Design of clinical trials in integrative medicine: The issue of personalization

Abstract

IntroductionClassic randomized controlled trial design provides information about the effectiveness of a treatment at population level. In clinical practice based on precision medicine, we seek study designs that provide estimates accounting for individual patients’ demographics and context. Integrative medicine has a strong emphasis on personalization, and there is a pressing need for advancements in trial design to support clinical decisions. DiscussionTo enhance the generalizability and implementation of research evidence, study designs first need to mimic the real-world from both the clinicians’ and patients’ perspectives, which could be optimized based on stakeholder analysis. Previous stakeholder analyzes showed that the lack of personalization in clinical trial design is a key challenge for the adoption of evidence clinically. Variations of randomized controlled trial design including better-powered and better-designed subgroup analysis, pragmatic design, the N-of-1 cross-over design, and adaptive design have been introduced to personalize clinical trials. Each of these variations has unique advantages, assumptions and limitations. Stratified randomization can be used to divide a disease population into subgroups according to different schools of theory to match with the corresponding treatment, while preserving the generalizability to general readership. Patient-level meta-analysis can improve the power and precision of subgroup analysis. Enrichment design can increase the efficiency of a trial in detecting treatment effect(s) and in mimicking actual clinical use. Practical issues, including bias control and assessment in pragmatic trials, and N-of-1 trials are also discussed with recent trials as examples. We suggest strategies to maximize the validity and translation potential of clinical trials in integrative medicine with pragmatic or personalized design. ConclusionVariations of randomized controlled trial design improve the precision of estimates at patient level, which should be carefully considered in the trial design of integrative medicine according to the context of the future application of evidence.