Identifying optimal risk windows for self-controlled case series studies of vaccine safety

Institute for Health Research, Kaiser Permanente Colorado, Denver, CO, U.S.A.
Statistics in Medicine (Impact Factor: 1.83). 03/2011; 30(7):742-52. DOI: 10.1002/sim.4125
Source: PubMed


In vaccine safety studies, subjects are considered at increased risk for adverse events for a period of time after vaccination known as risk window. To our knowledge, risk windows for vaccine safety studies have tended to be pre-defined and not to use information from the current study. Inaccurate specification of the risk window can result in either including the true control period in the risk window or including some of the risk window in the control period, which can introduce bias. We propose a data-based approach for identifying the optimal risk windows for self-controlled case series studies of vaccine safety. The approach involves fitting conditional Poisson regression models to obtain incidence rate ratio estimates for different risk window lengths. For a specified risk window length (L), the average time at risk, T(L), is calculated. When the specified risk window is shorter than the true, the incidence rate ratio decreases with 1/T(L) increasing but there is no explicit relationship. When the specified risk window is longer than the true, the incidence rate ratio increases linearly with 1/T(L) increasing. Theoretically, the risk window with the maximum incidence ratio is the optimal risk window. Because of sparse data problem, we recommend using both the maximum incidence rate ratio and the linear relationship when the specified risk window is longer than the true to identify the optimal risk windows. Both simulation studies and vaccine safety data applications show that our proposed approach is effective in identifying medium and long-risk windows.

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    ABSTRACT: Conditional Poisson models have been used to analyze vaccine safety data from self-controlled case series (SCCS) design. In this paper, we derived the likelihood function of fixed effects models in analyzing SCCS data and showed that the likelihoods from fixed effects models and conditional Poisson models were proportional. Thus, the maximum likelihood estimates (MLEs) of time-varying variables including vaccination effect from fixed effects model and conditional Poisson model were equal. We performed a simulation study to compare empirical type I errors, means and standard errors of vaccination effect coefficient, and empirical powers among conditional Poisson models, fixed effects models, and generalized estimating equations (GEE), which has been commonly used for analyzing longitudinal data. Simulation study showed that both fixed effect models and conditional Poisson models generated the same estimates and standard errors for time-varying variables while GEE approach produced different results for some data sets. We also analyzed SCCS data from a vaccine safety study examining the association between measles mumps-rubella (MMR) vaccination and idiopathic thrombocytopenic purpura (ITP). In analyzing MMR-ITP data, likelihood-based statistical tests were employed to test the impact of time-invariant variable on vaccination effect. In addition a complex semi-parametric model was fitted by simply treating unique event days as indicator variables in the fixed effects model. We conclude that theoretically fixed effects models provide identical MLEs as conditional Poisson models. Because fixed effect models are likelihood based, they have potentials to address methodological issues in vaccine safety studies such as how to identify optimal risk window and how to analyze SCCS data with misclassification of adverse events.
    Full-text · Article · Apr 2012
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    No preview · Article · Apr 2012 · Vaccine
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    No preview · Article · Jun 2012 · Vaccine
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