Responder analyses for the evaluation of randomized clinical trial (RCT) data have become more common in the recent past, since they can provide the medical community with results that are more directly applicable to clinical care. For pain studies, the predominant responder analysis compares the change in the individual participants’ pain level at baseline to their value at the end of the study period and uses a predetermined clinically important change cut-off value to define a response. While useful, this method substantially reduces the efficiency of the RCT by dichotomizing the results and is limited to comparing the baseline to the end of the study only. In this paper, we introduce a novel approach to the patient response over time with a focus on single dose post-operative studies. This technique provides graphical presentations and statistical approaches to understand the onset of any specified level of response, the maximum proportion of patients with a response at any point in time, and the duration of that response over time. In addition, each outcome can be summarized to examine the result across all possible cut-off points for clinically important differences (CID). We accomplish this by introducing three interrelated, longitudinal efficacy statistics: ROOT, GRO, and GROOT. The response outcome over time (ROOT) estimates the total proportion of a study period an individual patient spends as a responder. The group response outcome (GRO) estimates the instantaneous proportion of responders at all time points across the study period. The group response outcome over time (GROOT) summarizes total efficacy in a cohort, and can be calculated as the area under the GRO curve, or as the mean ROOT; they are identical.

This novel method provides a clinically interpretable responder analysis over the full period of the study and, by using every data point across time, mitigates the loss of statistical power typically associated with dichotomized responder outcomes. Group response analysis is based upon repeated assessments of categorical or continuous measures categorizing each participant’s status as a treatment responder or non-responder at every timepoint based on the prespecified clinically important difference. Both the visual and statistical comparison of any two or more curves provide a comparison of the overall efficacy, which can be statistically tested using a standard asymptotic hypothesis test (such as Wald (Johnson & Romer, 2016)). The method allows for an integrated evaluation of three main components of drug efficacy: the proportion of participants achieving a CID over time (effect), the time to achieve that response (onset), and the length of the response (duration). In this paper, we present the group response analysis methodology and then illustrate it using data from a placebo-controlled randomized clinical trial (RCT) for postoperative pain after third molar extraction treated with meloxicam and ibuprofen as an active comparator (Christensen et al., 2018). Our approach yields similar effect sizes as the sum of pain intensity differences (SPID) commonly used for pain study analyses while providing superior clinical interpretability and a more complete evaluation of drug therapies beyond just efficacy. We propose that this method can be used as a primary or secondary analysis of pain RCTs to answer the question of the patient response to treatment and provide suitable data to compare efficacies across treatment groups.

I.D. reports no conflicts of interest to declare. J.T.F. reports that over the past 3 years, he has received funding from NIH-NCATS-UL1 Grant (Co-I), NIH-NIDDK-U01 Grant (Co-I), from NIH-NINDS-U24 Grant (PI), and 2 FDA-BAA Contracts; and compensation for serving on advisory boards or consulting on clinical trial methods from Vertex, EicOsis, 3Daughters, Scilex Holding Company, and Lilly. He is the past President of the United States Association for the Study of Pain. C.J.M. was previously a full-time employee and has served as an independent consultant for 3D Communications, LLC, a communications firm with many clients in the pharmaceutical, biotechnology, and medical device industries, including those developing analgesic and anesthetic products. C.E.A. has received consulting fees from AbbVie, XGene Pharmaceutical, Lundbeck, Averitas, and Vertex, as well as research support from AbbVie, Eli Lilly, and Vertex Pharmaceuticals. J.A.H reports funding from the NIH-NIAMS UH3 grant (Co-I). The remaining authors have no conflicts of interest to declare.

This study was funded by a contract with the US Food and Drug Administration (FDA) awarded to the University of Pennsylvania (BAA-75F40119C10099). The funder was not involved in the design and conduct of the study; collection, management, analysis, and interpretation of the data; or the decision to submit the manuscript for publication. All interpretations presented in this article reflect the views of the authors and do not reflect the views of the FDA. The data analyzed in this study were submitted to the FDA by industry sponsors as part of regulatory submissions and are not publicly available due to regulatory and confidentiality restrictions.

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University of Pennsylvania Institutional Review Board

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Contact: John T. Farrar, MD, PhD, Professor of Epidemiology, Neurology, and Anesthesia, Room 816 Blockley Hall, University of Pennsylvania, Philadelphia, PA 19104, Phone: 215-898-5802, jfarrarpennmedicine.upenn.edu

All data was submitted to the US Food and Drug Administration (FDA) in support of new drug applications, and is held on the FDA's Document Archiving Reporting and Regulatory Tracking System (DAARTS)