Nirali ★ India, 20071024 08:21 Posting: # 1245 Views: 7,949 

Dear All, What is the importance of Null ratio (=test/reference) in sample size calculations? It should be 1 (test/reference=1) or 0.95 or 1.05 (5% variability in test/reference). I am using paried equivalence test in SAS for sample size calculation where it asked for null ratio. Here I am confused whether to use 1 or 0.95 or to be consider actual variability (geometric mean ratio of test/ref untransformed data) received from the reference/pilot data. I gone through many guidelines but none has mentioned clearly about null ratio. ANVISA says take difference 0% or 5%. USFDA suggested to consider 5% variability. pls guide what is appropriate? Is it appropriate to consider actual variability as a null ratio regards, Nirali. 
Helmut ★★★ Vienna, Austria, 20071102 17:05 @ Nirali Posting: # 1266 Views: 4,971 

Dear Nirali! » What is the importance of Null ratio (=test/reference) in sample size calculations? Since power curves become very steep if moving the test/reference ratio away from 1 (for examples see this post) it's quite important to use reasonable estimates (of both the expected deviation of test from reference and CV_{intra}). » I gone through many guidelines but none has mentioned clearly about null ratio. ANVISA says take difference 0% or 5%. USFDA suggested to consider 5% variability. pls guide what is appropriate? » Is it appropriate to consider actual variability as a null ratio The magical number ±5% has its origin in the following reasoning: Specifications for batch release commonly are set to ±5% of the declared content. If both products show identical BA, you may expect in your study a deviation of ±5% (let’s say, test’s content 2.5% and reference’s content +2.5% of nominal). Nwakama P, Haidar S, Yang Y, Davit B, Conner D, Yu L. Generic Drug Products Demonstrate Small Differences in Bioavailability Relative to the Brand Name Counterparts: A Review of ANDAs Approved 1996 – 2004. AAPS J. 2005;7/S2:Abstract M1262. online abstract ... report from 1411 studies submitted to the FDA deviations of 3.13% (±2.71) for AUC_{t}, 3.05% (±2.62) for AUC_{inf}, and 4.52% (±3.57) for C_{max}. A more recent metaanalysis of 1636 BE studies submitted to the FDA within 19962005 showed deviations of 3.19% (±2.72) for AUC_{t}, 3.12% (±2.66) for AUC_{inf}, and 4.50% (±3.57) for C_{max}. If you have no clue, expect the true ratio to be 1 and want to be very conservative you may even go with an expected deviation of ±10%. Power curves are not symmetrical; if you want to go with a 5% deviation of test from reference and have no clue about which direction the deviation will point to, it's wise to use a ratio of 0.95 instead of 1.05. Power is lower for ratios <1 than for ratios >1; therefore you should apply the most conservative approach. At least some guidelines are specific: Canada calls for a sample size estimation based on the expected deviation, but wants to see two evaluations  one with original data and one with data corrected for actual content of study batches. The European guideline calls for a sample size estimation based on the expected deviation; a potency correction is currently under discussion for the next revision. The WHO’s guideline also suggests the expected deviation; a dose correction (which was suggested in earlier versions) is currently not recommended. As guidelines are ambiguous in their recommendations about potency correction, some people opt for this procedure:
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