Continuous lenalidomide-dexamethasone (Rd)-based regimens are among the standards of care in transplant-ineligible newly diagnosed multiple myeloma (NDMM) patients. The oral proteasome inhibitor ixazomib is suitable for continuous dosing, with predictable, manageable toxicities. In the double-blind, placebo-controlled TOURMALINE-MM2 trial transplant-ineligible NDMM patients were randomized to ixazomib 4 mg (n = 351) or placebo (n = 354) plus Rd. After 18 cycles, dexamethasone was discontinued; treatment continued using reduced-dose ixazomib (3 mg) and lenalidomide (10 mg) until progression/toxicity. The primary endpoint was progression-free survival (PFS). Median PFS (mPFS) was 35.3 vs 21.8 months with ixazomib-Rd vs placebo-Rd, respectively (hazard ratio [HR], 0.830; 95% confidence interval, 0.676-1.018; P = .073; median follow-up, 53.3 and 55.8 months). Complete (26% vs 14%; odds ratio [OR], 2.10; P < .001) and ≥ very good partial response (63% vs 48%; OR, 1.87; P < .001) rates were higher with ixazomib-Rd vs placebo-Rd. In a prespecified high-risk cytogenetics subgroup, mPFS was 23.8 vs 18.0 months (HR, 0.690; P = .019). Overall, treatment-emergent adverse events (TEAEs) were mostly grade 1/2. With ixazomib-Rd vs placebo-Rd, 88% vs 81% of patients experienced grade ≥3 TEAEs, 66% vs 62% serious TEAEs, and 35% vs 27% TEAEs resulting in regimen discontinuation; 8% vs 6% died on study. Ixazomib-Rd is a feasible option for certain patients who can benefit from an all-oral triplet combination. This trial was registered at www.clinicaltrials.gov (#NCT01850524).

– Coronavirus disease 2019 (COVID-19) outbreak has rapidly evolved into a global pandemic. The impact of COVID-19 on patient journeys in oncology represents a new risk to interpretation of trial results and its broad applicability for future clinical practice. We identify key intercurrent events (ICEs) that may occur due to COVID-19 in oncology clinical trials with a focus on time-to-event endpoints and discuss considerations pertaining to the other estimand attributes introduced in the ICH E9 addendum. We propose strategies to handle COVID-19 related ICEs, depending on their relationship with malignancy and treatment and the interpretability of data after them. We argue that the clinical trial objective from a world without COVID-19 pandemic remains valid. The estimand framework provides a common language to discuss the impact of COVID-19 in a structured and transparent manner. This demonstrates that the applicability of the framework may even go beyond what it was initially intended for.

Asymptotic distributions under alternative hypotheses and their corresponding sample size and power equations are derived for nonparametric test statistics commonly used to compare two survival curves. Test statistics include the weighted log‐rank test and the Wald test for difference in (or ratio of) Kaplan‐Meier survival probability, percentile survival, and restricted mean survival time. Accrual, survival, and loss to follow‐up are allowed to follow any arbitrary continuous distribution. We show that Schoenfeld's equation—often used by practitioners to calculate the required number of events for the unweighted log‐rank test—can be inaccurate even when the proportional hazards (PH) assumption holds. In fact, it can mislead one to believe that 1:1 is the optimal randomization ratio (RR), when actually power can be gained by assigning more patients to the active arm. Meaningful improvements to Schoenfeld's equation are made. The present theory should be useful in designing clinical trials, particularly in immuno‐oncology where nonproportional hazards are frequently encountered. We illustrate the application of our theory with an example exploring optimal RR under PH and a second example examining the impact of delayed treatment effect. A companion R package npsurvSS is available for download on CRAN.

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An addendum of the ICH E9 guideline on Statistical Principles for Clinical Trials was released in November 2019 introducing the estimand framework. This new framework aims to align trial objectives and statistical analyses by requiring a precise definition of the inferential quantity of interest, that is, the estimand. This definition explicitly accounts for intercurrent events, such as switching to new anticancer therapies for the analysis of overall survival (OS), the gold standard in oncology. Traditionally, OS in confirmatory studies is analyzed using the intention-to-treat (ITT) approach comparing treatment groups as they were initially randomized regardless of whether treatment switching occurred and regardless of any subsequent therapy (treatment-policy strategy). Regulatory authorities and other stakeholders often consider ITT results as most relevant.

Attempts to identify and prioritize functional DNA elements in coding and noncoding regions, particularly through use of in silico functional annotation data, continue to increase in popularity. However, specific functional roles may vary widely from one variant to another, making it challenging to summarize different aspects of variant function. Here we propose Multi-dimensional Annotation Class Integrative Estimation (MACIE), an unsupervised multivariate mixed model framework capable of integrating annotations of diverse origin to assess multi-dimensional functional roles for both coding and noncoding variants. Unlike existing one-dimensional scoring methods, MACIE views variant functionality as a composite attribute encompassing multiple different characteristics, and estimates the joint posterior functional probability vector of each genomic position, a quantity that offers richer and more interpretable information in the presence of multiple aspects of functionality. Applied to a variety of independent coding and non-coding datasets, MACIE demonstrates powerful and robust performance in discriminating between functional and non-functional variants. We also show an application of MACIE to fine-mapping using lipids GWAS summary statistics data from the European Network for Genetic and Genomic Epidemiology Consortium.

Peripheral neuropathy (PN) is the most troublesome adverse event associated with the proteasome inhibitor bortezomib. Studies suggest an inflammatory aetiology for bortezomib-induced PN (BiPN) and it has been hypothesized that reducing inflammation with concomitant dexamethasone may reduce BiPN incidence and/or severity. We retrospectively analysed PN rates from 32 studies (2697 patients with previously untreated multiple myeloma) incorporating bortezomib and differing dexamethasone schedules: partnered dosing (days of and after bortezomib), weekly dosing, and other dosing schedules (e.g. days 1-4, 8-11). Pooled overall PN rates were 45·5%, 63·9%, and 47·5%, respectively, with 5·3%, 11·0%, and 9·6% grade ≥3. Adjusting for potential confounders (age, gender, presence of thalidomide, bortezomib treatment duration), PN rates in patients on partnered dosing schedules appeared lower than in patients on weekly or other dosing schedules. Analyses conducted using patient-level data suggest that cumulative dexamethasone dose, a potential confounding factor, is unlikely to have influenced the analyses. Findings were similar in a separate pooled analysis excluding data from regimens incorporating thalidomide, when pooled overall PN rates were 50·1%, 63·9%, and 48·3%, respectively, with 4·2%, 11·0%, and 8·6% grade ≥3. These findings suggest that partnered dexamethasone dosing may result in less severe BiPN compared with alternative dexamethasone dosing schedules.