Developing a Targeted Learning-Based Statistical Analysis Plan
Susan Gruber, Hana Lee, Rachael V. Phillips, Martin Ho, Mark van der Laan
Abstract
The Targeted Learning estimation roadmap provides a rigorous framework for developing a statistical analysis plan (SAP) for synthesizing evidence from randomized controlled trials and real world data. Learning from these data necessitates acknowledging potential sources of bias, and specifying appropriate mitigation strategies. This article demonstrates how Targeted Learning informs different aspects of SAP development, including explicit representation of intercurrent events. Guiding principles are to (a) define the target parameter of interest separately from the model or estimation procedure; and (b) use targeted minimum loss-based estimation (TMLE) and super learning for causal inference. These flexible methodologies can be entirely pre-specified while remaining data adaptive; and (c) carry out a nonparametric sensitivity analysis to evaluate the plausibility of a causal interpretation of the estimated treatment effect, and its stability with respect to violations of underlying casual assumptions. The roadmap promotes the principles and practices set forth in the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use Guideline. An annotated SAP, checklists for pre-specifying the TMLE and super learning procedures, and sample R code are provided as supplementary materials.