Spline-based self-controlled case series method

The self-controlled case series (SCCS) method is an alternative to study designs such as cohort and case control methods and is used to investigate potential associations between the timing of vaccine or other drug exposures and adverse events. It requires information only on cases, individuals who have experienced the adverse event at least once, and automatically controls all fixed confounding variables that could modify the true association between exposure and adverse event. Time-varying confounders such as age, on the other hand, are not automatically controlled and must be allowed for explicitly. The original SCCS method used step functions to represent risk periods (windows of exposed time) and age effects. Hence, exposure risk periods and/or age groups have to be prespecified a priori, but a poor choice of group boundaries may lead to biased estimates. In this paper, we propose a nonparametric SCCS method in which both age and exposure effects are represented by spline functions at the same time. To avoid a numerical integration of the product of these two spline functions in the likelihood function of the SCCS method, we defined the first, second, and third integrals of I-splines based on the definition of integrals of M-splines. Simulation studies showed that the new method performs well. This new method is applied to data on pediatric vaccines

Self-controlled case series with multiple event types

Self-controlled case series methods for events that may be classified as one of several types are described. When the event is non-recurrent, the different types correspond to competing risks. It is shown that, under circumstances that are likely to arise in practical applications, the SCCS multi-type likelihood reduces to the product of the type-specific likelihoods. For recurrent events, this applies whether or not the marginal type-specific counts are dependent. As for the standard SCCS method, a rare disease assumption is required for non-recurrent events. Several forms of this assumption are investigated by simulation. The methods are applied to data on MMR vaccine and convulsions (febrile and non-febrile), and to data on thiazolidinediones and fractures (at different sites)

Investigating the assumptions of the self-controlled case series method.

We describe some simple techniques for investigating two key assumptions of the self-controlled case series (SCCS) method, namely that events do not influence subsequent exposures, and that events do not influence the length of observation periods. For each assumption we propose some simple tests based on the standard SCCS model, along with associated graphical displays. The methods also enable the user to investigate the robustness of the results obtained using the standard SCCS model to failure of assumptions. The proposed methods are investigated by simulations, and applied to data on measles, mumps and rubella vaccine, and antipsychotics

Proton pump inhibitors and the risk of pneumonia: A comparison of cohort and self-controlled case series designs

Background: 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. Methods: 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 01 Jul 2007 -30 Jun 2011. The same inclusion and exclusion criteria were applied to both studies, however, the SCCS study included subjects with a least one hospitalisation for pneumonia. Results: 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)). Conclusions: 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.Emmae N Ramsay, Nicole L Pratt, Philip Ryan and Elizabeth E Roughea

Self-controlled case series methods: an alternative to standard epidemiological study designs

The self controlled case series (SCCS) method is an epidemiological study design for which individuals act as their own control—ie, comparisons are made within individuals. Hence, only individuals who have experienced an event are included and all time invariant confounding is eliminated. The temporal association between a transient exposure and an event is estimated. SCCS was originally developed for evaluation of vaccine safety, but has since been applied in a range of settings where exact information on the size of the population at risk is lacking or identification of an appropriate comparison group is difficult—eg, for studies of adverse effects of drug treatments. We provide an overview of the SCCS method, with examples of its use, discuss limitations, assumptions, and potential biases that can arise where assumptions are not met, and provide solutions and examples of good practice

Four different study designs to evaluate vaccine safety were equally validated with contrasting limitations

OBJECTIVE: 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. STUDY DESIGN AND METHODS: 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. RESULTS: 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\u27s 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. CONCLUSIONS: 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

Smooth Risk Functions for Self-Controlled Case Series Models

The self-controlled case series (SCCS) method is commonly used to investigate associations between vaccine exposures and adverse events (side effects). It is an alternative to cohort and case control study designs. It requires information only on cases, individuals who have experienced the adverse event at least once, and automatically controls all fixed confounders that could modify the true association between exposure and adverse event. However, time-varying confounders (age, season) are not automatically controlled. The SCCS method has parametric and semi-parametric versions in terms of controlling the age effect. The parametric method uses piecewise constant functions with a priori chosen age groups and the semi-parametric method leaves the age effect unspecified. Mis-specification of age groups in the parametric version may lead to biased estimates of the exposure effect, and the semi-parametric approach runs into computational problems when the sample size is moderately large. Moreover, both versions of SCCS represent the time-varying exposures using step functions with pre-determined cut-points. A less prescriptive approach may be beneficial when the shape of the relative risk function associated with exposure is not known a priori, especially when exposure effects can be long-lasting. This thesis focuses on extending the SCCS method to avoid the aforementioned limitations by modelling the age and exposure effects using flexible smooth functions. Specifically, we used penalised regression splines based on cubic M-splines, which are piecewise polynomials of degree 3. We developed three new extensions: a method that represents only the age effect with splines, a method that uses splines to model only the exposure effect and a non-parametric SCCS method that represents both effects by splines. Simulation studies showed that these new methods outperformed the parametric and semi-parametric methods. The new methods are illustrated using large data sets. Review of SCCS vaccine studies and directions on how to use the method are also given

Flexible modelling of vaccine effect in self-controlled case series models

The self-controlled case-series method (SCCS), commonly used to investigate the safety of vaccines, requires information on cases only and automatically controls all age-independent multiplicative confounders, while allowing for an age dependent baseline incidence. Currently the SCCS method represents the time-varying exposures using step functions with pre-determined cut-points. A less prescriptive approach may be beneficial when the shape of the relative risk function associated with exposure is not known a priori, especially when exposure effects can be long-lasting. We therefore propose to model exposure effects using flexible smooth functions. Specifically, we used a linear combination of cubic M-splines which, in addition to giving plausible shapes, avoids the integral in the log-likelihood function of the SCCS model. The methods, though developed specifically for vaccines, are applicable more widely. Simulations showed that the new approach generally performs better than the step function method. We applied the new method to two data sets, on febrile convulsion and exposure to MMR vaccine, and on fractures and thiazolidinedione use

A modified self‐controlled case series method for event‐dependent exposures and high event‐related mortality, with application to COVID‐19 vaccine safety

We propose a modified self‐controlled case series (SCCS) method to handle both event‐dependent exposures and high event‐related mortality. This development is motivated by an epidemiological study undertaken in France to quantify potential risks of cardiovascular events associated with COVID‐19 vaccines. Event‐dependence of vaccinations, and high event‐related mortality, are likely to arise in other SCCS studies of COVID‐19 vaccine safety. Using this case study and simulations to broaden its scope, we explore these features and the biases they may generate, implement the modified SCCS model, illustrate some of the properties of this model, and develop a new test for presence of a dose effect. The model we propose has wider application, notably when the event of interest is death

Self-controlled methods for postmarketing drug safety surveillance in large-scale longitudinal data

A primary objective in postmarketing drug safety surveillance is to ascertain the relationship between time-varying drug exposures and adverse events (AEs) related to health outcomes. Surveillance can be based on longitudinal observational databases (LODs), which contain time-stamped patient-level medical information including periods of drug exposure and dates of diagnoses. Due to its desirable properties, we focus on the self-controlled case series (SCCS) method for analysis in this context. SCCS implicitly controls for fixed multiplicative baseline covariates since each individual acts as their own control. In addition, only exposed cases are required for the analysis, which is computationally advantageous. In the first part of this work we present how the simple SCCS model can be applied to the surveillance problem, and compare the results of simple SCCS to those of existing methods. Many current surveillance methods are based on marginal associations between drug exposures and AEs. Such analyses ignore confounding drugs and interactions and have the potential to give misleading results. In order to avoid these difficulties, it is desirable for an analysis strategy to incorporate large numbers of time-varying potential confounders such as other drugs. In the second part of this work we propose the Bayesian multiple SCCS approach, which deals with high dimensionality and can provide a sparse solution via a Laplacian prior. We present details of the model and optimization procedure, as well as results of empirical investigations. SCCS is based on a conditional Poisson regression model, which assumes that events at different time points are conditionally independent given the covariate process. This requirement is problematic when the occurrence of an event can alter the future event risk. In a clinical setting, for example, patients who have a first myocardial infarction (MI) may be at higher subsequent risk for a second. In the third part of this work we propose the positive dependence self-controlled case series (PD-SCCS) method: a generalization of SCCS that allows the occurrence of an event to increase the future event risk, yet maintains the advantages of the original by controlling for fixed baseline covariates and relying solely on data from cases. We develop the model and compare the results of PD-SCCS and SCCS on example drug-AE pairs