From the Guidelines
The most recent and relevant reference for targeted maximum likelihood estimation (TMLE) is not directly provided in the given evidence, but based on the context, a key reference is the work by Mark van der Laan and Daniel Rubin, who introduced this methodology. TMLE is a doubly robust estimation method used in causal inference and statistical analysis that combines machine learning with efficient estimation theory 1. The method addresses the limitations of traditional statistical approaches by targeting the parameter of interest while minimizing bias.
- TMLE works by first creating an initial outcome prediction model, then updating this model in a way that optimizes the estimation of the specific causal parameter while maintaining consistency.
- This approach is particularly valuable in observational studies where confounding is a concern, as it can adjust for measured confounders while maintaining efficiency.
- Although the provided evidence does not directly discuss TMLE, the concept of targeted interventions and the importance of accurate prediction models are highlighted in the context of economic evaluation of targeted cancer interventions 1 and Bayesian analysis for clinical trial interpretation 1.
- The use of Bayesian analysis, as discussed in 1, can complement TMLE by providing a framework for updating prior knowledge with new evidence, which is essential in the context of targeted maximum likelihood estimation.
- However, for a direct reference to TMLE, one would need to look beyond the provided evidence, focusing on studies that specifically introduce and apply TMLE, such as those by Mark van der Laan and collaborators.
From the Research
Targeted Maximum Likelihood Estimation References
- The study 2 provides a reference for targeted maximum likelihood estimation, specifically in the context of causal inference in survival and competing risks analysis.
- Another study 3 applies targeted maximum likelihood estimation in public health and epidemiological studies, highlighting its benefits and increasing adoption in various disciplines.
- The paper 4 describes a targeted maximum likelihood estimator for longitudinal static and dynamic marginal structural working models, demonstrating its practical advantages over other estimators.
- The article 5 illustrates the application of targeted maximum likelihood estimation to the meta-analysis of safety data, reducing bias in causal effect estimates and comparing performance with other estimators.
- The research 6 introduces collaborative double robust targeted maximum likelihood estimation, providing a template for targeted efficient and robust loss-based learning of a particular target feature of the probability distribution of the data.
Key Features of Targeted Maximum Likelihood Estimation
- Targeted maximum likelihood estimation provides a general methodology for estimation of causal parameters in presence of high-dimensional nuisance parameters 2.
- It combines data-adaptive nuisance parameter estimation with semiparametric efficiency and rigorous statistical inference obtained via a targeted update step 2.
- The approach is double-robust, meaning that it is consistent and asymptotically normally distributed when either the nuisance parameter or the relevant factor of the likelihood is correctly specified 6.
- Targeted maximum likelihood estimation can be used in various contexts, including survival and competing risks analysis 2, public health and epidemiological studies 3, and meta-analysis of safety data 5.