Estimating minimal, moderate, and marked clinically important differences for patient reported outcome measure in randomised controlled trial

– Background: – Patient-reported outcome measures (PROMs) are increasingly used in clinical trials, yet thresholds that define clinically important difference (CID) within patients and treatment effects beyond placebo remain inconsistently reported, limiting its utility in clinical research and practice. Using chronic pelvic pain and its core PROM as a worked example, the study aims to present a structured framework for CID thresholds that separates within-group change from between-group difference in treatment effect, each further stratified as minimal, moderate, and marked. Methods: – We used data from a multicenter randomized controlled trial of acupuncture vs. sham acupuncture in 440 men with chronic prostatitis (CP)/chronic pelvic pain syndrome (CPPS) conducted from October 2017 to April 2019. National Institutes of Health Chronic Prostatitis Symptom Index (NIH-CPSI) served as the PROM and the Patient Global Impression of Change (PGI-C) as the anchor. CIDs were estimated using a linear mixed-effects model to assess changes in NIH-CPSI scores across PGI-C categories. Results: – At the end of treatment, 12.3% (51/414) participants reported no change, 34.1% (141/414) slight improvement, 32.1% (133/414) moderate improvement, and 19.6% (81/414) marked improvement; and 1.9% (8/414) reported worsening. The estimated Clinically important within-group difference (CIDs_within-group) in NIH-CPSI total score was 4.6 points for minimal, 6.0 points for moderate, and 7.3 points for marked improvement. The corresponding Clinically important between-group difference (CIDs_{between-group}) were 1.4, 2.7, and 4.1 points, respectively. Conclusions: – In this methodological study, we proposed a pragmatic and scalable operational framework to derive thresholds for minimal, moderate, and marked CIDs. By explicitly distinguishing CIDs_within-group and CIDs_{between-group}, the approach clarifies two frequently conflated terms that serve distinct purposes in interpretation and decision-making. This framework offers benchmarks to strengthen inference on treatment response, trial design, and guideline decision thresholds.