Real-world data analysis on effectiveness of integrative therapies: A practical guide to study design and data analysis using healthcare databases

Abstract

Real world data (RWD) is increasingly used to investigate health outcomes and treatment efficacy in the field of integrative medicine. Due to the fact that the majority of RWDs are not intended for research, their secondary use in research necessitates complex study designs to account for bias and confounding. To conduct a robust analysis of RWD in integrative medicine, a comprehensive study design process that reflects the characteristics of integrative therapies is necessary. In this paper, we present a guide for designing comparative effectiveness RWE research in integrative medicine. We discuss key factors to consider when selecting RWDs for research on integrative medicine. We provide practical steps for developing a research question, formulating the PICOT objectives (population, intervention, comparator, outcome, and time horizon), and selecting and defining covariates with a summary table. Specific study designs are depicted with corresponding diagrams. Finally, data analysis procedures are introduced. We hope this article clarifies the importance of RWE research design and related processes in order to improve the rigor of RWD studies in the field of integrative medicine research.

Keywords: Acupuncture; Epidemiology; Integrative medicine; Real world data; Real world evidence.

Fig. 1

Sample directed acyclic graph (DAG) illustrating the relationship between independent variable, dependent variable, and covariates.

Fig. 2

Study design diagram for acupuncture research using RWD using cohort design.

Fig. 3

Study design diagram for acupuncture research using RWD using nested case-control design.