Implementing TMLE in the presence of a continuous outcome

Abstract

In a real-world observational data analysis setting, guessing the true model specification can be difficult for an analyst. Unfortunately, correct model specification is a core assumption for treatment effect estimation methods such as propensity score methods, G-computation, and regression techniques. Targeted maximum likelihood estimation (TMLE) is an alternative method that allows the use of data-adaptive and machine learning algorithms for model fitting. TMLE therefore does not require strict assumptions about the model specification but preserves the validity of the inference. Multiple studies have shown that TMLE outperforms other methods in certain real-world settings, making it a useful and potentially superior algorithm for causal inference. However, there is a lack of accessible resources for practitioners to understand the implementation. Hence the TMLE framework is the least-used method by practitioners in epidemiology literature. Recently a few accessible articles have been published, but they focus only on binary outcomes and demonstrations are done mainly with simulated data. This paper aims to fill the gap in the literature by providing a step-by-step TMLE implementation guide for a continuous outcome, using an openly accessible clinical dataset.