Generating fit-for-purpose real-world evidence in Asia: How far are we from closing the gaps?

Abstract

Evidence generated by randomized controlled trials (RCTs) does not often represent the patient journey and clinical outcomes in the real world due to limited external validity or generalizability. Studies based on real-world data are intended to generalize results to the broader population; however, if the influence of external factors or confounders is not effectively managed, the cause-and-effect relationship and internal validity may be challenged, resulting in flawed results. The collection of quality real-world evidence (RWE) is crucial in Asia as there is often an underrepresentation of Asian populations in RCTs. In addition, few countries in Asia are catching up with the Western world in issuing practical foundational principles and guidance for conducting and adopting evidence for regulatory and reimbursement decisions. However, privacy and data protection laws are generally lagging behind technological developments in electronic medical records. While leveraging RWE in clinical and regulatory decision-making holds excellent potential, collective efforts across industry, governments, and research institutions are required for generating standardized practices and building capabilities for developing fit-for-purpose RWE in Asia.

Keywords: Asia; decision-making; health technology assessment; randomized controlled trials; real-world data; real-world evidence.

Figure 1

Strengths and weaknesses of RCTs versus RWD studies. RWE complements RCTs by generalizing the results to a broader patient population. However, confounding due to lack of randomization often limits the potential of RWD. Confounding can be minimized either through careful study design or through data analysis. These could include a comparison or control group, restricting the selection of patients in the treatment group to those who are new users (of a particular treatment)/only to adherent patients and carrying out appropriate and adequate matching (to create comparable cohorts in the real world, after observing how a doctor decides which drug for which patient) and sensitivity analysis based on the observed confounders. RWD = Real-world data, RCTs = Randomized controlled trials