Speaker
Description
Non-experimental data, such as electronic medical records, are often used in causal inference to estimate the effect of an exposure on an outcome of interest. However, this type of data can be affected by potential sources of bias in causal analyses. For example, these data do not come from a study design that ensures a balance of patient characteristics between exposure groups, a problem known as confounding. Patients are also observed irregularly over time, which can lead to selection bias. Methods have recently been proposed to address these challenges, but they have mostly focused on acute treatment effects, which occur rapidly and are short-term (Pullenayegum et al., 2023; Coulombe and Yang, 2024).
In this presentation, we propose a methodology, the Inverse Density Exposure and Monitoring (IDEM) estimator, to consistently estimate the causal effect of a cumulative exposure on an outcome measured repeatedly in non-experimental longitudinal studies. Under certain assumptions, the proposed estimator accounts for delayed treatment effects and allows for causal estimation in the presence of time-fixed confounding and irregular observation times of the outcome. To achieve this, the Inverse Density of Treatment (IDT) and Inverse Intensity of Visits (IIV) weights are combined using generalized estimating equations to derive the IDEM estimator. Using properties of two-step estimators, we present results on its asymptotic distribution, which is valid under a set of conditions.
In a simulation study with four scenarios in which the exposure and visit models vary, the causal estimates obtained with IDEM were compared with those obtained using the ordinary least squares (OLS) estimator and two other simply weighted estimators, which we refer to as IDT and IIV. Across all scenarios, IDEM showed the smallest bias. The four estimators were then applied to the Phenobarb dataset (Grasela and Donn, 1985) to estimate the causal effect of cumulative phenobarbital administration on its irregularly measured blood concentrations in newborns, with weight and Apgar score available at baseline.
References:
Pullenayegum, E. M., Birken, C., Maguire, J., and TARGet Kids! Collaboration. (2023). Causal inference with longitudinal data subject to irregular assessment times. Statistics in Medicine, 42(14), 2361–2393.
Coulombe, J., and Yang, S. (2024). Multiply robust estimation of marginal structural models in observational studies subject to covariate-driven observations. Biometrics, 80(3).
Grasela Jr, T. H., and Donn, S. M. (1985). Neonatal population pharmacokinetics of phenobarbital derived from routine clinical data. Developmental Pharmacology and Therapeutics, 8(6), 374–383.
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