Search

According to recent regulatory guidance, clinical trial objectives should be translated into estimands, i.e., precise descriptions of that what is to be estimated. Hence, estimands are to be formulated for influenza vaccine immunogenicity (IVI) trials, notably for one of the most popular immunogenicity trials, the non-inferiority trial. In this paper an estimand for this trial design is proposed. An estimand should state how intercurrent events are handled. Intercurrent events are events that occur after the start of the trial and that affect the endpoint's measurement or interpretation or prevent its observation. In IVI trials the intercurrent events of interest are immunological intercurrent events (IIEs). Major IIEs are identified, i.e., protocol deviations occurring during the trial that affect immunogenicity endpoints, and the consequences for the trial data collection are discussed. In the statistical analysis endpoint values that are missing or excluded from the analysis due to IIEs are to be substituted by plausible values, by means of multiple imputing. Replacing values are based on predictors of the endpoint. A distinction is made between mandatory and non-mandatory predictors. Mandatory predictors are predictors necessary to prevent biased prediction. Non-mandatory predictors are predictors that limit the additional variance due to the imputing. The four steps of the multiple imputing are explained, and available software is listed. [ABSTRACT FROM AUTHOR]

Abstract: During the pandemic (H1N1) 2009 vaccination campaign, vaccine providers collected immunization data using hybrid (paper-based and electronic methods) and electronic data systems. We measured staff time in seconds spent on data collection tasks to compare system efficiencies. The sample consisted of 38 organizations across nine Canadian provinces/territories. The total mean data collection times per client were 104s (electronic system), 143s (hybrid system with electronic registration) and 172s (hybrid system with paper registration). Electronic registration and record keeping were faster than paper-based methods; these findings should be used to improve data collection for future influenza seasons. [Copyright &y& Elsevier]

Abstract: Background: The potential for development of autoimmune diseases after vaccination with new vaccines containing novel adjuvants is a theoretical concern. Randomised, placebo-controlled trials are the best method for assessing a potential causal relationship between an adverse event and vaccination, but usually have a sample size too small to detect adverse events occurring in <1% of subjects. Incomplete case documentation may hamper definitive diagnoses, preventing accurate causality assessment. To date there are no guidelines for collection, documentation and monitoring of potential immune mediated disorders (pIMD) reported in the course of clinical trials with adjuvanted vaccines. Objective: This paper proposes a methodology for collection of pIMDs in clinical vaccine trials, with the objective of obtaining complete and reliable data using standardised methodology for its collection and analysis. Recommendations: The role of the study investigator in prospective, standardised safety data collection is key and can be facilitated by providing a pIMD list in study documents and disease-specific standard questionnaires to assist timely and thorough documentation. External expert review of histopathology samples or other specialised diagnostic data would increase diagnostic accuracy. Centralised case ascertainment using standard case definitions would identify true cases of interest. We propose collection of safety data for at least 6 months and up to one year after the last vaccine dose. Bio-banking as a platform for collecting samples from enrolled patients for future use (e.g., to measure biomarkers of diagnostic, prognostic or predictive utility) could eventually provide valuable information in cases where a pIMD is diagnosed during the study period. Conclusion: Standardised collection of safety data to allow appropriate analyses are optimal approaches for detecting rare events in clinical trials. Appropriate data analysis will then more reliably define potential causal relationships with vaccination. [Copyright &y& Elsevier]