Statistical power lays a foundation for a successful clinical trial, thus affecting all clinical trial professionals. Underpowered studies have a higher risk of not showing a statistically significant effect at the end of the study; whereas overpowered studies can lead to unreasonably large sample sizes, unnecessary risk to patients, and added expense. This webinar will address the basics of statistical power for non-statisticians, highlighting what you need to know about statistical power, how it affects your clinical trial, and what to ask for from your statistician.

1. Understanding Statistical
Power for Non-Statisticians
WEBINAR | JUNE 28, 2016 | 12:00PM EASTERN | DIA 2016
DALE W. USNER, PH.D. | PRESIDENT | SDC

2. General Housekeeping
 Use mic & speakers or call in
 Webinar users submit questions
via chat

3. Dale W. Usner, Ph.D.
 20 years in industry
 50+ FDA and international regulatory body
interactions
 Frequent study design support
 Therapeutic Expertise
Anti-viral/Anti-infective, Cardiovascular, Gastrointestinal,
Oncology/Immunology, Ophthalmology, Surgical, Other

4. Agenda
 What is Statistical Power
 How Assumptions Affect Statistical Power and Sample Size
 How Power Is Associated With What is Statistically Significant
 Q&A

5. What is
Statistical
Power?

6. What is Statistical Power?
 Clinical/Medical: If I want to compare our Test Product
to the Control Product in systolic blood pressure, what
sample size will I need?
 Statistician: Assuming a Difference in Means of 10 mm
Hg and a common Standard Deviation (SD) of 20 mm
Hg, 64 subjects per treatment group are required to
have 80% power for a 2-sided  = 0.05 test.

7. What is Statistical Power?
Next?

8. What is Statistical Power? Questions
 What does it mean to have 80% power?

9. What is Statistical Power? Questions
 What does it mean to have 80% power?
 What does it mean to assume a difference in means of
10 mm Hg? What if the true diff <10? >10?

10. What is Statistical Power? Questions
 What does it mean to have 80% power?
 What does it mean to assume a difference in means of
10 mm Hg? What if the true diff <10? >10?
 What role does the SD play? What if the true SD is >20?
What if the true SD is <20?

11. What is Statistical Power? Questions
 What does it mean to have 80% power?
 What does it mean to assume a difference in means of
10 mm Hg? What if the true diff <10? >10?
 What role does the SD play? What if the true SD is >20?
What if the true SD is <20?
 What happens if the difference observed in the study is
<10 mm Hg?

12. Motivation
 Assuming a Difference in Means of 10 mm Hg and a
common SD of 20 mm Hg, N = 64 subjects per
treatment group are required to have 80% power for a
2-sided  = 0.05 test.
 The study will demonstrate a statistically significant
result if the difference observed in the study is ≥7.0 mm
Hg (assuming the observed SD is 20 mm Hg).

13. Motivation
 With 85% Power (N = 73 subjects / Tx Gp) observed
differences ≥6.55 mm Hg would yield statistical
significance
 With 90% Power (N = 86 subjects / Tx Gp) observed
differences ≥6.03 mm Hg would be statistical
significance

14. Background

15. Statistical Inference
 Statistical Inference: Drawing conclusions
about an entire population based on a
sample from that population.

16. Hypotheses (Efficacy)
Superiority
 H0: Test Arm is no different from Control Arm
 H1: Test Arm is different (superior) than Control Arm
Non-Inferiority
 H0: Test Arm is inferior to Control Arm
 H1: Test Arm is non-inferior to Control Arm
Desired Outcome: Reject H0 in favor of H1

17. Statistical Inference: Coin Flip
 H0: Proportion of Heads = Prop Tails = 0.50
 H1: Proportion of Heads > Prop Tails
 Flip a coin 4 times with result 3H and 1T. 75% Heads,
should H0 be rejected?

18. Statistical Inference: Coin Flip
 H0: Proportion of Heads = Prop Tails = 0.50
 H1: Proportion of Heads > Prop Tails
 Flip a coin 4 times with result 3H and 1T. 75% Heads,
should H0 be rejected?
 No, probability of this occurring under H0 is 31.25%

19. Statistical Inference: Coin Flip
 H0: Proportion of Heads = Prop Tails = 0.50
 H1: Proportion of Heads > Prop Tails
 Flip a coin 4 times with result 3H and 1T. 75% Heads,
should H0 be rejected?
 No, probability of this occurring under H0 is 31.25%
 Flip a coin 40 times with result 30H and 10T. 75%
Heads, should H0 be rejected?

20. Statistical Inference: Coin Flip
 H0: Proportion of Heads = Prop Tails = 0.50
 H1: Proportion of Heads > Prop Tails
 Flip a coin 4 times with result 3H and 1T. 75% Heads,
should H0 be rejected?
 No, probability of this occurring under H0 is 31.25%
 Flip a coin 40 times with result 30H and 10T. 75%
Heads, should H0 be rejected?
 Yes, probability of this occurring under H0 is <0.1%

21. Define  and Power
  (Type I Error) is the probability that the study
concludes the Test Arm is different from the Control
Arm, when the Test Arm truly is no different. This is a
regulatory risk.
 Power is the probability that the study concludes the
Test Arm is different from the Control Arm, when in
the Test Arm truly is different. This is sponsor risk.

22. Continuous & Binary Measures
 Continuous Measures
 Generally testing differences in Means or Medians
 Standard Deviation also very important
 Binary Measures
 Generally testing differences or ratios of proportions
 Standard Deviations generally determined by assumed
proportions

23. Power in Pictures
 Consider
 a therapy designed to lower systolic blood pressure
(SysBP) by an additional 10 mm Hg more than the
currently best selling therapy, which has been shown to
lower the SysBP to an average of 140 mm Hg.
 that each treatment has an SD of 20 mm Hg
 Assume the data follow normal distributions

24. Power in Pictures: Sample Size = 1

25. Power in Pictures: Sample Size = 5

26. Power in Pictures: Sample Size = 10

27. Power in Pictures: Sample Size = 30

28. Power in Pictures: Sample Size = 50

29. Power in Pictures: Sample Size = 64

30. Power in Pictures: Sample Size = 73

31. Power in Pictures: Sample Size = 86

32. Power in Pictures: Sample Size = 1

33. Power in Pictures: Sample Size = 5
Power: 10%

34. Power in Pictures: Sample Size = 10
Power: 18%

35. Power in Pictures: Sample Size = 30
Power: 47%

36. Power in Pictures: Sample Size = 50
Power: 69%

37. Power in Pictures: Sample Size = 64
Power: 80%

38. Power in Pictures: Sample Size = 73
Power: 85%

39. Power in Pictures: Sample Size = 86
Power: 90%

40. Standard Powers
 80%: If the test product is as efficacious as assumed
under H1, 80% of trials should reject H0 in favor of H1
by design. Generally considered to be the lowest
targeted power.
 85%: 85% of trials should reject H0 in favor of H1 by
design.
 90%: 90% of trials should reject H0 in favor of H1 by
design.

41. Sponsor Risk
 80%: If the test product is as efficacious as assumed
under H1, 20% of trials will fail to reject H0 in favor of
H1 by design.
 85%: 15% of trials will fail to reject H0 in favor of H1 by
design.
 90%: 10% of trials will fail to reject H0 in favor of H1 by
design.

42. How Assumptions Affect
Statistical Power and
Sample Size

43. Sample Size x Assumed Difference x
Power
0
100
200
300
400
500
6 7 8 9 10 11 12 13 14
TotalSampleSize
Assumed Difference
SD = 20, 2-sided alpha = 0.05
80% Power
85% Power

44. % Increase in Sample Size (N) for
Increasing Power
 Assuming a 2-sided  = 0.05 test, N increases by:
 ~14% from 80% to 85% power
 ~34% from 80% to 90% power
 Assuming a 2-sided  = 0.10 test, N increases by
 ~16% from 80% to 85% power
 ~38% from 80% to 90% power
 Assuming a 2-sided  = 0.20 test, N increases by
 ~19% from 80% to 85% power
 ~46% from 80% to 90% power

45. Sample Size x Assumed Difference x
Standard Deviation
0
100
200
300
400
500
600
6 7 8 9 10 11 12 13 14
TotalSampleSize
Assumed Difference
80% Power, 2-sided alpha = 0.05
SD = 16
SD = 18
SD = 20
SD = 22

46. % Increase in Sample Size (N) for
Increasing Standard Deviation
 Regardless of the  and power, N increases by a factor of
(SDhigh / SDlow)2

47. Sample Size x Assumed Difference x
Alpha (Type I Error)
0
100
200
300
400
6 7 8 9 10 11 12 13 14
TotalSampleSize
Assumed Difference
SD = 20, 80% Power
2-sided alpha = 0.20
2-sided alpha = 0.10

48. % Decrease in Sample Size (N) for
Increasing Alpha (Type I Error)
 Assuming 80% power, N decreases by:
 ~21% from 2-sided alpha = 0.05 to 0.10
 ~42% from 2-sided alpha = 0.05 to 0.20
 Assuming 85% power, N decreases by:
 ~20% from 2-sided alpha = 0.05 to 0.10
 ~40% from 2-sided alpha = 0.05 to 0.20
 Assuming 90% power, N decreases by:
 ~18% from 2-sided alpha = 0.05 to 0.10
 ~37% from 2-sided alpha = 0.05 to 0.20

49. Sample Size x Assumed Difference x
Power and Ratio of Randomization
0
100
200
300
400
500
600
6 7 8 9 10 11 12 13 14
TotalSampleSize
Assumed Difference
SD = 20, 2-sided alpha = 0.05
80% Power
80% Power 2:1
85% Power
85% Power 2:1

50. % Increase in Sample Size (N) for
Increasing Randomization Ratio
 Regardless of the  and power, N increases by:
 ~4.2% from 1:1 to 3:2 randomization ratio
 ~12.5% from 1:1 to 2:1 randomization ratio
 ~33.3% from 1:1 to 3:1 randomization ratio

51. % Decrease in Min Obs Diff Required for
Significance with Increasing N
 Regardless of the , the Minimum Observed Difference
required for Significance decreases by a factor of sqrt(Nlow /
Nhigh)
 With 80% Power, N = 64 / Tx Group required diff is 7.00 mm Hg
 With 85% Power, N = 73 / Tx Group required diff is
(7.00*sqrt(64/73)) = 6.55 mm Hg
 With 90% Power, N = 86 / Tx Group required diff is
(7.00*sqrt(64/86)) = 6.03 mm Hg

52. Effect on Statistical Significance
 Increasing Sample Size decreases the minimum
difference required to show statistical significance
(driven by H0) and therefore increases power.
 Required sample size increases with:
 Increasing: Power, SD, Randomization Ratio
 Decreasing: Alpha, Assumed Difference

53. Thank You
info@sdcclinical.com
www.sdcclinical.com
Q&A