Covariate Adjustment in Chronic Pain Trials: An Oft-Missed Opportunity

Self-reported pain intensity, frequently used as an outcome in randomized clinical trials (RCTs) of chronic pain, is often highly variable and could be associated with multiple baseline factors. Thus, the assay sensitivity of pain trials (ie, the ability of the trial to detect a true treatment effect) could be improved by including prespecified baseline factors in the primary statistical model. The objective of this focus article was to characterize the baseline factors included in statistical analyses of chronic pain RCTs. Seventy-three RCTs published between 2016 and 2021 that investigated interventions for chronic pain were included. The majority of trials identified a single primary analysis (72.6%; n = 53). Of these, 60.4% (n = 32) included one or more covariates in the primary statistical model, most commonly baseline value of the primary outcome, study site, sex, and age. Only one of the trials reported information regarding associations between covariates and outcomes (ie, information that could inform prioritization of covariates for prespecification in future analyses). These findings demonstrate inconsistent use of covariates in the statistical models in chronic pain clinical trials. Prespecified adjustments for baseline covariates that could increase precision and assay sensitivity should be considered in future clinical trials of chronic pain treatments. Perspective: This review demonstrates inconsistent inclusion and potential underutilization of covanate adjustment in analyses of chronic pain RCTs. This article highlights areas for possible improvement in design and reporting related to covanate adjustment to improve efficiency in future RCTs. & REG; 2023 by the American Pain Society All rights reserved.

Automated summary

The statistical analysis of baseline factors in chronic pain RCTs is inconsistent, and prespecified adjustments for baseline covariates can improve accuracy and assay sensitivity.