  The 21st Century Cures Act of 2016 includes a provision for the U.S. Food and
Drug Administration (FDA) to evaluate the potential use of real-world evidence
(RWE) to support new indications for use for previously approved drugs, and to
satisfy post-approval study requirements. Extracting reliable evidence from
real-world data (RWD) is often complicated by a lack of treatment
randomization, potential intercurrent events, and informative loss to follow
up. Targeted Learning (TL) is a sub-field of statistics that provides a
rigorous framework to help address these challenges. The TL Roadmap offers a
step-by-step guide to generating valid evidence and assessing its reliability.
Following these steps produces an extensive amount of information for assessing
whether the study provides reliable scientific evidence in support regulatory
decision making. This paper presents two case studies that illustrate the
utility of following the roadmap. We use targeted minimum loss-based estimation
combined with super learning to estimate causal effects. We also compared these
findings with those obtained from an unadjusted analysis, propensity score
matching, and inverse probability weighting. Non-parametric sensitivity
analyses illuminate how departures from (untestable) causal assumptions would
affect point estimates and confidence interval bounds that would impact the
substantive conclusion drawn from the study. TL's thorough approach to learning
from data provides transparency, allowing trust in RWE to be earned whenever it
is warranted.
