Handling Missing Data in Vaccine Clinical Trials for Immunogenicity and Safety Evaluation

Abstract

In clinical trials, study subjects are usually followed for a period of time after treatment, and the missing data issue is almost inevitable due to various reasons, including early dropout or lost-to-follow-up. It is important to take the missing data into consideration at the study design stage to minimize its occurrence throughout the study and to prospectively account for it in the analyses. There are many methods available in the literature that are designed to handle the missing data issue under various settings. Vaccines are biological products that are primarily designed to prevent infectious diseases, and are different from pharmaceutical products, which traditionally have been chemical products designed to treat or cure diseases. While a lot of similarities exist between clinical trials for vaccines and those for pharmaceutical products, there are some unique issues in vaccine trials, including how to handle the missing data, which calls for special considerations. In this report we present a variety of statistical approaches for analyses of vaccine immunogenicity and safety trials in the presence of missing data. The methods are illustrated with numerical simulations and vaccine trial examples.

Key Words:

• Missing at random
• Missing completely at random
• Missing data
• Vaccine clinical trial

Notes

^aGMC ratio of the ZOSTAVAX group over the placebo group.

Note: Sample size n = 100 per group.

^†10,000 simulations in each parameter setting; the simulation standard errors for coverage rate and tail error rate are 0.22% and 0.16%, respectively.

^‡Percentage of coverage for the confidence interval of the estimated mean .

^{<sect >}Left tail error implies the lower limit of the 95% CI > δ.

Right tail error implies the upper limit of the 95% CI < δ.