Four different study designs to evaluate vaccine safety were equally validated with contrasting limitations
ABSTRACT We conducted a simulation study to empirically compare four study designs [cohort, case-control, risk-interval, self-controlled case series (SCCS)] used to assess vaccine safety.
Using Vaccine Safety Datalink data (a Centers for Disease Control and Prevention-funded project), we simulated 250 case sets of an acute illness within a cohort of vaccinated and unvaccinated children. We constructed the other three study designs from the cohort at three different incident rate ratios (IRRs, 2.00, 3.00, and 4.00), 15 levels of decreasing disease incidence, and two confounding levels (20%, 40%) for both fixed and seasonal confounding. Each of the design-specific study samples was analyzed with a regression model. The design-specific beta; estimates were compared.
The beta; estimates of the case-control, risk-interval, and SCCS designs were within 5% of the true risk parameters or cohort estimates. However, the case-control's estimates were less precise, less powerful, and biased by fixed confounding. The estimates of SCCS and risk-interval designs were biased by unadjusted seasonal confounding.
All the methods were valid designs, with contrasting strengths and weaknesses. In particular, the SCCS method proved to be an efficient and valid alternative to the cohort method.
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- "It is claimed that the stratified Cox's partial likelihood with an arbitrary constant as the time to event gives the same results as a conditional Poisson regression model (Cummings, McKnight and Weiss 2003; Cummings, McKnight and Greenland, 2003). The Cox's stratified partial likelihood has been used in vaccine safety studies for modeling count data (France et al., 2004; Glanz et al., 2006; Hambidge, et al., 2006). But this has not been "
ABSTRACT: The self-controlled case series (SCCS) and the matched cohort are two frequently used study designs to adjust for known and unknown con-founding effects in epidemiological studies. Count data arising from these two designs may not be independent. While conditional Poisson regres-sion models have been used to take into account the dependence of such data, these models have not been available in some standard statistical soft-ware packages (e.g., SAS). This article demonstrates 1) the relationship of the likelihood function and parameter estimation between the conditional Poisson regression models and Cox's proportional hazard models in SCCS and matched cohort studies; 2) that it is possible to fit conditional Pois-son regression models with procedures (e.g., PHREG in SAS) using Cox's partial likelihood model. We tested both conditional Poisson likelihood and Cox's partial likelihood models on data from studies using either SCCS or a matched cohort design. For the SCCS study, we fitted both parametric and semi-parametric models to model age effects, and described a simple way to apply the parametric and complex semi-parametric analysis to case series data.
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ABSTRACT: Post-marketing observational safety studies seek to identify potential rare events that may be associated with a licensed product but could not be detected in clinical studies. In vaccine post-marketing safety studies, it is often useful to compare incidence rate in a risk period immediately following vaccination vs. a longer self-comparison period for many (usually several hundreds or even thousands) medical events. It is recognized that when the risk and comparison event rates are the same, some of the tests for a difference between the two periods will reach significance by chance alone, and in this case one would expect the differences reaching significance to be equally likely to be favorable or unfavorable. However, when the risk and comparison periods differ in length and events are rare, all or most of the significant findings could favor the group with longer follow-up even after accounting for the differential length of follow-up. An investigation of this phenomenon confirms that unequal comparison periods can affect the direction of chance findings and shows that the magnitude depends on the expected number of events in the risk period.Statistics in Biopharmaceutical Research 01/2012; 4(1). DOI:10.1080/19466315.2011.633865 · 0.70 Impact Factor
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ABSTRACT: The Decade of Vaccines Collaboration and development of the Global Vaccine Action Plan provides a catalyst and unique opportunity for regulators worldwide to develop and propose a global regulatory science agenda for vaccines. Regulatory oversight is critical to allow access to vaccines that are safe, effective, and of assured quality. Methods used by regulators need to constantly evolve so that scientific and technological advances are applied to address challenges such as new products and technologies, and also to provide an increased understanding of benefits and risks of existing products. Regulatory science builds on high-quality basic research, and encompasses at least two broad categories. First, there is laboratory-based regulatory science. Illustrative examples include development of correlates of immunity; or correlates of safety; or of improved product characterization and potency assays. Included in such science would be tools to standardize assays used for regulatory purposes. Second, there is science to develop regulatory processes. Illustrative examples include adaptive clinical trial designs; or tools to analyze the benefit-risk decision-making process of regulators; or novel pharmacovigilance methodologies. Included in such science would be initiatives to standardize regulatory processes (e.g., definitions of terms for adverse events [AEs] following immunization). The aim of a global regulatory science agenda is to transform current national efforts, mainly by well-resourced regulatory agencies, into a coordinated action plan to support global immunization goals. This article provides examples of how regulatory science has, in the past, contributed to improved access to vaccines, and identifies gaps that could be addressed through a global regulatory science agenda. The article also identifies challenges to implementing a regulatory science agenda and proposes strategies and actions to fill these gaps. A global regulatory science agenda will enable regulators, academics, and other stakeholders to converge around transformative actions for innovation in the regulatory process to support global immunization goals.Vaccine 04/2013; 31:B163–B175. DOI:10.1016/j.vaccine.2012.10.117 · 3.49 Impact Factor