Modeling the Long-term Antibody Response and Duration of Immune Protection Induced by an Inactivated, Preservative-free Hepatitis A Vaccine (Healive) in Children / 生物医学与环境科学（英文）

Objective@#Long-term seroprotection the hepatitis A vaccine is essential for the prevention of disease from the hepatitis A virus (HAV). Due to documented difficulties during decade-long follow-ups after receiving vaccines, statistical-modeling approaches have been applied to predict the duration of immune protection.@*Methods@#Based on five-year follow-up data from a randomized positive-controlled trial among Chinese children (1-8 years old) following a 0, 6 months vaccination schedule, a power-law model accounting for the kinetics of B-cell turnover, as well as a modified power-law model considering a memory-B-cell subpopulation, were fitted to predict the long-term immune responses induced by HAV vaccination (Healive or Havrix). Anti-HAV levels of each individual and seroconversion rates up to 30 years after vaccination were predicted.@*Results@#A total of 375 participants who completed the two-dose vaccination were included in the analysis. Both models predicted that, over a life-long period, participants vaccinated with Healive would have close but slightly higher antibody titers than those of participants vaccinated with Havrix. Additionally, consistent with previous studies, more than 90% of participants were predicted to maintain seroconversion for at least 30 years. Moreover, the modified power-law model predicted that the antibody titers would reach a plateau level after nearly 15 years post-vaccination.@*Conclusions@#Based on the results of our modeling, Healive may adequately induce long-term immune responses following a 0, 6 months vaccination schedule in children induction of memory B cells to provide stable and durable immune protection.

Power Analysis and Sample Size Determination for Crossover Trials with Application to Bioequivalence Assessment of Topical Ophthalmic Drugs Using Serial Sampling Pharmacokinetic Data / 生物医学与环境科学（英文）

OBJECTIVE@#To develop methods for determining a suitable sample size for bioequivalence assessment of generic topical ophthalmic drugs using crossover design with serial sampling schemes.@*METHODS@#The power functions of the Fieller-type confidence interval and the asymptotic confidence interval in crossover designs with serial-sampling data are here derived. Simulation studies were conducted to evaluate the derived power functions.@*RESULTS@#Simulation studies show that two power functions can provide precise power estimates when normality assumptions are satisfied and yield conservative estimates of power in cases when data are log-normally distributed. The intra-correlation showed a positive correlation with the power of the bioequivalence test. When the expected ratio of the AUCs was less than or equal to 1, the power of the Fieller-type confidence interval was larger than the asymptotic confidence interval. If the expected ratio of the AUCs was larger than 1, the asymptotic confidence interval had greater power. Sample size can be calculated through numerical iteration with the derived power functions.@*CONCLUSION@#The Fieller-type power function and the asymptotic power function can be used to determine sample sizes of crossover trials for bioequivalence assessment of topical ophthalmic drugs.

Data quality in clinical trials: the role of blind review / 药学学报

Blind review is one of the most important milestones in clinical trials, which connects data management process to statistical analysis. During blind review, data quality should be reviewed and assessed on both data management and statistical aspects. The primary work of data managers in blind review is to ensure the accuracy of data before it is handed over to biostatistics group. Database auditing, listing data reviewing and reconciliation should become a good clinical data management practice. Statisticians, on the other hand, will focus on quality findings related to protocol deviations or protocol violations. To investigate the protocol deviations and/or violations and relevant impacts on data outcomes, it is important to provide the essential basis of data quality through the blind review, and to assess the reliability of study outcomes.