title: Future cases as present controls to adjust for exposure trend bias in case-only studies.
abstract: Self-matched case-only studies (such as the case-crossover or self-controlled case-series method) control by design for time-invariant confounders (measured or unmeasured), but they do not control for confounders that vary with time. A bidirectional case-crossover design can be used to adjust for exposure-time trends. In pharmacoepidemiology, however, illness often influences future use of medications, making a bidirectional design problematic. Suissa's case-time-control design combines a case-crossover and case-control design, and adjusts for exposure-trend bias in the cases' self-controlled odds ratio by dividing that ratio by the corresponding self-controlled odds ratio in a concurrent matched control group. However, if not well matched, the control group may reintroduce selection bias. We propose a "case-case-time-control" that involves crossover analyses in cases and future-case controls. This person-time sampling strategy improves matching by restricting controls to future cases. We evaluate the proposed study design through simulations and analysis of a theoretically null relationship using Veterans Administration (VA) data. Simulation studies show that the case-case-time-control can adjust for exposure trends while controlling for time-invariant confounders. Use of an inappropriate control group left case-time-control analyses biased by exposure-time trends. When analyzing the relationship between vitamin exposure and stroke, using data on 3192 patients in the VA system, a case-crossover odds ratio of 1.5 (95% confidence interval = 1.3-1.7) was reduced to 1.1 (0.9-1.3) when divided by the concurrent exposure trend odds ratio (1.4) in matched future cases. This applied example demonstrates how our approach can adjust for exposure trends observed across time axes.
authors.affiliation: Department of Community Health-Epidemiology, Brown University, Providence, RI, USA. Shirley_Wang@brown.edu
authors.forename: Shirley
authors.initials: S
authors.lastname: Wang
authors.forename: Crystal
authors.initials: C
authors.lastname: Linkletter
authors.forename: Malcolm
authors.initials: M
authors.lastname: Maclure
authors.forename: David
authors.initials: D
authors.lastname: Dore
authors.forename: Vincent
authors.initials: V
authors.lastname: Mor
authors.forename: Stephen
authors.initials: S
authors.lastname: Buka
authors.forename: Gregory A
authors.initials: GA
authors.lastname: Wellenius
mesh_terms: D015982:Bias; D016022:Case-Control Studies; D015986:Confounding Factors, Epidemiologic; D035061:Control Groups; D018592:Cross-Over Studies; D006801:Humans; D016015:Logistic Models; D017891:Pharmacoepidemiology / Q000379:methods*; D013997:Time Factors