1 / 15

Discussion of Presentations Issues: Imbalanced Sample Size and Equivalence Test

Explore challenges in sample size imbalance adjustment, equivalence testing, and lot correlation adjustments in statistical analyses. Review proposed methods and implications on power, error rates, and study design controls.

carolyngrant
Download Presentation

Discussion of Presentations Issues: Imbalanced Sample Size and Equivalence Test

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Discussion of presentationsIssues1. imbalanced sample size2. estimate of reference variance for margin3. adjusted for correlated lots 4. equivalence test of effect size instead5. Comparability6. 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 DIA/FDA Stat Forum 2016

  5. III. Sample Size Imbalance Adjustment • Method B: Power Comparison(σT = σR) 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 lotsTsong’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. 4. Equiv. test of effect size 90% CI 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

More Related