Post on 21-Jan-2016
Biostatistics Case Studies 2014
Youngju Pak, PhD.
Biostatistician
ypak@labiomed.org
Session 2:
Sample Size & Power for Inequality and Equivalence Studies II
What we have leaned in session 1? Info Needed for Study Size: Comparing Means (Inequality test)
1. Effect (clinically meaningful difference)
2. Subject variability
3. Type I error (1.96 for α=0.05; 2.58 for α=0.01)
4. Power (0.842 for 80% power; 1.645 for 95% power)
Free sample size calculations:
www.stat.uiowa.edu/~rlenth/Power
(1.96 + 0.842)22SD2
Δ2N =
Case Study
Ophthalmology 2006; 113:70-76.
Abstract
Primary Outcome and Study Size
Study Size - Page 72 bottom of column 1:
Primary Outcome - Page 72 middle of column 1:
Needs Consensus
PI’s Gamble
Testing inequality vs. equivalence.
• Hypotheses for testing inequality:Ha: | mean(treatment ) - mean (control ) | ≠ 0H0: | mean(treatment ) - mean (control ) | = 0
• Hypotheses for testing inequality:• Ha : δ1< mean(trt1) – mean (trt2) < δ2
• H0 : mean(trt1) – mean (trt2) ≤ δ 1 Non-Inferiority
or mean(trt1) – mean (trt2) ≥ δ2
Graphical presentation of equivalence test
• With our regular t-tests, to conclude there is a substantial difference you must observe a difference large enough to conclude it is not due to sampling error
• To conclude there is not a substantial difference you must observe a difference small enough to reject that closeness is not due to sampling error from distributions centered on large effects
Non-Inferiority Study
• Usually a new treatment or regimen is compared with an accepted treatment or regimen or standard of care.
• The new treatment is assumed inferior to the standard and the study is designed to show overwhelming
evidence that it is at least nearly as good, i.e., non- inferior. It usually has other advantages, e.g., oral vs. inj.
• A negative inferiority study fails to detect inferiority, but does not necessarily give evidence for non-inferiority.
• The accepted treatment is usually known to be efficacious already, but an added placebo group may also be used.
How to determine Sample Size?
• For IOP study, we have– Ha: mean IOP change uf – mean IOP change f < 1.5
– H0: mean IOP change uf – mean IOP change f ≥ 1.5
thus, we are only interested in the upper limit of the difference Non-inferiority one-sided T-test
• Thus we reject the H0 if Signal/ Noise < some clinical value.
• But N for a non-inferiority test require more complicated parameters such as the non-centrality parameter of the t-distribution (a Two One Sided T-test is usually used for the equivalence test ).
Let’s run a softwarefrom
www.stat.uiowa.edu/~rlenth/Power• Information you will need
– Equivalence Margin • Non-Inferiority Margin(NIM) =1.5 for the IOP
study– Assumed mean difference in change of IOP
between two groups -> usually zero difference assumed but it is assumed 0.5 for the IOP study
– SD of changes of IOP = 3.5– α (usually set to 2.5%) since the confidence level of
the confidence interval is (100-2 x α) %
Sample size for IOP study
Three dimensional power curve for
a non-inferiority test
How do we determine if the fixed method is noninferior to the unfixed method?
Regardless of study aim – to prove treatments equivalent or to prove them different - inference can be based on:
Primary Outcome: IOP reduction D= Duf – Df , where Df = mean IOP reduction with fixed therapy
Typical superiority/inferiority study:Compare to 0.
Non-inferiority study:Compare to δ2, a pre-specified margin of
equivalence (1.5 here).
= 95% CI for D(= Duf – Df ) = “true (population) values for D”
Typical Analysis: Inferiority or Superiority
H0: Duf – Df = 0
H1: Duf – Df ≠ 0
Aim: H1 → therapies differ
α = 0.05 & N=2•194
Power = 80%
when Δ=1, SD=3.5
Fixed is inferior
= 95% CI for D = “true (population) values for D”
Fixed is superior
0
0
Du – Df
[Not used in this paper]
0No difference detected
Du – Df
Du – Df
Typical Analysis: Inferiority Only
H0: Du – Df ≤ 0
H1: Du – Df > 0
Aim: H1 → fixed is inferior
α = 0.025 & N=2•194
Power = 80% for
when Δ=1, SD=3.5
Fixed is inferior
= 95% CI for Du – Df= “true (population) values for D”
0
0
Duf – Df
[Not used in this paper]
0
Inferiority not detectedDuf – Df
Duf – Df
( α = 0.05 → N=2•153 )
Non-Inferiority
H0: Du – Df ≥ 1.5
H1: Du – Df < 1.5
Aim: H1 → fixed is non-inferior
α = 0.025 & N=2•194
Power = 80% for
When Δ= 0.5, NIM=1.5
Fixed is non-inferior
= 95% CI for Du – Df= “true (population) values for D”
0
0
Duf – Df
[As in this paper]
0Non-Inferiority not detected
Duf – Df
Duf – Df
1.5
1.5
Fixed is inferior1.5
Inferiority and Non-Inferiority
Fixed is non-inferior
= 95% CI for Du – Df = “true (population) values for D”
0
0
0Neither is detected
Duf – Df
1.5
1.5
Fixed is inferior
0 1.5Fixed is “non-clinically”
inferior
D^uf = 9.0 D^f = 8.7 D^ = 0.3 95% CI = -0.1 to 0.7Observed Results:
Fixed is non-inferior0 1.5
1.5
Conclusions: General
• “Negligibly inferior” would be a better term than non-inferior.
• All inference can be based on confidence intervals.
• Pre-specify the comparisons to be made. Cannot test for both non-inferiority and superiority.
• Power for only one or for multiple comparisons, e.g., non-inferiority and inferiority. Power can be different for different comparisons.
• Very careful consideration must be given to choice of margin of equivalence (1.5 here). You can be risky and gamble on what expected differences will be (0.5 here), but the study is worthless if others in the field would
find your margin too large.
• FDA Guidelines :
http://www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/ucm202140.pdf
Where, M1= Full effect of the active control compare with the placebo effectM2= NI Margin
Self-Quiz
1. Give an example in your specialty area for a superiority /inferiority study. Now modify it to an equivalence study. Now modify it to a non-inferiority study.
2. T or F: The main point about non-inferiority studies is that we are asking whether a treatment is as good or better vs. worse than another treatment, so it uses a one-sided test.
3. Power for a typical superiority test is the likelihood that you will declare treatment differences (p<0.05) if treatments really differ by some magnitude Δ. Explain what power means for a non-inferiority study.
4. T or F: Last-value-carried-forward is a good way to handle drop-outs in a non-inferiority study. Explain.
continued
Self-Quiz
5. T or F: In a non-inferiority study, you should first test for non-inferiority with a confidence interval, and then use a t-test to test for superiority, but only if non-inferiority was established at the first step.
6. What is the meaning of the equivalence margin, and how do you determine it?
continued
Self-Quiz
7. Suppose the primary outcome for a study is a serum inflammatory marker. If it’s assay is poor (low reproducibility), then it is more difficult to find treatment differences in a typical superiority/inferiority study than for a better assay, due to this noise. Would it be easier or more difficult to find non-inferiority with this assay, compared to a better assay?
8. Does the assumed treatment difference (0.5 here) for power calculations have the same meaning as the difference used for power calculations in a typical superiority/inferiority study?
Self-Quiz
1. Give an example in your specialty area for a superiority /inferiority study. Now modify it to an equivalence study. Now modify it to a non-inferiority study.
Self-Quiz
1. Answer
Vaccine Testing:
Superiority: New candidate vaccine vs. placebo
Equivalence: Antigen potency between two manufacturing plants or lots.
Non-Inferiority: New candidate vaccine vs. old one.
Self-Quiz
2. T or F: The main point about non-inferiority studies is that we are asking whether a treatment is as good or better vs. worse than another treatment, so it uses a one-sided test.
Self-Quiz
2. Answer
False. That is a feature of these studies, but not their distinguishing feature. They and equivalence studies are used to try to prove sameness, as opposed to typical studies that try to prove differences.
Self-Quiz
3. Power for a typical superiority test is the likelihood that you will declare treatment differences (p<0.05) if treatments really differ by some magnitude Δ. Explain what power means for a non-inferiority study.
Self-Quiz
3. Answer
Power for a non-inferiority study is the likelihood that you will declare treatment A to be no worse than a pre-specified magnitude δ from B (p<0.05) if treatments really differ by some Δ. Of course, Δ is less than δ , and is often 0.
Self-Quiz
4. T or F: Last-value-carried-forward is a good way to handle drop-outs in a non-inferiority study. Explain.
Self-Quiz
4. Answer
False. LVCF biases results toward less of a difference. This makes typical superiority studies conservative, but increases the chance of a falsely “proving” the aim.
Self-Quiz
5. T or F: In a non-inferiority study, you should first test for non-inferiority with a confidence interval, and then use a t-test to test for superiority, but only if non-inferiority was established at the first step.
Self-Quiz
5. Answer
False. You must specify a-priori superiority, in order to have a legitimate claim of proving it (beyond a reasonable (5%) doubt). The stated sequential strategy will only allow you to claim an observed result, without a statement about it’s certainty.
Self-Quiz
6. What is the meaning of the equivalence margin, and how do you determine it?
Self-Quiz
6. Answer
The equivalence margin is the maximum difference between treatments that is considered to be negligible or unimportant.
It must be pre-specified in order to prove equivalence or non-inferiority to that degree, rather than just noting it as an observation. Thus, it is ideally determined by peer-agreement or FDA concurrence prior to starting the study.
Self-Quiz
7. Suppose the primary outcome for a study is a serum inflammatory marker. If it’s assay is poor (low reproducibility), then it is more difficult to find treatment differences in a typical superiority/inferiority study than for a better assay, due to this noise. Would it be easier or more difficult to find non-inferiority with this assay, compared to a better assay?
Self-Quiz
7. Answer
It would still be more difficult to show the aim, non-inferiority here, since CIs will be wider, but there will be no bias due to it toward either treatment.
Generally, poorer study conduct is penalized in superiority studies and rewarded in non-inferiority studies, but that is not true for this type of poorer measurement error.
Self-Quiz
10.Does the assumed treatment difference (0.5 for the IOP study) for power calculations have the same meaning as the difference used for power calculations in a typical superiority/inferiority study?
Self-Quiz
10.Answer
No. Here, it is our best estimate of true treatment differences.
For superiority studies, the difference is ideally the minimal difference that is “clinically relevant”, not the expected difference, closer in meaning to the equivalence margin here. In practice, it is the smallest difference that logistics, money, time, and effort will allow us to detect with specified certainty.