Adverse Event Reporting in Randomized Clinical Trials for Multiple Myeloma.

Importance: Cancer treatment can result in burdensome toxic effects that profoundly affect patient quality of life. In seeking to emphasize the efficacy of tested treatments, clinical trial reports may use subjective or minimizing terms to describe adverse events (AEs). Objective: To evaluate patterns of AE reporting in multiple myeloma (MM) randomized clinical trials (RCTs) published between 2015 and early 2023. Design, Setting, and Participants: For this cohort study, the PubMed, Embase, and Cochrane Central Register of Controlled Trials databases were searched to assess the prevalence of minimizing terms in MM RCTs published between January 1, 2015, and March 1, 2023. Minimizing terms were defined as subjective terms used to favorably describe the safety profile of the intervention. The terms searched included convenient, manageable, acceptable, expected, well-tolerated, tolerable, favorable, and safe. Final data analysis was performed on July 21, 2023. Main Outcomes and Measures: The primary outcome was the occurrence of at least 1 minimizing term in an article. Univariate logistic regression analyses were performed to evaluate the association between the presence of at least 1 minimizing term and the actual incidence of grade 3 or 4 AEs, serious AEs, or grade 5 AEs. Results: Of the 65 RCTs included, 56 (86%) used minimizing terms when describing treatment-emergent AEs. The most frequently used minimizing terms were well-tolerated or tolerable in 29 trials (45%), manageable in 18 (28%), and acceptable in 16 (25%). Grade 3 or 4 AE rate in the examined RCTs ranged from 23% to 94%, with a median of 75% (IQR, 59%-82%). A univariate regression analysis demonstrated no association between the use of minimizing terms and grade 3 or 4 AE rates (odds ratio [OR], 1.35 [95% CI, 0.88-2.10] per 10% AE rate increase; P = .17) or grade 5 AE rates (OR, 3.16 [95% CI, 0.27-12.7] per 10% AE rate increase; P = .45). Conclusions and Relevance: These findings suggest that trial investigators and sponsors regularly use minimizing terms to describe toxic effects in MM trials, and use of this terminology may not reflect actual AE rates in these studies. Instead of using these terms, trial investigators should highlight event rates and patient-reported outcomes, to allow clinicians and patients to better evaluate the true tolerability of AEs.

Refined moderation analysis with binary outcomes in precision medicine research.

Moderation analysis for evaluating differential treatment effects serves as the bedrock of precision medicine, which is of growing interest in many fields. In the analysis of data with binary outcomes, we observe an interesting symmetry property concerning the ratio of odds ratios, which suggests that heterogeneous treatment effects could be equivalently estimated via a role exchange between the outcome and treatment variable in logistic regression models. We then obtain refined inference on moderating effects by rearranging data and combining two models into one via a generalized estimating equation approach. The improved efficiency is helpful in addressing the lack-of-power problem that is common in the search for important moderators. We investigate the proposed method by simulation and provide an illustration with data from a randomized trial on wart treatment.