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Introduction to Sample Size and Power Tamuno Alfred, PhD - PowerPoint PPT Presentation

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DataCamp Designing and Analyzing Clinical Trials in R DESIGNING AND ANALYZING CLINICAL TRIALS IN R Introduction to Sample Size and Power Tamuno Alfred, PhD Biostatistician DataCamp Designing and Analyzing Clinical Trials in R Statistical

  1. DataCamp Designing and Analyzing Clinical Trials in R DESIGNING AND ANALYZING CLINICAL TRIALS IN R Introduction to Sample Size and Power Tamuno Alfred, PhD Biostatistician

  2. DataCamp Designing and Analyzing Clinical Trials in R Statistical inference

  3. DataCamp Designing and Analyzing Clinical Trials in R Importance of correct sample size Costs Study completion time Exposure to experimental drug Patients receiving no treatment Ability to reject null hypothesis

  4. DataCamp Designing and Analyzing Clinical Trials in R Requirements

  5. DataCamp Designing and Analyzing Clinical Trials in R Requirements Trial purpose (compare weight loss between drug and placebo)

  6. DataCamp Designing and Analyzing Clinical Trials in R Requirements Trial purpose (compare weight loss between drug and placebo) Primary endpoint (difference in mean % weight loss)

  7. DataCamp Designing and Analyzing Clinical Trials in R Requirements Trial purpose (compare weight loss between drug and placebo) Primary endpoint (difference in mean % weight loss) Statistical analysis of primary endpoint (two-sample t-test)

  8. DataCamp Designing and Analyzing Clinical Trials in R Requirements Trial purpose (compare weight loss between drug and placebo) Primary endpoint (difference in mean % weight loss) Statistical analysis of primary endpoint (two-sample t-test) Smallest meaningful difference, δ (3)

  9. DataCamp Designing and Analyzing Clinical Trials in R Requirements Trial purpose (compare weight loss between drug and placebo) Primary endpoint (difference in mean % weight loss) Statistical analysis of primary endpoint (two-sample t-test) Smallest meaningful difference, δ (3) Variability (standard deviation=10)

  10. DataCamp Designing and Analyzing Clinical Trials in R Requirements Trial purpose (compare weight loss between drug and placebo) Primary endpoint (difference in mean % weight loss) Statistical analysis of primary endpoint (two-sample t-test) Smallest meaningful difference, δ (3) Variability (standard deviation=10) Significance level, α (0.05)

  11. DataCamp Designing and Analyzing Clinical Trials in R Requirements Trial purpose (compare weight loss between drug and placebo) Primary endpoint (difference in mean % weight loss) Statistical analysis of primary endpoint (two-sample t-test) Smallest meaningful difference, δ (3) Variability (standard deviation=10) Significance level, α (0.05) Power to detect treatment effect (80%)

  12. DataCamp Designing and Analyzing Clinical Trials in R Hypothesis testing H0: μ1 = μ2, i.e. no treatment difference

  13. DataCamp Designing and Analyzing Clinical Trials in R Two-sample t-test power.t.test(delta=3, sd=10, power=0.8, type = "two.sample", alternative = "two.sided")

  14. DataCamp Designing and Analyzing Clinical Trials in R Relationship between power and sample size power.t.test(delta=3, sd=10, power=0.9, type = "two.sample", alternative = "two.sided")

  15. DataCamp Designing and Analyzing Clinical Trials in R Relationship between power and sample size

  16. DataCamp Designing and Analyzing Clinical Trials in R Relationship between treatment difference and sample size

  17. DataCamp Designing and Analyzing Clinical Trials in R Test of proportions power.prop.test(p1=0.3, p2=0.15, power=0.8)

  18. DataCamp Designing and Analyzing Clinical Trials in R DESIGNING AND ANALYZING CLINICAL TRIALS IN R Let's practice!

  19. DataCamp Designing and Analyzing Clinical Trials in R DESIGNING AND ANALYZING CLINICAL TRIALS IN R Sample size adjustments Tamuno Alfred, PhD Biostatistician

  20. DataCamp Designing and Analyzing Clinical Trials in R One-sided tests

  21. DataCamp Designing and Analyzing Clinical Trials in R One-sided tests power.t.test(delta=3, sd=10, power=0.8, type = "two.sample", alternative = "two.sided")

  22. DataCamp Designing and Analyzing Clinical Trials in R One-sided tests power.t.test(delta=3, sd=10, power=0.8, type = "two.sample", alternative = "one.sided")

  23. DataCamp Designing and Analyzing Clinical Trials in R Unequal group sizes

  24. DataCamp Designing and Analyzing Clinical Trials in R Unequal group sizes library(samplesize) n.ttest(power = 0.8, alpha = 0.05, mean.diff = 3, sd1 = 10, sd2 = 10, k = 0.5, design = "unpaired", fraction = "unbalanced")

  25. DataCamp Designing and Analyzing Clinical Trials in R Unequal group sizes library(samplesize) n.ttest(power = 0.8, alpha = 0.05, mean.diff = 3, sd1 = 10, sd2 = 10, k = 0.5, design = "unpaired", fraction = "unbalanced")

  26. DataCamp Designing and Analyzing Clinical Trials in R Unequal variances

  27. DataCamp Designing and Analyzing Clinical Trials in R Unequal variances n.ttest(power = 0.8, alpha = 0.05, mean.diff = 3, sd1 = 9.06, sd2 = 9.06, k = 1, design = "unpaired", fraction = "balanced")

  28. DataCamp Designing and Analyzing Clinical Trials in R Unequal variances n.ttest(power = 0.8, alpha = 0.05, mean.diff = 3, sd1 = 9.06, sd2 = 9.06, k = 1, design = "unpaired", fraction = "balanced")

  29. DataCamp Designing and Analyzing Clinical Trials in R Loss to follow-up Q: anticipated dropout rate Multiply original sample size by

  30. DataCamp Designing and Analyzing Clinical Trials in R Loss to follow-up Q: anticipated dropout rate Multiply original sample size by orig.n <- power.t.test(delta=3, sd=10, power=0.8, type = "two.sample", alternative = "one.sided")$n orig.n ceiling(orig.n/(1-0.1))

  31. DataCamp Designing and Analyzing Clinical Trials in R DESIGNING AND ANALYZING CLINICAL TRIALS IN R Let's practice!

  32. DataCamp Designing and Analyzing Clinical Trials in R DESIGNING AND ANALYZING CLINICAL TRIALS IN R Interim analyses and stopping rules Tamuno Alfred, PhD Biostatistician

  33. DataCamp Designing and Analyzing Clinical Trials in R Motivation Patients recruited over time

  34. DataCamp Designing and Analyzing Clinical Trials in R Motivation Patients recruited over time Data accumulated gradually

  35. DataCamp Designing and Analyzing Clinical Trials in R Motivation Patients recruited over time Data accumulated gradually Safety and efficacy can be monitored regularly

  36. DataCamp Designing and Analyzing Clinical Trials in R Motivation Patients recruited over time Data accumulated gradually Safety and efficacy can be monitored regularly Investigators must safeguard patients' interests

  37. DataCamp Designing and Analyzing Clinical Trials in R When to stop a trial early Efficacy

  38. DataCamp Designing and Analyzing Clinical Trials in R When to stop a trial early Efficacy Safety

  39. DataCamp Designing and Analyzing Clinical Trials in R When to stop a trial early Efficacy Safety Futility

  40. DataCamp Designing and Analyzing Clinical Trials in R When to stop a trial early Efficacy Safety Futility Other Cost Inability to recruit enough patients Poor trial design

  41. DataCamp Designing and Analyzing Clinical Trials in R Stopping rules Interim analyses often require increased sample size

  42. DataCamp Designing and Analyzing Clinical Trials in R Stopping rules Interim analyses often require increased sample size Multiple testing increases chance of Type I error

  43. DataCamp Designing and Analyzing Clinical Trials in R Stopping rules Interim analyses often require increased sample size Multiple testing increases chance of Type I error

  44. DataCamp Designing and Analyzing Clinical Trials in R Stopping rules Interim analyses often require increased sample size Multiple testing increases chance of a Type I error Stopping rules use p-values or test- statistics

  45. DataCamp Designing and Analyzing Clinical Trials in R Pocock (Fixed Nominal) Rule

  46. DataCamp Designing and Analyzing Clinical Trials in R Pocock (Fixed Nominal) Rule library(gsDesign) Pocock <- gsDesign(k=3, test.type=2, sfu="Pocock") 2*(1-pnorm(Pocock$upper$bound))

  47. DataCamp Designing and Analyzing Clinical Trials in R Pocock (Fixed Nominal) Rule library(gsDesign) Pocock<- gsDesign(k=3, test.type=2, sfu="Pocock") 2*(1-pnorm(Pocock$upper$bound))

  48. DataCamp Designing and Analyzing Clinical Trials in R Pocock (Fixed Nominal) Rule library(gsDesign) Pocock<- gsDesign(k=3, test.type=2, sfu="Pocock") 2*(1-pnorm(Pocock$upper$bound)) Pocock.ss <- gsDesign(k=3, test.type=2, sfu="Pocock", n.fix=200, beta=0.1) ceiling(Pocock.ss$n.I)

  49. DataCamp Designing and Analyzing Clinical Trials in R Pocock (Fixed Nominal) Rule library(gsDesign) Pocock<- gsDesign(k=3, test.type=2, sfu="Pocock") 2*(1-pnorm(Pocock$upper$bound)) Pocock.ss<- gsDesign(k=3, test.type=2, sfu="Pocock", n.fix=200, beta=0.1) ceiling(Pocock.ss$n.I)

  50. DataCamp Designing and Analyzing Clinical Trials in R O’Brien-Fleming Rule

  51. DataCamp Designing and Analyzing Clinical Trials in R O’Brien-Fleming Rule OF <- gsDesign(k=3, test.type=2, sfu="OF") 2*(1-pnorm(OF$upper$bound))

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