Evaluation of adaptive treatment strategies in an observational study where time-varying covariates are not monitored systematically
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In studies based on electronic health records (EHR), the frequency of covariate monitoring can vary by covariate type, across patients, and over time. This can lead to major challenges: first, the difference in monitoring protocols may invalidate the extrapolation of study results obtained in one population to the other, and second, monitoring can act as a time-varying confounder of the causal effect of a time-varying treatment on the outcomes of interest. This paper demonstrates how to account for non-systematic covariate monitoring when evaluating dynamic treatment interventions, and how to evaluate joint dynamic treatment-censoring and static monitoring interventions, in a real world, EHR-based, comparative effectiveness research (CER) study of patients with type II diabetes mellitus. First, we show that the effects of dynamic treatment-censoring regimes can be identified by including indicators of monitoring events in the adjustment set. Second, we demonstrate the poor performance of the standard inverse probability weighting (IPW) estimator of the effects of joint treatment-censoring-monitoring interventions, due to a large decrease in data support resulting in a large increase in standard errors and concerns over finite-sample bias from near-violations of the positivity assumption for the monitoring process. Finally, we detail an alternate IPW estimator of the effects of these interventions using the No Direct Effect assumption. We demonstrate that this estimator can result in improved efficiency but at the cost of increased bias concerns over structural near-violations of the positivity assumption for the treatment process. To conclude, this paper develops and illustrates new tools that researchers can exploit to appropriately account for non-systematic covariate monitoring in CER, and to ask new causal questions about the joint effects of treatment and monitoring interventions.
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