This paper describes a targeted maximum likelihood estimator (TMLE) for the parameters of
longitudinal static and dynamic marginal structural models. We consider a longitudinal data …

Cluster randomized trials (CRTs) randomly assign an intervention to groups of individuals
(eg, clinics or communities) and measure outcomes on individuals in those groups. While …

In this issue, Naimi et al.(Am J Epidemiol. 2023; 192 (9): 1536–1544) discuss a critical topic
in public health and beyond: obtaining valid statistical inference when using machine …

Background: We sought to investigate the effect of public masking mandates in US states on
COVID-19 at the national level in Fall 2020. Specifically, we aimed to evaluate how the …

Inverse probability weighting (IPW) and targeted maximum likelihood estimation (TMLE) are
methodologies that can adjust for confounding and selection bias and are often used for …

Longitudinal targeted maximum likelihood estimation (LTMLE) has very rarely been used to
estimate dynamic treatment effects in the context of time‐dependent confounding affected by …

In cluster randomized trials, the study units usually are not a simple random sample from
some clearly defined target population. Instead, the target population tends to be …

Background: Targeted maximum likelihood estimation has been proposed for estimating
marginal causal effects, and is robust to misspecification of either the treatment or outcome …

Background: Population-level estimates of disease prevalence and control are needed to
assess prevention and treatment strategies. However, available data often suffer from …

Data-adaptive methods have been proposed to estimate nuisance parameters when using
doubly robust semiparametric methods for estimating marginal causal effects. However, in …