Original research case–control studies are the most frequently chosen epide- miological designs in assessing effects of drug treatments. However, these designs are usually subject to selection bias and measurement bias, both of which may distort the estimates of drug treatment effects, and even flip the direction of the effects. Many forms of selection biases have been identified in studies using RCD, 19 and indication bias is among the most common selection biases that warrants strong attention. 20 Another common bias is time-dependent bias, such as immortal time bias and time-lag bias, which may derive from a wrongly defined timeframe of the exposure group (eg, a waiting time between initiation of follow-up and treatment inappropriately assigned to the exposed group). 21 There is an extensive litera- ture discussing the different forms of selection biases 22–24 and interested researchers may find them helpful in designing their studies. In general, new user design, treatment-naïve new user design or active comparator are often desirable strategies to resolve some of these important biases. 25 26 New user design align exposure and comparator groups at the same initiation time, while active comparators can restrict participants with the same indications. DEVELOPING RESEARCH DATASET FROM THE RCD On the completion of study planning and design, a research dataset should be established. As RCD are collected for admin- istrative purposes, they are not usable for observational studies in their original forms. Therefore, it is necessary to transform the data into a uniformed and structured format. The transfor- mation of RCD into a research dataset may include multiple running steps, such as data linkage, structurisation of the free texts and variable labelling. Additional data cleaning is also an essential part of building a research dataset. This process often includes establishing vari- able dictionaries, processing special data (ie, extreme values, outliers, missing values and contradictory data). Notably, raw data, detailed cleaning rules, and data processing procedures should be kept to ensure the transparency of the study. A specific question of using RCD is to how to frame oper- ational phenotyping algorithms—computer-executable defini- tions that use diagnosis codes, clinical markers, or demographic characteristics—for identifying research variables (including exposure, outcome and covariates). 27 The validity or reliability of these codes or algorithms for research variables are critical. STATISTICAL ANALYSIS Statistical analysis in observational studies should be mindful of controlling for confounding factors. Confounding is very common in observational studies, and many types of confounding may be present in the use of RCD for assessing drug treatment effects, for example, time-dependent confounding and unmea- sured confounding. These issues may often distort the estimated treatment effects. 19 20 28–30 Various methods have been developed to address confounding issues such as multivariable models, propensity score analysis and instrumental variable analysis. 31–33 Guidance is available for the use of sophisticated statistical methods in the analysis of RCD. 34 Given these methodological challenges in observational studies, both regulatory decision-makers and academic experts are committed to developing methodological guidelines about observational studies using RCD. 13 15 25 35–39 It is always recom- mended that researchers should develop a research protocol for any study. 25
REPORTING Complete and transparent reporting is essential for evaluating the reliability and validity of study findings. However, the reporting quality of observational studies using RCD is often suboptimal, 40 especially in the elaborations of research questions, type of data sources, time frames, study designs, and statistical models. 40 41 Several guidelines have been developed to enhance reporting, such as Strengthening the Reporting of Observational Studies in Epidemiology (STROBE), 42 the Reporting of studies Conducted using Observational Routinely-collected Data (RECORD) state- ment, 43 and its extension for pharmacoepidemiology studies (RECORD-PE). 44 Interested researchers should always consult these guidelines for reporting of their studies. CONCLUSIONS In this paper, we provide a snapshot of the concepts and key methodological issues for RWE. For researchers, real-world data have provided important data sources to address a variety of questions. Nevertheless, important methodological chal- lenges may be present, and careful planning, implementing and reporting of such studies are highly desirable. The users of RWE should also be cautious when interpreting the findings from such studies and should always be aware of the potential methodolog- ical pitfalls.
What this paper adds
What is already known on this subject ► The release of the 21st Century Cure Act in the USA has accelerated the interest in real-world evidence (RWE), especially among healthcare researchers and policymakers. ► Misunderstanding and lack of methodological know-how is common about RWE. What this study adds ► This paper summarises the conceptual framework of RWE and proposes a research flow to assist in the understanding and implementation of an RWE study. ► This paper provides an overview of pitfalls inherent with RWE, especially those observational studies using routinely collected healthcare data, and offers reference to guidance documents about reporting. Contributors Conceptualisation: XS. Writing – original draft: ML. Writing – review and editing: XS, ML, YQ, WW. XS is the guarantor who takes responsibility for the overall content. Funding This research was supported by Sichuan Youth Science and Technology Innovation Research Team (Grant No. 2020JDTD0015), 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University (Grant No. ZYYC08003), and China Medical Board (Grant No. CMB19-324). Competing interests None declared. Patient consent for publication Not applicable. Provenance and peer review Commissioned; internally peer reviewed. Data availability statement No data are available. Not applicable. ORCID iD Xin Sun http://orcid.org/0000-0002-6554-7088 REFERENCES 1 Booth CM, Karim S, Mackillop WJ. Real-world data: towards achieving the achievable in cancer care. Nat Rev Clin Oncol 2019;16:312–25. 2 Makady A, de Boer A, Hillege H, et al . What is real-world data? A review of definitions based on literature and stakeholder interviews. Value Health 2017;20:858–65. 3 Sherman RE, Anderson SA, Dal Pan GJ, et al . Real-world evidence - what is it and what can it tell us? N Engl J Med 2016;375:2293–7.
Liu M, et al . Eur J Hosp Pharm 2022; 29 :8–11. doi:10.1136/ejhpharm-2021-003081
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