Identifying back pain subgroups: developing and applying approaches using individual patient data collected within clinical trials

Background There is good evidence that therapist-delivered interventions have modest beneficial effects for people with low back pain (LBP). Identification of subgroups of people with LBP who may benefit from these different treatment approaches is an important research priority. Aims and objectives To improve the clinical effectiveness and cost-effectiveness of LBP treatment by providing patients, their clinical advisors and health-service purchasers with better information about which participants are most likely to benefit from which treatment choices. Our objectives were to synthesise what is already known about the validity, reliability and predictive value of possible treatment moderators (patient factors that predict response to treatment) for therapist-delivered interventions; develop a repository of individual participant data from randomised controlled trials (RCTs) testing therapist-delivered interventions for LBP; determine which participant characteristics, if any, predict clinical response to different treatments for LBP; and determine which participant characteristics, if any, predict the most cost-effective treatments for LBP. Achieving these objectives required substantial methodological work, including the development and evaluation of some novel statistical approaches. This programme of work was not designed to analyse the main effect of interventions and no such interpretations should be made. Methods First, we reviewed the literature on treatment moderators and subgroups. We initially invited investigators of trials of therapist-delivered interventions for LBP with >179 participants to share their data with us; some further smaller trials that were offered to us were also included. Using these trials we developed a repository of individual participant data of therapist-delivered interventions for LBP. Using this data set we sought to identify which participant characteristics, if any, predict response to different treatments (moderators) for clinical effectiveness and cost-effectiveness outcomes. We undertook an analysis of covariance to identify potential moderators to apply in our main analyses. Subsequently, we developed and applied three methods of subgroup identification: recursive partitioning (interaction trees and subgroup identification based on a differential effect search); adaptive risk group refinement; and an individual participant data indirect network meta-analysis (NWMA) to identify subgroups defined by multiple parameters. Results We included data from 19 RCTs with 9328 participants (mean age 49 years, 57% females). Our prespecified analyses using recursive partitioning and adaptive risk group refinement performed well and allowed us to identify some subgroups. The differences in the effect size in the different subgroups were typically small and unlikely to be clinically meaningful. Increasing baseline severity on the outcome of interest was the strongest driver of subgroup identification that we identified. Additionally, we explored the application of Bayesian indirect NWMA. This method produced varying probabilities that a particular treatment choice would be most likely to be effective for a specific patient profile. Conclusions These data lack clinical effectiveness or cost-effectiveness justification for the use of baseline characteristics in the development of subgroups for back pain. The methodological developments from this work have the potential to be applied in other clinical areas. The pooled repository database will serve as a valuable resource to the LBP research community.