The case test-negative design for studies of the effectiveness of influenza vaccine☆
Introduction
A modification of the traditional case–control study design often termed the “test-negative case–control” design has become popular for post-licensure observational studies of the effectiveness of vaccines for influenza [1], [2], [3], [4], [5], [6], [7], [8], [9] and rotavirus [10], [11], [12], [13], [14], [15]. In this design, patients seeking medical care for a defined clinical condition (e.g., acute respiratory illness) are tested for a specific viral infection (e.g., influenza) by using a highly sensitive and specific laboratory test, usually a polymerase chain reaction (PCR) assay. Those testing positive are cases. Controls are patients meeting the same enrollment criteria but who test negative for infection. Vaccine effectiveness (VE) is calculated in the usual manner for case-control studies, i.e., VE = (1 − vaccination odds ratio) x 100%. As the marginal ratio of cases to non-cases or “controls” is not specified or even knowable during enrollment, which occurs prior to testing, this design is clearly not a traditional case–control study. For clarity, we will refer to this design as a “case test-negative” design hereafter. The recent popularity of this design arises primarily from ease of implementation, since both cases and controls are recruited in one process. Another advantage is the likelihood that enrollment in this manner reduces the risk of particular type confounding: if vaccine receipt is associated with a greater likelihood of seeking health care for mild to moderate illnesses, then the design should adjust implicitly for this confounding, which otherwise would bias VE estimates downwards [3], [9]. Although the case test-negative approach is appealing both intuitively and practically, its assumptions have not been stated explicitly nor have the statistical properties of the VE estimator been derived formally. Using an observational study of influenza VE as an example, we derive the mathematical foundation of the case test-negative design and determine the conditions under which VE estimates derived with it are valid and unbiased.
Section snippets
Methods
We examined the properties of the case test-negative design in two ways. First, we derived mathematical expressions for VE estimates, starting with the most constraining assumptions and then relaxing them to address a broader range of situations. Second, we used simulation methods to examine the validity of the VE estimates and to illustrat their performance.
Results
Based on a derivation of the parameters obtained from a hypothetical case test-negative study of the effectiveness of influenza vaccination, we found that under our base case assumptions the VE estimate was valid and unbiased (Eq. (2)). The estimate was also valid in the context of time-varying incidence rates (Eq. (A.3), Appendix 1). However, if health care-seeking behavior is not only driven by the attributes that also drive vaccination uptake, but also by disease severity and if disease
Discussion
We found that, under increasingly general assumptions, estimates for the effectiveness of vaccine against symptomatic influenza, obtained from case test-negative studies, are valid and unbiased. Thus, standard statistical methods can be used to analyze data from such studies, and VE can be calculated directly from the estimated exposure odds ratio, VE = (1 − odds ratio) x 100%, as done when analyzing case–control study data.
Our derivations and the simulations demonstrated that the case test-negative
Acknowledgements
The authors wish to thank Michael Jackson, Paul Gargiullo and Sue Reynolds for their helpful comments.
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The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention.