Analytical methods to incorporate potential concurrent analgesic use into primary statistical summaries are underutilized in pain randomized controlled trials (RCTs). Without valid inclusion of analgesic use, the primary analyses of pain may produce diminished estimated treatment effects. We used contemporary causal inference methods that can account for concurrent treatment to reanalyze RCT data examining the effect of epidural steroid injection (ESI). Specifically, we define an “attributable to ESI estimand”, which is the controlled direct effect of ESI. We used a simple composite pain intensity outcome, the QPAC1.5, and structural mean models (SMM) to estimate the target estimand. Compared to traditional methods such as strict intention to treat analysis (strict ITT), SMMs can account for analgesic use without assuming sequential ignorability. We estimated treatment effects of ESI on leg pain intensity (LPI) using the numeric rating scale (NRS) with strict ITT, 3 SMM estimating methods (estimating equations [EE], g-estimation, and generalized method of moments [GMM]), and the QPAC1.5. The treatment effect of ESI on LPI using strict ITT was -0.751 NRS points (95% confidence interval [CI]: [-1.287, -0.214]). Estimates for the attributable to ESI estimand were -0.864 (95% CI: [-3.207, 1.478]) for EE, -0.935 (95% CI: [-1.779, 0.090]) for g-estimation, -0.653 (95% CI: [-1.218, -0.089]]) for GMM, and -0.930 (95% CI: [-1.508, - 0.352]) for the QPAC1.5. We illustrate how contemporary causal inference methods and alternative estimands can be used to account for concurrent analgesic use in pain RCTs, in a manner that may result in larger treatment effects.

The authors have declared no competing interest.

NCT01238536

Research reported in this publication was supported by the University of Washington Clinical Learning, Evidence And Research (CLEAR) Center for Musculoskeletal Research Methodologic and Resource Cores. CLEAR is a Core Center for Clinical Research (CCCR) funded by P30AR072572 from the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) of the National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Separate from the current work, Dr. Suri is a Staff Physician at the VA Puget Sound Health Care System in Seattle, Washington.

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