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.
"Another study testing the association between proton pump inhibitors and pneumonia used high-dimensional propensity scores to match exposed and control groups . Cohort, case-control, risk-interval, and self-controlled case-series designs are often used to assess vaccine safety . The VISION (Canada) researchers have used the linked data for self-controlled case-series analyses to investigate the association between vaccination and emergency room visits, hospital admissions, and deaths . "
[Show abstract][Hide abstract] ABSTRACT: Post-marketing drug surveillance for adverse drug events (ADEs) has typically relied on spontaneous reporting. Recently, regulatory agencies have turned their attention to more preemptive approaches that use existing data for surveillance. We conducted an environmental scan to identify active surveillance systems worldwide that use existing data for the detection of ADEs. We extracted data about the systems' structures, data, and functions. We synthesized the information across systems to identify common features of these systems. We identified nine active surveillance systems. Two systems are US based - the FDA Sentinel Initiative (including both the Mini-Sentinel Initiative and the Federal Partner Collaboration) and the Vaccine Safety Datalink (VSD); two are Canadian-the Canadian Network for Observational Drug Effect Studies (CNODES) and the Vaccine and Immunization Surveillance in Ontario (VISION); and two are European-the Exploring and Understanding Adverse Drug Reactions by Integrative Mining of Clinical Records and Biomedical Knowledge (EU-ADR) Alliance and the Vaccine Adverse Event Surveillance and Communication (VAESCO). Additionally, there is the Asian Pharmacoepidemiology Network (AsPEN) and the Shanghai Drug Monitoring and Evaluative System (SDMES). We identified two systems in the UK - the Vigilance and Risk Management of Medicines (VRMM) Division and the Drug Safety Research Unit (DSRU), an independent academic unit. These surveillance systems mostly use administrative claims or electronic medical records; most conduct pharmacovigilance on behalf of a regulatory agency. Either a common data model or a centralized model is used to access existing data. The systems have been built using national data alone or via partnership with other countries. However, active surveillance systems using existing data remain rare. North America and Europe have the most population coverage; with Asian countries making good advances.
Drug Safety 07/2014; 37(8). DOI:10.1007/s40264-014-0194-3 · 2.82 Impact Factor
"The self-controlled case series method was developed to study adverse events associated with vaccines where use of the vaccine is transient and the adverse event is acute. It has been compared previously to the cohort study design to evaluate vaccine safety . This method originates from cohort logic and the emphasis like a cohort study is on the relative incidence or relative hazard of an event . "
[Show abstract][Hide abstract] ABSTRACT: To compare the results of a new-user cohort study design and the self-controlled case series (SCCS) design using the risk of hospitalisation for pneumonia in those dispensed proton pump inhibitors compared to those unexposed as a case study.
The Australian Government Department of Veterans' Affairs administrative claims database was used. Exposure to proton pump inhibitors and hospitalisations for pneumonia were identified over a 4 year study period 01Jul2007 -30Jun2011. The same inclusion and exclusion criteria were applied to both studies, however, the SCCS study included subjects with a least one hospitalisation for pneumonia.
There were 105,467 subjects included in the cohort study and 6775 in the SCCS. Both studies showed an increased risk of hospitalisations for pneumonia in the three defined risk periods following initiation of proton pump inhibitors compared to baseline. With the highest risk in the first 1 to 7 days (Cohort RR, 3.24; 95% CI (2.50, 4.19): SCCS: RR, 3.07; 95% CI (2.69, 3.50)).
This study has shown that the self-controlled case series method produces similar risk estimates to a new-users cohort study design when applied to the association of proton pump inhibitors and pneumonia. Exposure to a proton pump inhibitor increases the likelihood of being admitted to hospital for pneumonia, with the risk highest in the first week of treatment.
BMC Medical Research Methodology 06/2013; 13(1):82. DOI:10.1186/1471-2288-13-82 · 2.27 Impact Factor
"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 "
[Show abstract][Hide abstract] 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.
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.