1. Sample Size and Power Steven R. Cummings, MD Director, S.F. Coordinating Center

2. The Secret of Long Life Resveratrol Resveratrol In the skin of red grapes In the skin of red grapes Makes mice Makes mice  Run faster  Live longer

3. The Secret of Long Life Resveratrol Resveratrol In the skin of red grapes In the skin of red grapes Makes mice Makes mice  Run faster  Live longer Mimics ‘sirtuin:’ senses energy and controls DNA transcription

4. What I want to show Consuming resveratrol prolongs healthy life Consuming resveratrol prolongs healthy life

5. Sample Size Ingredients Testable hypothesis Testable hypothesis Type of study Type of study Statistical test Statistical test  Type of variables Effect size (and its variance) Effect size (and its variance) Power and alpha Power and alpha

6. Sample Size Ingredients Testable hypothesis Testable hypothesis Type of study Type of study Statistical test Statistical test  Type of variables Effect size (and its variance) Effect size (and its variance) Power and alpha Power and alpha

7. My research question I need to plan the study I need to plan the study My question is My question is Does consuming resveratrol lead to a long and healthy life?

8. What’s wrong with the question? I need to plan the study I need to plan the study My question is My question is Does consuming resveratrol lead to a long and healthy life?

9. What’s wrong with the question? Does consuming resveratrol lead to a long and healthy life? Vague Vague Must be measurable Must be measurable

10. “Consuming resveratrol” Most rigorous design: randomized placebo- controlled trial Most rigorous design: randomized placebo- controlled trial Comparing red wine to placebo would be difficult Comparing red wine to placebo would be difficult But resveratrol supplements are widely available But resveratrol supplements are widely available

11. Measurable (specific) outcome “Consuming resveratrol” = taking resveratrol supplements vs. taking placebo “Consuming resveratrol” = taking resveratrol supplements vs. taking placebo “Prolong healthy life” = “Prolong healthy life” =

12. Measurable (specific) outcome “Consuming resevertrol” = taking resveratrol supplements vs. taking placebo “Consuming resevertrol” = taking resveratrol supplements vs. taking placebo “Prolong healthy life” = reduces all-cause mortality “Prolong healthy life” = reduces all-cause mortality Do people randomized to get a resveratrol supplement have a lower mortality rate than those who get a placebo?

13. In whom? Elderly men and women (≥70 years) Elderly men and women (≥70 years)

14. The research hypothesis Men and women > age 70 years randomized to get a resveratrol supplement have a lower mortality rate than those who get a placebo.

15. The research hypothesis The ‘alternative’ hypothesis Men and women > age 70 years randomized to get a resveratrol supplement have a lower mortality rate than those who get a placebo. Cannot be tested statistically Cannot be tested statistically Statistical tests only reject null hypothesis - that there is no effect Statistical tests only reject null hypothesis - that there is no effect

16. The Null Hypothesis Men and women > age 70 years randomized to receive a resveratrol supplement do not have lower mortality rate than those who receive placebo. Can be rejected by statistical tests Can be rejected by statistical tests

17. Ingredients for Sample Size  Testable hypothesis Type of study Type of study Statistical test Statistical test  Type of variables Effect size (and its variance) Effect size (and its variance) Power and alpha Power and alpha

18. Types of studies Approach to sample size is different for Descriptive studies Descriptive studies  Only one variable / measurements Analytical studies Analytical studies  ‘Predictor’ and ‘outcome’ variables

19. Types of studies Approach to sample size is different for Descriptive studies Descriptive studies  Only one variable / measurements Analytical studies Analytical studies  ‘Predictor’ and ‘outcome’ variable  From the point of view of sample size estimates, cross-sectional, cohort studies and randomized trials look the same

20. Descriptive studies Descriptive Descriptive  Only one variable / measurements For example: What proportion of people who live to >100 years (centenarians) take resveratrol supplements? What proportion of people who live to >100 years (centenarians) take resveratrol supplements? What is the mean red wine intake of centenarians? What is the mean red wine intake of centenarians? Sample size based on confidence intervals Sample size based on confidence intervals Not covered in this lecture Not covered in this lecture

21. Analytical studies Analytical means a comparison Analytical means a comparison  Cross-sectional  Mean red wine intake in centenarians vs. 60-80 year olds

22. Analytical studies Analytical means a comparison Analytical means a comparison  Cross-sectional  Mean red wine intake in centenarians vs. 60-80 year olds  Cohort study  Those who drink red wine have lower mortality rates than others  Randomized trial  Elders who get resveratrol have lower mortality than those who get placebo

23. Ingredients for Sample Size  Testable hypothesis  Type of study: analytical (RCT) Statistical test Statistical test  Type of variables Effect size (and its variance) Effect size (and its variance) Power and alpha Power and alpha

24. Type of statistical tests Depends on the types of variables This works for most study planning

25. The types of variables? Men and women > age 70 years randomized to receive a resveratrol supplement do not have lower mortality rate than those who receive placebo Dichotomous: resveratrol or placebo Dichotomous: resveratrol or placebo Continuous: mortality rate Continuous: mortality rate

26. The types of variables? Men and women > age 70 years randomized to receive a resveratrol supplement do not have lower mortality rate than those who receive placebo Dichotomous: resveratrol or placebo Dichotomous: resveratrol or placebo Continuous: mortality rate Continuous: mortality rate What’s wrong?

27. The types of variables? Men and women > age 70 years randomized to receive a resveratrol supplement do not have lower mortality rate than those who receive placebo Dichotomous: resveratrol or placebo Dichotomous: resveratrol or placebo Continuous: mortality rate Continuous: mortality rate  It is a proportion at certain times  For example, 3% at 1 year

28. The appropriate test for this randomized trial for mortality

29. Ingredients for Sample Size  Testable hypothesis  Type of study: analytical (RCT)  Statistical test  Type of variables Effect size (and its variance) Effect size (and its variance) Power and alpha Power and alpha

30. Estimating the effect size For randomized trials, Estimate the expected rate in the placebo Estimate the expected rate in the placebo  For example, 10% Specify the rate in the treatment group Specify the rate in the treatment group  For example, 5% (50% decrease) * ~ mean annual female/males @ 78 yrs

31. Estimating the placebo rate Best source: cohort studies of similar populations Best source: cohort studies of similar populations Another source: data from the census Another source: data from the census In this case, we know the mortality rates from our large cohort studies of aging: In this case, we know the mortality rates from our large cohort studies of aging: 3-4% per year*; for a 3 year study: 10% 3-4% per year*; for a 3 year study: 10% * ~ mean annual female/males @ 78 yrs

32. Effect size the hardest part What should I assume for the effect of resveratrol on mortality?

33. Effect size the hardest part Ways to choose an effect size: What is likely, based on other data? What is likely, based on other data? Do a pilot study Do a pilot study Estimate based on effect on biomarkers Estimate based on effect on biomarkers What difference is important to detect? What difference is important to detect?  “We don’t want to miss a __%_ difference” What can we afford? What can we afford?

34. The effect of resveratrol on mortality rate? What is likely, based on other data? What is likely, based on other data? Do a pilot study Do a pilot study Estimate based on effect on biomarkers Estimate based on effect on biomarkers What difference is important to detect? What difference is important to detect?  “We don’t want to miss a __%_ difference” What can we afford? What can we afford?

35. Resveratrol pronged survival of mice fed high calorie diet Baur, Nature 2006 ~ 25%

36. The effect of resveratrol on mortality rate? What is likely, based on other data? What is likely, based on other data? Pilot study? What endpoint? Pilot study? What endpoint? No reliable markers for the effect on death No reliable markers for the effect on death What difference is important to detect? What difference is important to detect?  “We don’t want to miss a ____ difference” What can we afford to find? What can we afford to find?

37. The effect of resveratrol on mortality rate? What is likely, based on other data? What is likely, based on other data? Do a pilot study Do a pilot study Estimate based on biomarkers Estimate based on biomarkers What difference is important to detect? What difference is important to detect?  “We don’t want to miss a _1%_ difference” What can we afford? What can we afford?  1%: too big & expensive  5%: small and cheap

38. The effect of resveratrol on mortality rate? Finding a smaller effect is important to health Finding a smaller effect is important to health Allowing a larger effect is important for your budget Allowing a larger effect is important for your budget

39. The Science of Effect Sizes: Too large! Too small!

40. The Science of Effect Sizes Too large! Too small! Just right. Smaller effect is important to health Smaller effect is important to health Larger effect is important for your budget Larger effect is important for your budget

41. The Science of Effect Sizes Too large! Too small! Just right. Smaller effect is important to health Smaller effect is important to health Larger effect is important for your budget Larger effect is important for your budget It requires good judgment, balancing science and feasibility

42. Effect size Men and women > age 70 years randomized to receive a resveratrol supplement do not have lower mortality rate than those who receive placebo It would be important to find (I don’t want to miss) a 20% decrease It would be important to find (I don’t want to miss) a 20% decrease Placebo rate: 10% Placebo rate: 10% Resveratrol rate: 8% Resveratrol rate: 8%

43. Ingredients for Sample Size  Testable hypothesis  Type of study: analytical (RCT)  Statistical test  Type of variables  Effect size (and its variance) Power and alpha Power and alpha

44.  (alpha) The probability of finding a ‘significant’ result if nothing is going on

45. I will need to convince people Customarily, a result is ‘statistically significant’ if P<0.05 Customarily, a result is ‘statistically significant’ if P<0.05 In other words, Probability of a type I error = 5% Probability of a type I error = 5%  (alpha) = 0.05  (alpha) = 0.05

46. I will need to convince skeptics Very small chance that a positive result is an error Very small chance that a positive result is an error  (alpha) = 0.01 P<0.01 A smaller  means larger sample size A smaller  means larger sample size

47. Two-sided vs. one-sided   A 2-sided  assumes that the result could go either way A 2-sided  assumes that the result could go either way  Recognizes that you have two chances of finding something that isn’t really there  Resveratrol decreases mortality  Resveratrol increases mortality A 1-sided hypothesis reduces sample size (somewhat) A 1-sided hypothesis reduces sample size (somewhat)  A one-sided  of 0.05 corresponds to a two-sided  of 0.10 It assumes that the result could, plausibly, go only one way It assumes that the result could, plausibly, go only one way

48. Two-sided vs. one-sided   You may believe that your effect could only go one way! You may believe that your effect could only go one way!  Resveratrol is ‘natural.’ It could not increase mortality! Be humble. Be humble.  The history of research is filled with results that contradicted expectations  Vitamin D trial (JAMA 2010):  To everyone’s surprise, ~1500 IU of vitamin D/d increased the risk of falls and fractures in elderly women and men A 1-sided test is almost never the best choice A 1-sided test is almost never the best choice

49. Two-sided vs. one-sided   You may believe that your effect could only go one way! You may believe that your effect could only go one way!  Resveratrol is ‘natural.’ It could not increase mortality!

50. Two-sided vs. one-sided   You may believe that your effect could only go one way! You may believe that your effect could only go one way!  Resveratrol is ‘natural.’ It could not increase mortality! Be humble. Be humble.  The history of research is filled with results that contradicted expectations  Vitamin D trial (JAMA 2010):  To everyone’s surprise, ~1500 IU of vitamin D/d increased the risk of falls and fractures in elderly women and men A 1-sided test is almost never the best choice A 1-sided test is almost never the best choice

51.  (beta) The probability of missing this effect size in this sample, if it is really true in the populations

52. Power (1-  ) The probability of finding this effect size in this sample, if it is really true in the population

53. If it’s true, I don’t want to miss it The chance of missing the effect (  ) The chance of missing the effect (  ) is “customarily” 20% In other words Probability of a type II error = 0.20 Probability of a type II error = 0.20  (beta) = 0.20  (beta) = 0.20 Power = 1-  0.80 Power = 1-  0.80

54. I really don’t want to miss it  =.10  =.10 Power (1-  ) = 0.90 Power (1-  ) = 0.90 Greater power requires a larger sample size Greater power requires a larger sample size

55. We have all of the ingredients  Testable hypothesis  Type of study: analytical (RCT)  Statistical test: Chi-squared  Effect size 10% vs 8%  Power: 0.90; alpha: 0.05

56. From Table 6B.2 Comparing two proportions

57. From Table 6B.2 Sample size: 4,401 Sample size: 4,401 Per group Per group Total: 8,802 Total: 8,802 Does not include drop-outs Does not include drop-outs  20% drop-out: 11,002 total sample size!

58. Appropriate responses Shock and awe Shock and awe Depression Depression Anxiety Anxiety Consider alternative approaches Consider alternative approaches

59. Alternatives Tweak  one-sided Tweak  one-sided  Almost never appropriate Tweak the power: 0.80 Tweak the power: 0.80

60. From Table 6B.2 Comparing two proportions

61. Alternatives Tweak  one-sided Tweak  one-sided  Almost never appropriate Tweak the power: 0.80 Tweak the power: 0.80 Modest effect: 3,308 (6,616 total) Modest effect: 3,308 (6,616 total)

62. Alternatives Increase the effect size Increase the effect size  10% vs. 6%

63. From Table 6B.2 Comparing two proportions

64. Increasing the effect size 10% vs. 6% 10% vs. 6% Makes a big difference! Makes a big difference! 769 / group; 1,538 total (no dropouts) 769 / group; 1,538 total (no dropouts) However, still large (and not affordable) However, still large (and not affordable) Not believable Not believable

65. Alternatives: a new hypothesis Change the outcome measure Change the outcome measure  Continuous measurement  A precise measurement A ‘surrogate’ for mortality rate A ‘surrogate’ for mortality rate  Strongly associated with mortality rate  Likely to be influenced by resveratrol Walking speed Walking speed

66. Mice on resveratrol Mice fed resveratrol Mice fed resveratrol  Live 25% longer  Are significantly faster  Have greater endurance

67. Increased gait speed (0.1 m/s) in 1 year and survival over 8 years Faster by ≥0.1 m/s Slower ~20% decreased mortality rate

68. The new ingredients  New testable hypothesis  Type of study: RCT Statistical test: ? Statistical test: ?  Continuous variable (walking speed)  Difference between means (pbo vs. tx) > Effect size and variance: ? Power and alpha Power and alpha

69. Type of statistical tests Depends on the types of variables

70. The new ingredients  New testable hypothesis  Type of study: RCT Statistical test: t-test Statistical test: t-test  Continuous variable (walking speed)  Difference between means (pbo vs. tx) > Effect size and variance: E/S Power and alpha Power and alpha

71. The new ingredients  New testable hypothesis  Type of study: RCT Statistical test: t-test Statistical test: t-test  Continuous variable (walking speed)  Difference between means (pbo vs. tx) > Effect size and variance: E/S Power and alpha Power and alpha

72. E/S E = effect size: difference between the change in placebo and change in the treatment group E = effect size: difference between the change in placebo and change in the treatment group S = the variability in the outcome S = the variability in the outcome  S = ‘Standard Deviation”  For a longitudinal study of change, S is the standard deviation of the change

73. Sample size critically depends on E/S You need smaller sample size if You need smaller sample size if  Greater effect (E)  More precise measurement (lower SD)

74. What we need to determine E/S for our RCT Effect size (E) for change in walking speed Effect size (E) for change in walking speed  Mean baseline value = 1.0 m/s  Change in the placebo group = 0  Change with resveratrol = +0.1 m/s Standard deviation (S) Standard deviation (S)  Standard deviation of the change

75. S (Standard Deviation of Change) Standard deviation for the measurement Standard deviation for the measurement  Cross-sectional data: 0.25 m / sec However, we are interested in change However, we are interested in change How to find th standard deviation of change in speed? How to find th standard deviation of change in speed?  Often more difficult to find because cross-sectional surveys are more common than longitudinal studies

76. What if you don’t know the SD? Standard deviation of change in speed? Standard deviation of change in speed? If you cannot find data from other studies If you cannot find data from other studies Alternatives Alternatives  Pilot study: measure change in 3 or 6 mo.s

77. What if you don’t know the SD? Standard deviation of change in speed? Standard deviation of change in speed? If you cannot find data from other studies If you cannot find data from other studies Alternatives Alternatives  Pilot study: measure change in 3 or 6 mo.s  Or, a well educated guess

78. Estimating an S.D. the 1/4 rule ~ 4 S.D.s across a ‘usual’ range of values So, 1 S.D. will = 1/4 of the range

79. Estimating S.D. for change in walking speed the 1/4 rule Range of changes over 1 year* Range of changes over 1 year* +0.2 m/sec to -0.6 m/sec +0.2 m/sec to -0.6 m/sec Range = 0.8 m/sec Range = 0.8 m/sec 1/4 of the range = 0.2 m/sec 1/4 of the range = 0.2 m/sec * Single short, 6 meter walk

80. E/S Effect size: 0.1 m/sec difference in change Effect size: 0.1 m/sec difference in change Standard deviation: 0.2 m/sec Standard deviation: 0.2 m/sec  (We also know this from our cohort studies) E/S = 0.5 E/S = 0.5

81. The new ingredients  New testable hypothesis  Type of study: analytical (RCT)  Statistical test: t-test  Continuous variable  Difference between means  Effect size 1.0 vs. 1.1 m/sec; E/S = 0.5  Power: 0.80; alpha: 0.05

83. The new ingredients  New testable hypothesis  Type of study: analytical (RCT)  T-test  Effect size 1.0 vs.1.1 m/sec; E/S: 0.5  Power: 0.80; alpha: 0.20 Sample size: 64 per group; 128 total With 20% drop out: 160 total

84. Improving precision of the outcome measurement Increased precision (decreased SD) will decrease the sample size Increased precision (decreased SD) will decrease the sample size For example For example  Mean of repeated walks  Longer, 400 m walk  Standard deviation may improve from 0.2 m/sec to 0.15 m/sec E/S improves from 0.5 to 0.1÷ 0.15 = 0.7 E/S improves from 0.5 to 0.1÷ 0.15 = 0.7

85. A modest improvement in precision reduced sample size by 1/2

86. Summary Estimate sample size early Estimate sample size early Systematically collect the ingredients Systematically collect the ingredients Effect size is the most difficult - and important - judgement Effect size is the most difficult - and important - judgement Alternatives that reduce sample size Alternatives that reduce sample size  Compromise power  Increase effect size  Precise continuous outcomes

87. Thank you

88. Descriptive Studies

89. Type of study Descriptive Descriptive  Only one variable / measurements What proportion of centenarians take resveratrol supplements? What proportion of centenarians take resveratrol supplements?  Confidence interval for proportions What is the mean red wine intake of centenarians? What is the mean red wine intake of centenarians?  Confidence interval for the mean

90. Sample size for a descriptive study For example: “What proportion of centenarians take resveratrol supplements?” “What proportion of centenarians take resveratrol supplements?”

91. Sample size for a descriptive study For example: “What proportion of centenarians take resveratrol supplements?” “What proportion of centenarians take resveratrol supplements?” How much precision do you want? How much precision do you want?  Sample size is based on the width of the confidence interval (Table 6D and 6E)

92. Sample size for a descriptive study For example: “What proportion of centenarians take resveratrol supplements?” “What proportion of centenarians take resveratrol supplements?” How much precision do you want? How much precision do you want?  Sample size is based on the width of the confidence interval (Table 6D and 6E) I assume that 20% of centenarians take resveratrol I assume that 20% of centenarians take resveratrol  Conventional 95% C.I.  I want to be confident that the truth is within ±10%  Total width of the C.I. = 0.20

96. Supplementary slides The effect of increasing the rate of events in the population while maintaining the same effect size

97. Supplementary slides The effect of increasing the rate of events in the population The effect of increasing the rate of events in the population Maintaining the same effect size Maintaining the same effect size

98. Alternatives Increase the event rate Increase the event rate Choose an older population with higher mortality Choose an older population with higher mortality Enroll men ≥ 80 years old Enroll men ≥ 80 years old  3-year mortality: 25% Effect size: 20% reduction: 25% vs. 20% Effect size: 20% reduction: 25% vs. 20%

100. Alternatives Increase the event rate Increase the event rate Choose an older population with higher mortality Choose an older population with higher mortality Enroll men ≥ 80 years old Enroll men ≥ 80 years old  3-year mortality: 25% Effect size: 20% reduction: 25% vs. 20% Effect size: 20% reduction: 25% vs. 20% 1,133 per group; 2,266 total 1,133 per group; 2,266 total

101. Measurable (specific) predictor Does consuming reservatrol prolong healthy life? “Consuming resveratrol” = taking resveratrol supplements versus taking placebo “Consuming resveratrol” = taking resveratrol supplements versus taking placebo “Prolong healthy life” … uhh… “Prolong healthy life” … uhh… (complex endpoint: “health” and “life”)