Using Real-World Evidence in Health Technology Assessment and Regulatory Decisions

Definition of RWD and RWE

RWD are defined as data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources.1 RWE is defined as clinical evidence regarding the usage and potential benefits or risks of a medical product and is derived from RWD analyses.1

Recent Trends in RWE: Changing Landscapes in APAC

The use of RWE has been rapidly increasing from 2011 across healthcare product lifecycles, where it has been used to support regulatory decisions, develop clinical guidelines and inform reimbursement evaluations.2 This growing trend is also reflected in the rising proportion of Health Technology Assessment (HTA) records year-on-year that utilised RWE (6% in 2011, rising to 39% in 2021), suggesting further acceptance and utilisation of RWE across therapeutic and treatment areas by regulators and HTA bodies.3

Dr. Lu Ban (Associate Director, RWE, Evidera) emphasised that China might soon be ’leading the way’ within APAC in terms of using RWE to support drug development by actively encouraging the use of RWE in its regulatory and reimbursement decisions.4 In particular, local RWE inputs for health economic evaluations (e.g. cost effectiveness analyses and budget impact analyses) are preferred and would be a key part of their National Reimbursement Drug List negotiations.4 Therefore, it is not surprising to find that regulators in the APAC region are releasing more policies and guidance surrounding the use of RWE to support development of healthcare products today (Figure 1). Greater use of RWE is likely to grow across APAC as regulatory and HTA bodies improve their processes and recognise the utility of RWE.

Figure 1: Recent guidance surrounding RWE in APAC

RWE Recent Policies and Guidance5


  • In 2021, the Therapeutic Goods Administration (TGA) released the review on RWE and patient reported outcomes (PRO) (view here), establishing a central point of information on RWE and PROs in Australia.


  • In 2020, the National Medical Products Administration (NMPA) released the Guidelines for RWE to Support Drug Development and Review (Interim) (view here) to further guide and standardise the use of real-world evidence in China.
  • In 2021, the Guidelines for RWD Used to Generate RWE (Interim) (Chinese) (view here) was used to guide RWE data generation.


  • The Ministry of Health, Labour and Welfare (MHLW) and the Pharmaceuticals and Medical Devices Agency (PMDA) have been guiding RWE use since 2014 with the release of the Guidelines for the Conduct of Pharmacoepidemiological Studies in Drug Safety Assessment with Medical Information Databases (view here).
  • More recently, PMDA established its RWD working group (view here) in 2021 to clarify general principles on RWD utilisation and data reliability ranging from development through post-marketing surveillance of drugs and medical devices.


  • The Taiwan Food and Drug Administration (Taiwan FDA) have developed several RWE-related guidelines including the Basic Considerations for RWE to Support Drug Research and Development (Chinese) (view here) and Precautions for Using RWD/RWE as Technical Documents for Applying for Drug Review (Chinese) (view here).

There are a variety of data sources that can be used to collect RWD and these sources can be split into primary research data (e.g. clinical trials, patient-reported data, chart review) and secondary use of data (e.g. from electronic medical records, claims/insurance database, disease/product registry, social media platforms and mobile devices).4 Although not primarily used for research, these secondary data sources can contain a wealth of RWD to be used for RWE generation.4 Advantages of using RWE in treatment effectiveness research include improving generalisability of study findings to patients in the usual practice settings, facilitating the identification of populations with enhanced benefit and risk, assessing heterogenous treatment effects according to patient characteristics and evaluating personalised treatment strategies.6 My colleague, Jian Yi Choy, has touched on how adequate Asian representation in global clinical trials may not always be achievable and how RWE is accepted as a source of evidence for HTA decision-making in Singapore in a separate commentary here (‘How Can We Incorporate Locally Relevant and Culturally Appropriate Evidence for HTA Decision-Making in Asia?’).

However, there are several challenges in the use of RWD for RWE generation and these may vary across sources. For example, medical claims data may not capture all the data required to answer a question of interest. While these sources are likely to contain major events such as hospitalisation or mortality data, they may not have more nuanced records such as quality of life data or changes in symptom severity over time.1 Another limitation could be that these data were recorded in an unstructured manner (e.g. as free-text or lack of specific coding of diagnoses or treatment).1 Chun-Ting Yang (MSc, National Cheng Kung University, Taiwan) emphasised that the first step to develop reliable and robust RWE is to select a high quality RWD source by understanding the research question and being aware of the potential limitations around each source.6

The second consideration would be to develop robust study designs to limit confounding effects or possible selection bias present in RWD. Target Trial Emulation is a study design methodology shared by Felicitas Kuehne (Senior Scientist, Health Services Research and HTA, UMIT TIROL) to limit bias and generate quality RWE in situations when a randomised controlled trial (RCT) is not practical (e.g. too expensive, time-sensitive or due to ethical limitations) or not necessary.7 This approach involves designing a hypothetical randomised trial (i.e. the target trial) and then emulating the target trial using the available RWD. Although this concept could be implicit in many analyses, the target trial itself is rarely defined.8 A target trial should explicitly define the eligibility criteria, treatment strategies, assignment procedures, follow-up period, outcome, casual comparisons of interest (i.e. intention-to-treat effect, per-protocol effect) and the statistical analysis plan (Figure 2).8 Any data that do not meet the criteria should be regarded as lost to follow-up (i.e. censored).8 If the emulation is successful, the RWE should yield similar effect estimates as the target trial if the trial had been conducted.8

Figure 2: Target trial emulation methodology

Figure 5

Today, HTA bodies are increasingly tasked with issuing reimbursement or pricing recommendations on novel technologies in the absence of RCTs or limited RCT data,9 likely in a bid to improve patient access. When decisions must be made in the absence of randomised trials, it is essential to adopt a robust approach to the design and analysis of RWE. With increasing use of RWE in the APAC region, it would be crucial to build local expertise within each market to critically appraise RWD and generate quality RWE for use in HTA and treatment evaluation and these would likely benefit patient access in the long term.


  1. US Food & Drug Administration. Framework for FDA’s Real-World Evidence Program. 2018.
  2. Olivia, C; Sastry, C; Matthias, E et al. Real-world evidence: From activity to impact in healthcare decision making. 2017. Available here.
  3. Dima, S; Anke, vE; Eliana, T et al. A lifecycle approach to the use of RWE in HTA submissions and re-submissions, a decade’s experience 2022. Available here.
  4. Lu, B. ISPOR Asia-Pacific 2022: Real world Data and Real world Evidence in Support of Drug Development: Will China Soon be Leading the Way. 2022.
  5. Ellen, S; Fengyun, VH; Annetta, CB. Real-World Evidence Regulatory Landscape in Asia Pacific: Australia, China, Japan, South Korea, and Taiwan. 2022. Available here.
  6. Chun-Ting, Y. ISPOR Asia-Pacific 2022: How to estimate treatment effectiveness and safety with real world population data. 2022.
  7. Felicitas, K; Uwe, S; Isao, K. ISPOR Asia-Pacific 2022: Target Trial Emulation in Health Economics and Outcomes Research: Opportunities and Challenges. 2022.
  8. Hernán, MA; Robins, JM. Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available. Am J Epidemiol. 2016;183(8):758-764.
  9. Kent, S; Salcher-Konrad, M; Boccia, S et al. The use of nonrandomized evidence to estimate treatment effects in health technology assessment. Journal of Comparative Effectiveness Research. 2021;10(14):1035-1043.

If you would like any further information on the themes presented above, please do not hesitate to contact Andrew Lim, Analyst (LinkedIn). Andrew is an employee at Costello Medical. The views/opinions expressed are their own and do not necessarily reflect those of Costello Medical’s clients/affiliated partners.