American journal of epidemiology
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Aspirin, nonsteroidal antiinflammatory drugs (NSAID), and acetaminophen are commonly used. Frequent use of analgesics has been associated with a higher risk of hearing loss. However, the association between duration of analgesic use and the risk of hearing loss is unclear. ⋯ Duration of aspirin use was not associated with hearing loss (for >6 years of use compared with <1 year, multivariable-adjusted relative risk = 1.01, 95% confidence interval: 0.97, 1.05; P for trend = 0.35). In this cohort of women, longer durations of NSAID and acetaminophen use were associated with slightly higher risks of hearing loss, but duration of aspirin use was not. Considering the high prevalence of analgesic use, this may be an important modifiable contributor to hearing loss.
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Estimation of causal effects using observational data continues to grow in popularity in the epidemiologic literature. While many applications of causal effect estimation use propensity score methods or G-computation, targeted maximum likelihood estimation (TMLE) is a well-established alternative method with desirable statistical properties. TMLE is a doubly robust maximum-likelihood-based approach that includes a secondary "targeting" step that optimizes the bias-variance tradeoff for the target parameter. ⋯ Our simulation study compares methods under parametric regression misspecification; our results highlight TMLE's property of double robustness. Additionally, we discuss best practices for TMLE implementation, particularly the use of ensembled machine learning algorithms. Our simulation study demonstrates all methods using super learning, highlighting that incorporation of machine learning may outperform parametric regression in observational data settings.