Estimation using all available covariate information versus a fixed look-back window for dichotomous covariates

The validity of epidemiologic findings depends on the completeness and accuracy with which covariates are measured and taken into account. For some variables such as comorbid conditions or concurrent therapies, it is often not possible to distinguish between missing data and absence of the condition. Operationally, missing information for such variables is usually assumed to indicate that the condition is not present.1 In cohort studies conducted within healthcare utilization databases, the sensitivity with which such variables are detected depends in large part on the duration of time over which each subject had been observed before follow-up began.2 Because subjects will vary in terms of the duration of available baseline information, the investigator is faced with a choice: 1) utilize covariate information based on all available baseline data for each subject, 2) utilize covariate information based on a fixed baseline window of time that is shared by all subjects (often chosen as 6 months or 1 year). We term these options the “all-available” and “fixed window” approaches. It can be assumed that under the all-available paradigm, confounders will be identified with equal or greater sensitivity than under a fixed window approach, which should serve to reduce residual confounding.3-4 On the other hand, the duration of available baseline data may be associated with subject covariates, subsequent exposure and outcome (each either directly or mediated through other unmeasured confounders), so that the all-available approach could introduce bias through differential confounder ascertainment. Consider the following motivating example. Investigators are interested in estimating the comparative effectiveness of new use of anti-platelet agent A versus a standard anti-platelet agent B on 30-day stroke risk following a transient ischemic attack among Medicaid beneficiaries in secondary claims data. Diabetes is an important potential confounder. The source cohort comprises patients who have a claim involving a diagnosis code for transient ischemic attack, and who subsequently filled a prescription for anti-platelet medication A or B (index fill). Potential subjects are excluded if they demonstrate claims for anti-platelet agents before the index fill, or if they do not have 6 months of available data before the index fill over which to document no prior exposure. Some patients meeting entry criteria may have the minimum requisite 6 months of baseline data, whereas others may have many years of baseline data. The duration of available data likely relates to observable characteristics (eg, socioeconomic status, disability) as well as unobservable ones (eg, frailty) that themselves are associated with diabetic status, choice of anti-platelet agent and risk of subsequent stroke. To define diabetes, the investigator must determine whether to identify claims suggesting diabetes using all available data for each patient, or to restrict to data from the collectively shared 6-month baseline period. The latter approach results in uniform observation periods for study participants, but that comes at the cost of ignoring some data available for some patients. We sought to explore the tradeoffs inherent in this choice through a simulation study that compared bias reduction under these two approaches, and to determine the conditions under which one approach may be preferable to the other. Specifically, we considered the following circumstance: 1) the effect of interest was that of a dichotomous exposure on a dichotomous outcome; 2) the observed exposure—outcome association was confounded by a single observed dichotomous covariate; 3) all study subjects included in the analysis had available data for at least the 6-month period preceding exposure, and variable amounts of lead-up time before that period. We then compared the two approaches in the presence of an additional unmeasured covariate that was associated with exposure, outcome, duration of historical follow up and frequency of medical contact. Of note, we have considered confounding based on a chronic disease (or analogous) conception; results do not pertain to covariates that confound on a temporary or transient basis (eg, acute conditions).