Miguel A. Hernán

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In observational studies with exposures or treatments that vary over time, standard approaches for adjustment of confounding are biased when there exist time-dependent confounders that are also affected by previous treatment. This paper introduces marginal structural models, a new class of causal models that allow for improved adjustment of confounding in(More)
Standard methods for survival analysis, such as the time-dependent Cox model, may produce biased effect estimates when there exist time-dependent confounders that are themselves affected by previous treatment or exposure. Marginal structural models are a new class of causal models the parameters of which are estimated through(More)
The use of instrumental variable (IV) methods is attractive because, even in the presence of unmeasured confounding, such methods may consistently estimate the average causal effect of an exposure on an outcome. However, for this consistent estimation to be achieved, several strong conditions must hold. We review the definition of an instrumental variable,(More)
The method of inverse probability weighting (henceforth, weighting) can be used to adjust for measured confounding and selection bias under the four assumptions of consistency, exchangeability, positivity, and no misspecification of the model used to estimate weights. In recent years, several published estimates of the effect of time-varying exposures have(More)
BACKGROUND Methotrexate is the most frequent choice of disease-modifying antirheumatic therapy for rheumatoid arthritis. Although results of studies have shown the efficacy of such drugs, including methotrexate, on rheumatoid arthritis morbidity measures, their effect on mortality in patients with the disease remains unknown. Our aim was to prospectively(More)
The term "selection bias" encompasses various biases in epidemiology. We describe examples of selection bias in case-control studies (eg, inappropriate selection of controls) and cohort studies (eg, informative censoring). We argue that the causal structure underlying the bias in each example is essentially the same: conditioning on a common effect of 2(More)
Results of case-control studies and of a prospective investigation in men suggest that consumption of coffee could protect against the risk of Parkinson's disease, but the active constituent is not clear. To address the hypothesis that caffeine is protective against Parkinson's disease, we examined the relationship of coffee and caffeine consumption to the(More)
Common strategies to decide whether a variable is a confounder that should be adjusted for in the analysis rely mostly on statistical criteria. The authors present findings from the Slone Epidemiology Unit Birth Defects Study, 1992-1997, a case-control study on folic acid supplementation and risk of neural tube defects. When statistical strategies for(More)
We use causal graphs and a partly hypothetical example from the Physicians' Health Study to explain why a common standard method for quantifying direct effects (i.e. stratifying on the intermediate variable) may be flawed. Estimating direct effects without bias requires that two assumptions hold, namely the absence of unmeasured confounding for (1) exposure(More)
BACKGROUND AND PURPOSE To estimate the incidence and lifetime risk of motor neuron disease (MND) in a population-based sample in the United Kingdom. METHODS We identified new cases of MND during the period 1990-2005 in the General Practice Research Database, which includes clinical information from more than 3 million Britons enrolled with selected(More)