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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.