of care • Measured experienced pain after root canal Perceived Control 1 2 High High Desired Control 3 4 Low Low (Logan, Baron, Kohout, 1995)

  6. Memory of Pain TrialStudy Design

  7. Memory of Pain TrialResearch Question

  8. Memory of Pain TrialStudy Population • Recruit participants who have a high desire/low felt coping style • 30 patients / week • 40% consent rate for previous studies

  9. How do we calculate an accurate sample size?

  10. Inputs for Power Analysis • Type I error rate: • Desired power: • Loss to follow-up:

  11. Inputs for Power Analysis • Type I error rate: • Desired power: • Loss to follow-up:

  12. Inputs for Power Analysis • Type I error rate: • Desired power: • Loss to follow-up:

  13. Inputs for Power Analysis • Type I error rate: • Desired power: • Loss to follow-up:

  14. Outline • Determining sample size and power for complex designs • Calculating power with our free, web-based software • Writing the grant • Novel results for missing data • Questions

  15. GLIMMPSE GLIMMPSE is a user-friendly online tool for calculating power and sample size for multilevel and longitudinal studies. http://glimmpse.samplesizeshop.org/

  16. Salient Software Features • Free • Requires no programming expertise • Allows saving study designs for later use • Also available on smartphones • Coming soon on iPad

  17. Create a Study Design

  18. Create a Study Design Select Guided Study Design

  19. Main Application Screen Tools and documentation appear on top navigation bar Left navigation bar allows access to input screens

  20. Solving For

  21. Solving For Green checkmark = complete Pencil = incomplete

  22. Solving For

  23. Desired Power Enter each desired power value here and click the enter key

  24. Desired Power

  25. Predictors

  26. Predictors Enter predictors names

  27. Predictors Enter categories for each predictor Enter predictors names

  28. Response Variables

  29. Response Variables Enter each outcome variable and click “Enter”

  30. Repeated Measures

  31. Repeated Measures Enter the units (time), the number of repeated measures (3), and the spacing (equal)

  32. Hypothesis time by treatment interaction

  33. Hypothesis

  34. Hypothesis Select the type of hypothesis

  35. Hypothesis Select the factors for the hypothesis

  36. Statistical Test

  37. Statistical Test

  38. Type I Error Rate

  39. Type I Error Rate Enter each Type I error rate value and click “Enter”

  40. Choices for Means and Variance • So far, all inputs are known as part of the study design • We now must obtain reasonable values for mean differences and variability to complete the calculation

  41. Where Can I Find Means, Variances, and Correlations? • Pilot study • Similar published research • Unpublished internal studies • Clinical experience

  42. Means 0.9 1.5 -2.1 -1.2

  43. Means 0.9 1.5 -2.1 -1.2

  44. Means 0.9 1.5 -2.1 -1.2

  45. Means 0.9 1.5 -2.1 -1.2

  46. Means 0.9 1.5 -2.1 -1.2

  47. Variances and Correlations • Consider the sources of variability and correlation in the study design • Repeated measures within a given participant will be correlated • Outcome measurements will vary between participants

  48. Variances and Correlations Correlation Between Outcomes Over Time Gedney, Logan, and Baron (2003) identified predictors of the amount of experienced pain recalled over time…One of the findings was that memory of pain intensity at 1 week and 18 months had a correlation of 0.4. We assume that the correlation between measures 18 months apart will be similar to the correlation between measures 12 months apart. Likewise, the correlation between measures 6 months apart will be only slightly greater than the correlation between measures 18 months apart.

  49. Variances and Correlations Standard Deviation of the Outcome Logan, Baron, and Kohout (1995) examined whether sensory focus therapy during a root canal procedure could reduce a patient’s experienced pain. The investigators assessed experienced pain on a 5 point scale both immediately and at one week following the procedure. The standard deviation of the measurements was 0.98.

  50. GLIMMPSE Means Specifying a Mean Difference

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