• Publications
  • Influence
Marginal Structural Models and Causal Inference in Epidemiology
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 alsoExpand
ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions
Risk of Bias In Non-randomised Studies - of Interventions is developed, a new tool for evaluating risk of bias in estimates of the comparative effectiveness of interventions from studies that did not use randomisation to allocate units or clusters of individuals to comparison groups. Expand
Constructing inverse probability weights for marginal structural models.
  • S. Cole, M. Hernán
  • Computer Science, Medicine
  • American journal of epidemiology
  • 15 September 2008
The authors describe possible tradeoffs that an epidemiologist may encounter when attempting to make inferences and weight truncation is presented as an informal and easily implemented method to deal with these tradeoffs. Expand
Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men.
The marginal structural Cox proportional hazards model is described and used to estimate the causal effect of zidovudine on the survival of human immunodeficiency virus-positive men participating in the Multicenter AIDS Cohort Study. Expand
RoB 2: a revised tool for assessing risk of bias in randomised trials
The Cochrane risk-of-bias tool has been updated to respond to developments in understanding how bias arises in randomised trials, and to address user feedback on and limitations of the original tool. Expand
A Structural Approach to Selection Bias
This work argues that the causal structure underlying the bias in each example is essentially the same: conditioning on a common effect of 2 variables, one of which is either exposure or a cause of exposure and the other is either the outcome or acause of the outcome. Expand
Prevalence of SARS-CoV-2 in Spain (ENE-COVID): a nationwide, population-based seroepidemiological study
The majority of the Spanish population is seronegative to SARS-CoV-2 infection, even in hotspot areas, and results emphasise the need for maintaining public health measures to avoid a new epidemic wave. Expand
Instruments for Causal Inference: An Epidemiologist's Dream?
The definition of an instrumental variable is reviewed, the conditions required to obtain consistent estimates of causal effects are described, and their implications are explored in the context of a recent application of the instrumental variables approach. Expand
Estimating causal effects from epidemiological data
This article reviews a condition that permits the estimation of causal effects from observational data, and two methods—standardisation and inverse probability weighting—to estimate population causal effects under that condition. Expand
Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology.
Findings are presented from the Slone Epidemiology Unit Birth Defects Study, 1992-1997, a case-control study on folic acid supplementation and risk of neural tube defects, which suggests that the crude odds ratio should be used because the adjusted odds ratio is invalid. Expand