Three-outcome designs for external pilot trials with progression criteria.

BACKGROUND: Whether or not to progress from a pilot study to a definitive trial is often guided by pre-specified quantitative progression criteria with three possible outcomes. Although the choice of these progression criteria will help to determine the statistical properties of the pilot trial, there is a lack of research examining how they, or the pilot sample size, should be determined. METHODS: We review three-outcome trial designs originally proposed in the phase II oncology setting and extend these to the case of external pilots, proposing a unified framework based on univariate hypothesis tests and the control of frequentist error rates. We apply this framework to an example and compare against a simple two-outcome alternative. RESULTS: We find that three-outcome designs can be used in the pilot setting, although they are not generally more efficient than simpler two-outcome alternatives. We show that three-outcome designs can help allow for other sources of information or other stakeholders to feed into progression decisions in the event of a borderline result, but this will come at the cost of a larger pilot sample size than the two-outcome case. We also show that three-outcome designs can be used to allow adjustments to be made to the intervention or trial design before commencing the definitive trial, providing the effect of the adjustment can be accurately predicted at the pilot design stage. An R package, tout, is provided to optimise progression criteria and pilot sample size. CONCLUSIONS: The proposed three-outcome framework provides a way to optimise pilot trial progression criteria and sample size in a way that leads to desired operating characteristics. It can be applied whether or not an adjustment following the pilot trial is anticipated, but will generally lead to larger sample size requirements than simpler two-outcome alternatives.

Applications of the prediction of satisfaction design for monitoring single-arm phase II trials.

Prediction of satisfaction design, with binary endpoints, is an innovative strategy for phase II trials. We explain this hybrid frequentist-Bayesian strategy with an adept statistical plan and thorough findings, incorporating a description of study design features such as the sample size and the beta prior distribution, to simplify the Bayesian design. We also provide a set of tables and figures ranging from the stopping boundary for futility to the prediction of satisfaction, performance (type I error, power, and the probability of early termination PET), and sensitivity analysis for prediction of satisfaction. The statistical plan includes the operating characteristics through simulation study. Several trial examples from phase II lung cancer studies demonstrate the approach's practical use. The prediction of satisfaction design presents a flexible method in clinical study. This statistical study adds value to the application by broadening its scope.

Bayesian sequential monitoring strategies for trials of digestive cancer therapeutics.

BACKGROUND: New therapeutics in oncology have presented challenges to existing paradigms and trial designs in all phases of drug development. As a motivating example, we considered an ongoing phase II trial planned to evaluate the combination of a MET inhibitor and an anti-PD-L1 immunotherapy to treat advanced oesogastric carcinoma. The objective of the paper was to exemplify the planning of an adaptive phase II trial with novel anti-cancer agents, including prolonged observation windows and joint sequential evaluation of efficacy and toxicity. METHODS: We considered various candidate designs and computed decision rules assuming correlations between efficacy and toxicity. Simulations were conducted to evaluate the operating characteristics of all designs. RESULTS: Design approaches allowing continuous accrual, such as the time-to-event Bayesian Optimal Phase II design (TOP), showed good operating characteristics while ensuring a reduced trial duration. All designs were sensitive to the specification of the correlation between efficacy and toxicity during planning, but TOP can take that correlation into account more easily. CONCLUSIONS: While specifying design working hypotheses requires caution, Bayesian approaches such as the TOP design had desirable operating characteristics and allowed incorporating concomittant information, such as toxicity data from concomitant observations in another relevant patient population (e.g., defined by mutational status).

How to Balance Prognostic Factors in Controlled Phase II Trials: Stratified Permuted Block Randomization or Minimization? An Analysis of Clinical Trials in Digestive Oncology.

In controlled phase II trials, major prognostic factors need to be well balanced between arms. The main procedures used are SPBR (Stratified Permuted Block Randomization) and minimization. First, we provide a systematic review of the treatment allocation procedure used in gastrointestinal oncology controlled phase II trials published in 2019. Second, we performed simulations using data from six phase II studies to measure the impacts of imbalances and bias on the efficacy estimations. From the 40 articles analyzed, all mentioned randomization in both the title and abstract, the median number of patients included was 109, and 77.5% were multicenter. Of the 27 studies that reported at least one stratification variable, 10 included the center as a stratification variable, 10 used minimization, 9 used SBR, and 8 were unspecified. In real data studies, the imbalance increased with the number of centers. The total and marginal imbalances were higher with SBR than with minimization, and the difference increased with the number of centers. The efficiency estimates per arm were close to the original trial estimate in both procedures. Minimization is often used in cases of numerous centers and guarantees better similarity between arms for stratification variables for total and marginal imbalances in phase II trials.

REDOMA: Bayesian random-effects dose-optimization meta-analysis using spike-and-slab priors.

The rise of cutting-edge precision cancer treatments has led to a growing significance of the optimal biological dose (OBD) in modern oncology trials. These trials now prioritize the consideration of both toxicity and efficacy simultaneously when determining the most desirable dosage for treatment. Traditional approaches in early-phase oncology trials have conventionally relied on the assumption of a monotone relationship between treatment efficacy and dosage. However, this assumption may not hold valid for novel oncology therapies. In reality, the dose-efficacy curve of such treatments may reach a plateau at a specific dose, posing challenges for conventional methods in accurately identifying the OBD. Furthermore, achieving reliable identification of the OBD is typically not possible based on a single small-sample trial. With data from multiple phase I and phase I/II trials, we propose a novel Bayesian random-effects dose-optimization meta-analysis (REDOMA) approach to identify the OBD by synthesizing toxicity and efficacy data from each trial. The REDOMA method can address trials with heterogeneous characteristics. We adopt a curve-free approach based on a Gamma process prior to model the average dose-toxicity relationship. In addition, we utilize a Bayesian model selection framework that uses the spike-and-slab prior as an automatic variable selection technique to eliminate monotonic constraints on the dose-efficacy curve. The good performance of the REDOMA method is confirmed by extensive simulation studies.

A seamless phase II/III design with dose optimization for oncology drug development.

The US FDA's Project Optimus initiative that emphasizes dose optimization prior to marketing approval represents a pivotal shift in oncology drug development. It has a ripple effect for rethinking what changes may be made to conventional pivotal trial designs to incorporate a dose optimization component. Aligned with this initiative, we propose a novel seamless phase II/III design with dose optimization (SDDO framework). The proposed design starts with dose optimization in a randomized setting, leading to an interim analysis focused on optimal dose selection, trial continuation decisions, and sample size re-estimation (SSR). Based on the decision at interim analysis, patient enrollment continues for both the selected dose arm and control arm, and the significance of treatment effects will be determined at final analysis. The SDDO framework offers increased flexibility and cost-efficiency through sample size adjustment, while stringently controlling the Type I error. This proposed design also facilitates both accelerated approval (AA) and regular approval in a "one-trial" approach. Extensive simulation studies confirm that our design reliably identifies the optimal dosage and makes preferable decisions with a reduced sample size while retaining statistical power.

Precision Oncology Clinical Trials: A Systematic Review of Phase II Clinical Trials with Biomarker-Driven, Adaptive Design.

Novel clinical trial designs are conducted in the precision medicine era. This study aimed to evaluate biomarker-driven, adaptive phase II trials in precision oncology, focusing on infrastructure, efficacy, and safety. We systematically reviewed and analyzed the target studies. EMBASE and PubMed searches from 2015 to 2023 generated 29 eligible trials. Data extraction included infrastructure, biomarker screening methodologies, efficacy, and safety profiles. Government agencies, cancer hospitals, and academic societies with accumulated experiences led investigator-initiated precision oncology clinical trials (IIPOCTs), which later guided sponsor-initiated precision oncology clinical trials (SIPOCTs). Most SIPOCTs were international studies with basket design. IIPOCTs primarily used the central laboratory for biomarker screening, but SIPOCTs used both central and local laboratories. Most of the studies adapted next-generation sequencing and/or immunohistochemistry for biomarker screening. Fifteen studies included an independent central review committee for outcome investigation. Efficacy assessments predominantly featured objective response rate as the primary endpoint, with varying results. Nine eligible studies contributed to the United States Food and Drug Administration's marketing authorization. Safety monitoring was rigorous, but reporting formats lacked uniformity. Health-related quality of life and patient-reported outcomes were described in some protocols but rarely reported. Our results reveal that precision oncology trials with adaptive design rapidly and efficiently evaluate anticancer drugs' efficacy and safety, particularly in specified biomarker-driven cohorts. The evolution from IIPOCT to SIPOCT has facilitated fast regulatory approval, providing valuable insights into the precision oncology landscape.

rBMA: A robust Bayesian Model Averaging Method for phase II basket trials based on informative mixture priors.

Oncology drug research in the last few decades has been driven by the development of targeted agents. In the era of targeted therapies, basket trials are often used to test the antitumor activity of a novel treatment in multiple indications sharing the same genomic alteration. As patient population are further fragmented into biomarker-defined subgroups in basket trials, novel statistical methods are needed to facilitate cross-indication learning to improve the statistical power in basket trial design. Here we propose a robust Bayesian model averaging (rBMA) technique for the design and analysis of phase II basket trials. We consider the posterior distribution of each indication (basket) as the weighted average of three different models which only differ in their priors (enthusiastic, pessimistic and non-informative). The posterior weights of these models are determined based on the effect of the experimental treatment in all the indications tested. In early phase oncology trials, different binary endpoints might be chosen for different indications (objective response, disease control or PFS at landmark times), which makes it even more challenging to borrow information across indications. Compared to previous approaches, the proposed method has the flexibility to support cross-indication learning in the presence of mixed endpoints. We evaluate and compare the performance of the proposed rBMA approach to competing approaches in simulation studies. R scripts to implement the proposed method are available at https://github.com/xwang317/rBMA.

Adaptive phase I-II clinical trial designs identifying optimal biological doses for targeted agents and immunotherapies.

Targeted agents and immunotherapies have revolutionized cancer treatment, offering promising options for various cancer types. Unlike traditional therapies the principle of "more is better" is not always applicable to these new therapies due to their unique biomedical mechanisms. As a result, various phase I-II clinical trial designs have been proposed to identify the optimal biological dose that maximizes the therapeutic effect of targeted therapies and immunotherapies by jointly monitoring both efficacy and toxicity outcomes. This review article examines several innovative phase I-II clinical trial designs that utilize accumulated efficacy and toxicity outcomes to adaptively determine doses for subsequent patients and identify the optimal biological dose, maximizing the overall therapeutic effect. Specifically, we highlight three categories of phase I-II designs: efficacy-driven, utility-based, and designs incorporating multiple efficacy endpoints. For each design, we review the dose-outcome model, the definition of the optimal biological dose, the dose-finding algorithm, and the software for trial implementation. To illustrate the concepts, we also present two real phase I-II trial examples utilizing the EffTox and ISO designs. Finally, we provide a classification tree to summarize the designs discussed in this article.

Risk-benefit trade-offs and precision utilities in phase I-II clinical trials.

BACKGROUND: Identifying optimal doses in early-phase clinical trials is critically important. Therapies administered at doses that are either unsafe or biologically ineffective are unlikely to be successful in subsequent clinical trials or to obtain regulatory approval. Identifying appropriate doses for new agents is a complex process that involves balancing the risks and benefits of outcomes such as biological efficacy, toxicity, and patient quality of life. PURPOSE: While conventional phase I trials rely solely on toxicity to determine doses, phase I-II trials explicitly account for both efficacy and toxicity, which enables them to identify doses that provide the most favorable risk-benefit trade-offs. It is also important to account for patient covariates, since one-size-fits-all treatment decisions are likely to be suboptimal within subgroups determined by prognostic variables or biomarkers. Notably, the selection of estimands can influence our conclusions based on the prognostic subgroup studied. For example, assuming monotonicity of the probability of response, higher treatment doses may yield more pronounced efficacy in favorable prognosis compared to poor prognosis subgroups when the estimand is mean or median survival. Conversely, when the estimand is the 3-month survival probability, higher treatment doses produce more pronounced efficacy in poor prognosis compared to favorable prognosis subgroups. METHODS AND CONCLUSIONS: Herein, we first describe why it is essential to consider clinical practice when designing a clinical trial and outline a stepwise process for doing this. We then review a precision phase I-II design based on utilities tailored to prognostic subgroups that characterize efficacy-toxicity risk-benefit trade-offs. The design chooses each patient's dose to optimize their expected utility and allows patients in different prognostic subgroups to have different optimal doses. We illustrate the design with a dose-finding trial of a new therapeutic agent for metastatic clear cell renal cell carcinoma.

Nonalcoholic steatohepatitis (NASH) cirrhosis: a snapshot of therapeutic agents in clinical development and the optimal design for clinical trials.

INTRODUCTION: Patients with nonalcoholic steatohepatitis (NASH)-associated cirrhosis have the highest rates of major adverse liver outcomes (MALO) within the fatty liver disease spectrum and therefore have the highest unmet need for effective therapeutic agents. Several drugs are being tested for patients with NASH cirrhosis with different mechanisms of action and endpoints. AREAS COVERED: This article summarizes the available data on the natural history of NASH cirrhosis and the rates of developing MALO. We provide examples of ongoing clinical trials for NASH cirrhosis including the study design and endpoints. We then discuss the FDA-guidance on acceptable endpoints for NASH cirrhosis trials that will lead to approval. EXPERT OPINION: Metabolic and antifibrotic drugs are currently in phase 2b trials for NASH cirrhosis with outcomes ranging from histologic improvement on liver biopsy to the development of varices or MALO. We provide the readers with pragmatic advice on trial design for phase 2B and 3 NASH cirrhosis trials. The data presented in the article justify further development and investigation of therapeutic agents for the treatment of NASH cirrhosis.

PIONEER-Panc: a platform trial for phase II randomized investigations of new and emerging therapies for localized pancreatic cancer.

BACKGROUND: Personalized and effective treatments for pancreatic ductal adenocarcinoma (PDAC) continue to remain elusive. Novel clinical trial designs that enable continual and rapid evaluation of novel therapeutics are needed. Here, we describe a platform clinical trial to address this unmet need. METHODS: This is a phase II study using a Bayesian platform design to evaluate multiple experimental arms against a control arm in patients with PDAC. We first separate patients into three clinical stage groups of localized PDAC (resectable, borderline resectable, and locally advanced disease), and further divide each stage group based on treatment history (treatment naïve or previously treated). The clinical stage and treatment history therefore define 6 different cohorts, and each cohort has one control arm but may have one or more experimental arms running simultaneously. Within each cohort, adaptive randomization rules are applied and patients will be randomized to either an experimental arm or the control arm accordingly. The experimental arm(s) of each cohort are only compared to the applicable cohort specific control arm. Experimental arms may be added independently to one or more cohorts during the study. Multiple correlative studies for tissue, blood, and imaging are also incorporated. DISCUSSION: To date, PDAC has been treated as a single disease, despite knowledge that there is substantial heterogeneity in disease presentation and biology. It is recognized that the current approach of single arm phase II trials and traditional phase III randomized studies are not well-suited for more personalized treatment strategies in PDAC. The PIONEER Panc platform clinical trial is designed to overcome these challenges and help advance our treatment strategies for this deadly disease. TRIAL REGISTRATION: This study is approved by the Institutional Review Board (IRB) of MD Anderson Cancer Center, IRB-approved protocol 2020-0075. The PIONEER trial is registered at the US National Institutes of Health (ClinicalTrials.gov) NCT04481204 .

The evolution of adjuvant/neoadjuvant trials for resectable localized sarcoma.

Soft-tissue sarcomas are rare tumors arising from mesenchymal tissues. As a heterogeneous group comprising more than 50 types, the development of clinical trials remains challenging. Decision-making for neoadjuvant or adjuvant chemotherapy and radiation therapy is based on the available evidence of contemporary trials and multidisciplinary clinical judgment.

Advantages of multi-arm non-randomised sequentially allocated cohort designs for Phase II oncology trials.

BACKGROUND: Efficient trial designs are required to prioritise promising drugs within Phase II trials. Adaptive designs are examples of such designs, but their efficiency is reduced if there is a delay in assessing patient responses to treatment. METHODS: Motivated by the WIRE trial in renal cell carcinoma (NCT03741426), we compare three trial approaches to testing multiple treatment arms: (1) single-arm trials in sequence with interim analyses; (2) a parallel multi-arm multi-stage trial and (3) the design used in WIRE, which we call the Multi-Arm Sequential Trial with Efficient Recruitment (MASTER) design. The MASTER design recruits patients to one arm at a time, pausing recruitment to an arm when it has recruited the required number for an interim analysis. We conduct a simulation study to compare how long the three different trial designs take to evaluate a number of new treatment arms. RESULTS: The parallel multi-arm multi-stage and the MASTER design are much more efficient than separate trials. The MASTER design provides extra efficiency when there is endpoint delay, or recruitment is very quick. CONCLUSIONS: We recommend the MASTER design as an efficient way of testing multiple promising cancer treatments in non-comparative Phase II trials.

An Overview of Phase 2 Clinical Trial Designs.

Clinical trials are studies to test new treatments in humans. Typically, these treatments are evaluated over several phases to assess their safety and efficacy. Phase 1 trials are designed to evaluate the safety and tolerability of a new treatment, typically with a small number of patients (eg, 20-80), generally spread across several dose levels. Phase 2 trials are designed to determine whether the new treatment has sufficiently promising efficacy to warrant further investigation in a large-scale randomized phase 3 trial, as well as to further assess safety. These studies usually involve a few hundred patients. This article provides an overview of some of the most commonly used phase 2 designs for clinical trials and emphasizes their critical elements and considerations. Key references to some of the most commonly used phase 2 designs are given to allow the reader to explore in more detail the critical aspects when planning a phase 2 trial. A comparison of 3 potential designs in the context of the NRG-HN002 trial is presented to complement the discussion about phase 2 trials.

The quality of clinical trials in neuroendocrine tumours; have we learnt from our mistakes? An evaluation of phase II and phase III clinical trials.

The quality and reporting of neuroendocrine tumour (NET) clinical trials has previously been found to be heterogeneous impairing trial interpretability. We aimed to perform an updated review of the quality of phase II/III clinical trials in NET to assess if trial design and reporting have improved since 2011. We performed a PubMed search for phase II/III trials evaluating systemic anticancer therapies or liver-directed therapies published between 2011 and 2018. Data collected comprised administrative data, study population characteristics, endpoints, reporting and statistical design parameters, and results. Sixty studies were included (5218 patients): 50 phase II and 10 phase III trials. Study populations were heterogeneous: 52% of trials allowed tumours from various primary sites, 28% allowed both well- and poorly-differentiated tumour morphology or did not specify, and 57% did not report proliferative indices and/or tumour grade in ≥80% of the study population. Only 36% of trials mandated radiological disease progression on participant enrolment using a validated measure. Statistical design and primary endpoint were clearly defined in 67% and 88% of trials, respectively. Toxicity (88%), radiological response rate (85%) and progression-free survival/time to progression (82%) were well reported in a majority of trials, but health-related quality of life was included in the minority. Of the randomised trials (n = 11), study populations were more homogeneous and study design was more often clearly defined; however, only 45% mandated radiological progression at baseline as measured per Response Evaluation Criteria In Solid Tumours, and reporting of health-related quality of life (55%) remained suboptimal. The design and reporting of NET clinical trials, predominantly of single-arm phase II trials, remains suboptimal and has not considerably improved over time despite the growth in our knowledge of the biology and unique characteristics of NETs. Higher quality is seen in randomised trials, although certain design and reporting elements remain inadequate in some studies. We must prioritise the design and conduct of NET clinical trials to effectively inform future research and guide practice change.

RaPiDS (GOG-3028): randomized Phase II study of balstilimab alone or in combination with zalifrelimab in cervical cancer.

Balstilimab (anti-programmed death 1) and zalifrelimab (anti-CTLA-4) are two new checkpoint inhibitors that have emerged as promising investigational agents for the treatment of cervical cancer, particularly in the setting of previously-treated, recurrent/metastatic disease. Here we describe the rationale and design of RaPiDS (NCT03894215), a two-arm Phase II study evaluating the safety, tolerability and efficacy of balstilimab administered alone or in combination with zalifrelimab in patients with advanced cervical cancer who progressed after first-line, platinum-based chemotherapy. Patients will be randomized in a 1:1 ratio. The primary end point is objective response rate, and key secondary objectives include safety, duration of response, progression-free survival, overall survival and quality of life outcomes.

Lay abstract Current treatment options for women with recurrent/metastatic cervical cancer are limited. Immunotherapy is altering the therapeutic landscape in this setting yet opportunities remain to improve on current outcomes. Dual blockade of different immune checkpoints is an approach shown to be highly effective in other cancers. Balstilimab (anti-programmed death 1) and zalifrelimab (anti-CTLA-4) are two new checkpoint inhibitors showing promise in patients with advanced cervical cancer. The RaPiDS trial is designed to characterize the safety and activity of balstilimab, alone and in combination with zalifrelimab, in patients with recurrent/metastatic cervical cancer who progressed after prior platinum-based chemotherapy. Clinical trial registration: NCT03894215 (ClinicalTrials.gov).

Multimodal Treatment with GEMOX Plus Helical Tomotherapy in Unresectable Locally Advanced Pancreatic Cancer: A Pooled Analysis of Two Phase 2 Studies.

In locally advanced pancreatic cancer (LAPC), the combination of chemotherapy and radiotherapy is a widely used treatment option. We performed a pooled analysis, including an exploratory analysis for prognostic and predictive factors, of two phase 2 trials including 73 patients with LAPC, treated with gemcitabine and oxaliplatin (GEMOX) and hypofractionated tomotherapy. With a median follow-up of 36 months (range 1-65), median progression-free (PFS) and overall survival (OS) were 10.2 (95% confidence interval [CI] 7.8-13.2) and 14.3 (95% CI 12.0-18.1) months, respectively. The overall resectability rate was 23.3% (95% CI 13.6-33.0), and the R0 resection rate was 13.7% (95% CI 5.8-21.6). In the multivariate analysis, ECOG performance status (PS) 0 and low levels of CA 19-9 were associated with improved OS and PFS. Concerning OS, log(CA19-9) resulted in a hazard ratio (HR) of 1.20 (95% CI 1.02-1.42), p = 0.027. For ECOG PS 0, HR was 1.00; for PS 1, HR was 2.69 (95% CI 1.46-4.96); for PS 2, HR was 4.18 (95% CI 0.90-19.46); p = 0.003. Low CA19-9 levels were also predictive for resection, with an odds ratio of 0.71 (95% CI 0.52-0.97), p = 0.034. In conclusion, GEMOX and hypofractionated radiotherapy is a treatment option in LAPC. Further studies are needed to identify differences in tumor biology, which may help to predict resectability and prognosis.

Optimal Bayesian hierarchical model to accelerate the development of tissue-agnostic drugs and basket trials.

Tissue-agnostic trials and basket trials enroll patients based on their genetic biomarkers, not tumor type, in an attempt to determine if a new drug can successfully treat disease conditions based on biomarkers. The Bayesian hierarchical model (BHM) provides an attractive approach to design phase II tissue-agnostic trials by allowing information borrowing across multiple disease types. In this article, we elucidate two intrinsic and inevitable issues that may limit the use of BHM to tissue-agnostic trials: sensitivity to the prior specification of the shrinkage parameter and the competing "interest" among disease types in increasing power and controlling type I error. To address these issues, we propose the optimal BHM (OBHM) and clustered OBHM (COBHM) approaches. In these approach, we first specify a flexible utility function to quantify the tradeoff between type I error and power across disease types based on the study objectives, and then we select the prior of the shrinkage parameter to optimize the utility function of clinical and regulatory interest. COBHM further utilizes a simple Bayesian rule to cluster tumor types into sensitive and insensitive subgroups to achieve more accurate information borrowing. Simulation study shows that the OBHM and especially COBHM have desirable operating characteristics, outperforming some existing methods. COBHM effectively balances power and type I error, addresses the sensitivity of the prior selection, and reduces the "unwarranted" subjectivity in the prior selection. It provides a systematic, rigorous way to apply BHM and solve the common problem of blindingly using a non-informative inverse-gamma prior (with a large variance) or priors arbitrarily chosen that may lead to problematic statistical properties.

Use of endpoints in multiple myeloma randomized controlled trials over the last 15 years: A systematic review.

Surrogate endpoints are being used more frequently in randomized controlled trials, even though they do not consistently corelate with patient outcomes. We systemically evaluated the use of surrogate endpoints in multiple myeloma randomized controlled trials over the past 15 years. We searched three databases (Pubmed, Embase, Cochrane) for multiple myeloma randomized controlled trials from January 1, 2005 to December 30, 2019. The primary outcome of our study was the proportion of randomized controlled trials that used overall survival as their primary endpoint. Secondary outcomes included the use of surrogate endpoints, and trends over time, and whether they differed based on study sponsorship. We included 151 randomized controlled trials in our analysis. The primary endpoint was overall survival (OS) in 17 (11.3%) of studies, progression free survival (PFS) or event-defined endpoints in 91 studies (60.3%) and response-based endpoints in 44 studies (29.1%). Quality of life was a primary endpoint in only three studies (2%). The use of OS as a primary endpoint decreased from 28.5% of trials from 2005 to 2009 to 5.5% from 2015 to 2019. There has been a decrease in the clinically meaningful endpoint of OS over the past 15 years in multiple myeloma randomized controlled trials. Use of quality of life as a primary endpoint remains exceedingly low. It remains paramount to recognize that the use of surrogate endpoints is imperfect, and care based upon them requires constant physician and patient re-analysis.