Pain. 2022 Aug 26. doi: 10.1097/j.pain.0000000000002768. Online ahead of print.
Conventional “1-variable-at-a-time” analyses to identify treatment effect modifiers are often underpowered and prone to false positive results. This study used a “risk-modeling” approach guided by the Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement framework: (1) developing and validating a multivariable model to estimate predicted future back-related functional limitations as measured by the Roland-Morris Disability Questionnaire (RMDQ); and (2) stratifying patients from a randomized controlled trial (RCT) of lumbar epidural steroid injections (LESI) for the treatment of lumbar spinal stenosis into subgroups with different individualized treatment effects on RMDQ scores at 3-week follow-up. Model development and validation was conducted in a cohort (n=3259) randomly split into training and testing sets in a 4:1 ratio. The model was developed in the testing set using linear regression with least absolute shrinkage and selection regularization and 5-fold cross-validation. The model was then applied in the testing set, and subsequently in patients receiving the control treatment in the RCT of LESI. R2 values in the training set, testing set and RCT were 0.38, 0.32, and 0.34, respectively. There was statistically significant modification (p=0.03) of the LESI treatment effect according to predicted risk quartile, with clinically relevant LESI treatment effect point estimates in the two quartiles with greatest predicted risk (-3.7 and -3.3 RMDQ points) and no effect in the lowest two quartiles. A multivariable risk-modeling approach identified subgroups of patients with lumbar spinal stenosis with a clinically relevant treatment effect of LESI on back-related functional limitations.