1. Discussion of presentations Issues 1. imbalanced sample size 2
Discussion of presentations Issues 1. imbalanced sample size 2. estimate of reference variance for margin 3. adjusted for correlated lots 4. equivalence test of effect size instead 5. Comparability 6. tier 2 – What to propose for k? - it is more a statistical scale instead of a statistical test

2. Tsong and Dong’s presentation on Sample Size Imbalance Adjustment
Method B: when the sample size ratio > 1.5
Use all the reference sample to estimate μR and σR;
Use nR* = Min(1.5×nT, nR) to compute the CI.
Satterthwaite approximation is recommended for CI computation.
DIA/FDA Stat Forum 2016

3. III. Sample Size Imbalance Adjustment
Statistical properties of Method B
Decision Rule: conclude stat. Equivalence in means if T1 > t1-α, df* and T2 < -t1-α, df* .
Power:
p(u1,u2)~bivariate t(df*,df*, θ1, θ2,1)
DIA/FDA Stat Forum 2016

4. III. Sample Size Imbalance Adjustment
Method B: Type I error rate vs. SS Ratio
the type I error rate is a monotone function of the sample size ratio for given variance. More specifically, the type I error rate is more conservative if the sample size ratio (imbalance) is large.
DIA/FDA Stat Forum 2016

5. III. Sample Size Imbalance Adjustment
Method B: Power Comparison(σT = σR)
the type I error rate is a monotone function of the sample size ratio for given variance. More specifically, the type I error rate is more conservative if the sample size ratio (imbalance) is large.
DIA/FDA Stat Forum 2016

6. Use nR*=min(1.5nT,nR)
Power
Type I Error

7. Very similar results But the issues are
The need of information of biosimilar product
There is no blindness or randomization of the study
No control of change design through interim analysis
Power rewarding based on product of extremely large sample size

8. II. Estimated vs. fixed equivalence margin
Guidance will only focus on fixed margin
Estimation margin but test as fixed will inflate type I error rate and reduce power
Alternative approaches are in in the process of research and evaluation
Generalized confidence interval of effect size approach has been proposed

9. III. Correlation between lots Tsong’s presentation on adjustment of lot correlation
Parameters of a population may be estimated unbiasedly if the sample is taken randomly and representatively
Which means every member of the population should have the same chance to be sampled
Or randomly sample clusters before members but each clusters should represent the population
The practical analytical population may consists of clusters do not represent the population
And the random sample are taken from clusters only available during the study time
In conclusion, study sample define a population which is a population ℙ generalized from the sample

10. Practical sampling design of analytical biosimilarity assessment

11. True batch population and sample defined population

12. Out of 30 data set we examined, 70% of data set has observed reference variability is larger than the biosimilar variability

13. Population, sample defined population ℙ, random sample and correlated samples
Given the fact that there is no formula, example or knowledge of how the correlation is calculated, or how large it is based on the data available to the biosimilar sponsor, the adjustment proposed by researcher or sponsor becomes unrealistic
Under the assumption that such knowledge is available, we conducted a research and found the conventional t-test used for equivalence assessment inflates type I error rate and lowers power (Shen, Wang (2016), to submit to JBS)
2016 MBSW

14. 90% CI 4. Equiv. test of effect size
Is [1-(confidence level)]/2 = type I error rate of two one-sided test?
Is such a test monotone from -∞ to 0?
Tsong’s presentation “hypothesis testing and confidence interval – where is the duality?

15. 5. Comparability
Comparability is to demonstrate the consistency of the same product before and after some change to its manufacturing process
Equivalence test of means before and after change(s)
Profile comparison – multivariate equivalence
Normal vs. non-normal, parametric vs. nonparametric, equal vs. unequal variance – small sample problem
Dissolution profile comparison
Model dependent vs. model independent methods
SK (saranadasa & krishnamoorthy (2015)) using mean difference at all time point
IUT using non-constant margin at each time point
Parallelism testing