The tyranny of non-inferiority trials.

Opportunities to decrease the toxicity and cost of approved treatment regimens with lower dose, less frequent, or shorter duration alternative regimens have been limited by the perception that alternatives must be non-inferior to approved regimens. Non-inferiority trials are large and expensive to do, because they must show statistically that the alternative and approved therapies differ in a single outcome, by a margin far smaller than that required to demonstrate superiority. Non-inferiority's flaws are manifest: it ignores variability expected to occur with repeated evaluation of the approved therapy, fails to recognise that a trial of similar design will be labelled as superiority or non-inferiority depending on whether it is done prior to or after initial registration of the approved treatment, and relegates endpoints such as toxicity and cost. For example, while a less toxic and less costly regimen of 3 months duration would typically be required to demonstrate efficacy that is non-inferior to that of a standard regimen of 6 months to displace it, the longer duration therapy has no such obligation to prove its superiority. This situation is the tyranny of the non-inferiority trial: its statistics perpetuate less cost-effective regimens, which are not patient-centred, even when less intensive therapies confer survival benefits nearly identical to those of the standard, by placing a disproportionately large burden of proof on the alternative. This approach is illogical. We propose that the designation of trials as superiority or non-inferiority be abandoned, and that randomised, controlled trials should henceforth be described simply as "comparative".

End points in clinical trials in diffuse large B-cell lymphoma: time for more dialogue?

We observed lack of clarity and consistency in end point definitions of large randomized clinical trials in diffuse large B-cell lymphoma. These inconsistencies are such that trials might, in fact, address different clinical questions. They complicate interpretation of results, including comparisons across studies. Problems arise from different ways to account for events occurring after randomization including absence of improvement in disease status, treatment discontinuation or the initiation of new therapy. We call for more dialogue between stakeholders to define with clarity the questions of interest and corresponding end points. We illustrate that assessing different end point rules across a range of plausible patient journeys can be a powerful tool to facilitate such a discussion and contribute to better understanding of patient-relevant end points.

What is this article about? This article talks about the lack of clarity and consistency in the definitions of outcomes used in clinical trials that investigate new treatments for diffuse large B-cell lymphoma. This is mainly due to how these different outcome definitions handle events such as absence of improvement in disease status, treatment discontinuation or initiation of new treatment. The authors discuss how these inconsistencies make it hard to interpret the results of individual clinical trials and to compare results across clinical trials.Why is it important? Defining the above events and consequently defining outcomes affects what we can learn from the trials and can lead to different results. Some approaches may not reflect good and bad outcomes for patients appropriately. This makes it challenging for patients, physicians, health authorities and payors to understand the true benefit of treatments under investigation and which one is better.What are the key take-aways? This article serves as a call-to-action for more dialogue among all stakeholders involved in drug development and the decision-making process related to drug evaluations. There is an urgent need for clinical trials to be designed with more clarity and consistency on what is being measured so that relevant questions for patients and prescribing physicians are addressed. Understanding patient journeys will be key to successfully understand what truly matters to patients and how to measure the benefit of new treatments. Such discussions will contribute toward more clarity and consistency in the evaluation of new treatments.

Design of a clinical trial using generalized pairwise comparisons to test a less intensive treatment regimen.

BACKGROUND/AIMS: Showing "similar efficacy" of a less intensive treatment typically requires a non-inferiority trial. Yet such trials may be challenging to design and conduct. In acute promyelocytic leukemia, great progress has been achieved with the introduction of targeted therapies, but toxicity remains a major clinical issue. There is a pressing need to show the favorable benefit/risk of less intensive treatment regimens. METHODS: We designed a clinical trial that uses generalized pairwise comparisons of five prioritized outcomes (alive and event-free at 2 years, grade 3/4 documented infections, differentiation syndrome, hepatotoxicity, and neuropathy) to confirm a favorable benefit/risk of a less intensive treatment regimen. We conducted simulations based on historical data and assumptions about the differences expected between the standard of care and the less intensive treatment regimen to calculate the sample size required to have high power to show a positive Net Treatment Benefit in favor of the less intensive treatment regimen. RESULTS: Across 10,000 simulations, average sample sizes of 260 to 300 patients are required for a trial using generalized pairwise comparisons to detect typical Net Treatment Benefits of 0.19 (interquartile range 0.14-0.23 for a sample size of 280). The Net Treatment Benefit is interpreted as a difference between the probability of doing better on the less intensive treatment regimen than on the standard of care, minus the probability of the opposite situation. A Net Treatment Benefit of 0.19 translates to a number needed to treat of about 5.3 patients (1/0.19 ≃ 5.3). CONCLUSION: Generalized pairwise comparisons allow for simultaneous assessment of efficacy and safety, with priority given to the former. The sample size required would be of the order of 300 patients, as compared with more than 700 patients for a non-inferiority trial using a margin of 4% against the less intensive treatment regimen for the absolute difference in event-free survival at 2 years, as considered here.

A Randomized Controlled Trial of OPT-302, a VEGF-C/D Inhibitor for Neovascular Age-Related Macular Degeneration.

PURPOSE: Neovascular (wet) age-related macular degeneration (nAMD) is driven by VEGFs A, C, and D, which promote angiogenesis and vascular permeability. Intravitreal injections of anti-VEGF-A drugs are the standard of care, but these do not inhibit VEGF-C and D, which may explain why many patients fail to respond fully. This trial aimed to test the safety and efficacy of OPT-302, a biologic inhibitor of VEGF-C and D, in combination with the anti-VEGF-A inhibitor ranibizumab. DESIGN: Dose-ranging, phase 2b, randomized, double-masked, sham-controlled trial. PARTICIPANTS: Participants with treatment-naive nAMD were enrolled from 109 sites across Europe, Israel, and the United States. METHODS: Participants were randomized to 6, 4-weekly, intravitreal injections of 0.5 mg OPT-302, 2.0 mg OPT-302, or sham, plus intravitreal 0.5 mg ranibizumab. MAIN OUTCOME MEASURES: The primary outcome was mean change in ETDRS best-corrected visual acuity (BCVA) at 24 weeks. Secondary outcomes (comparing baseline with week 24) were the proportion of participants gaining or losing ≥ 15 ETDRS BCVA letters; area under the ETDRS BCVA over time curve; change in spectral-domain OCT (SD-OCT) central subfield thickness; and change in intraretinal fluid and subretinal fluid on SD-OCT. RESULTS: Of 366 participants recruited from December 1, 2017, to November 30, 2018, 122, 123, and 121 were randomized to 0.5 mg OPT-302, 2.0 mg OPT-302, and sham, respectively. Mean (± standard deviation) visual acuity gain in the 2.0 mg OPT-302 group was significantly superior to sham (+14.2 ± 11.61 vs. +10.8 ± 11.52 letters; P = 0.01). The 0.5 mg OPT-302 group was not significantly different than the sham group (+9.44 ± 11.32 letters; P = 0.83). Compared with sham, the secondary BCVA outcomes favored the 2.0 mg OPT-302 group, with structural outcomes favoring both OPT-302 dosage groups. Adverse events (AEs) were similar across groups, with 16 (13.3%), 7 (5.6%), and 10 (8.3%) participants in the lower-dose, higher-dose, and sham groups, respectively, developing at least 1 serious AE. Two unrelated deaths both occurred in the sham arm. CONCLUSIONS: Significantly superior vision gain was observed with OPT-302 2.0 mg combination therapy, versus standard of care, with favorable safety (ClinicalTrials.gov identifier: NCT03345082). FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found after the references.

Minimization in randomized clinical trials.

In randomized trials, comparability of the treatment groups is ensured through allocation of treatments using a mechanism that involves some random element, thus controlling for confounding of the treatment effect. Completely random allocation ensures comparability between the treatment groups for all known and unknown prognostic factors. For a specific trial, however, imbalances in prognostic factors among the treatment groups may occur. Although accidental bias can be avoided in the presence of such imbalances by stratifying the analysis, most trialists, regulatory agencies, and other stakeholders prefer a balanced distribution of prognostic factors across the treatment groups. Some procedures attempt to achieve balance in baseline covariates, by stratifying the allocation for these covariates, or by dynamically adapting the allocation using covariate information during the trial (covariate-adaptive procedures). In this Tutorial, the performance of minimization, a popular covariate-adaptive procedure, is compared with two other commonly used procedures, completely random allocation and stratified blocked designs. Using individual patient data of 2 clinical trials (in advanced ovarian cancer and age-related macular degeneration), the procedures are compared in terms of operating characteristics (using asymptotic and randomization tests), predictability of treatment allocation, and achieved balance. Fifty actual trials of various sizes that applied minimization for treatment allocation are used to investigate the achieved balance. Implementation issues of minimization are described. Minimization procedures are useful in all trials but especially when (1) many major prognostic factors are known, (2) many centers of different sizes accrue patients, or (3) the trial sample size is moderate.

Individualized Net Benefit estimation and meta-analysis using generalized pairwise comparisons in N-of-1 trials.

BACKGROUND: The Net Benefit (Δ) is a measure of the benefit-risk balance in clinical trials, based on generalized pairwise comparisons (GPC) using several prioritized outcomes and thresholds of clinical relevance. We extended Δ to N-of-1 trials, with a focus on patient-level and population-level Δ. METHODS: We developed a Δ estimator at the individual level as an extension of the stratum-specific Δ, and at the population-level as an extension of the stratified Δ. We performed a simulation study mimicking PROFIL, a series of 38 N-of-1 trials testing sildenafil in Raynaud's phenomenon, to assess the power for such an analysis with realistic data. We then reanalyzed PROFIL using GPC. This reanalysis was finally interpreted in the context of the main analysis of PROFIL which used Bayesian individual probabilities of efficacy. RESULTS: Simulations under the null showed good size of the test for both individual and population levels. The test lacked power when being simulated from the true PROFIL data, even when increasing the number of repetitions up to 140 days per patient. PROFIL individual-level estimated Δ were well correlated with the probabilities of efficacy from the Bayesian analysis while showing similarly wide confidence intervals. Population-level estimated Δ was not significantly different from zero, consistently with the previous Bayesian analysis. CONCLUSION: GPC can be used to estimate individual Δ which can then be aggregated in a meta-analytic way in N-of-1 trials. GPC ability to easily incorporate patient preferences allow for more personalized treatment evaluation, while needing much less computing time than Bayesian modeling.

On the use of extreme value tail modeling for generalized pairwise comparisons with censored outcomes.

In randomized clinical trials, methods of pairwise comparisons such as the 'Net Benefit' or the 'win ratio' have recently gained much attention when interests lies in assessing the effect of a treatment as compared to a standard of care. Among other advantages, these methods are usually praised for delivering a treatment measure that can easily handle multiple outcomes of different nature, while keeping a meaningful interpretation for patients and clinicians. For time-to-event outcomes, a recent suggestion emerged in the literature for estimating these treatment measures by providing a natural handling of censored outcomes. However, this estimation procedure may lead to biased estimates when tails of survival functions cannot be reliably estimated using Kaplan-Meier estimators. The problem then extrapolates to the other outcomes incorporated in the pairwise comparison construction. In this work, we suggest to extend the procedure by the consideration of a hybrid survival function estimator that relies on an extreme value tail model through the Generalized Pareto distribution. We provide an estimator of treatment effect measures that notably improves on bias and remains easily apprehended for practical implementation. This is illustrated in an extensive simulation study as well as in an actual trial of a new cancer immunotherapy.

Generalized pairwise comparisons for censored data: An overview.

The method of generalized pairwise comparisons (GPC) is an extension of the well-known nonparametric Wilcoxon-Mann-Whitney test for comparing two groups of observations. Multiple generalizations of Wilcoxon-Mann-Whitney test and other GPC methods have been proposed over the years to handle censored data. These methods apply different approaches to handling loss of information due to censoring: ignoring noninformative pairwise comparisons due to censoring (Gehan, Harrell, and Buyse); imputation using estimates of the survival distribution (Efron, Péron, and Latta); or inverse probability of censoring weighting (IPCW, Datta and Dong). Based on the GPC statistic, a measure of treatment effect, the "net benefit," can be defined. It quantifies the difference between the probabilities that a randomly selected individual from one group is doing better than an individual from the other group. This paper aims at evaluating GPC methods for censored data, both in the context of hypothesis testing and estimation, and providing recommendations related to their choice in various situations. The methods that ignore uninformative pairs have comparable power to more complex and computationally demanding methods in situations of low censoring, and are slightly superior for high proportions (>40%) of censoring. If one is interested in estimation of the net benefit, Harrell's c index is an unbiased estimator if the proportional hazards assumption holds. Otherwise, the imputation (Efron or Peron) or IPCW (Datta, Dong) methods provide unbiased estimators in case of proportions of drop-out censoring up to 60%.

Surrogacy Beyond Prognosis: The Importance of "Trial-Level" Surrogacy.

Many candidate surrogate endpoints are currently assessed using a 2-level statistical approach, which consists in checking whether (1) the potential surrogate is associated with the final endpoint in individual patients and (2) the effect of treatment on the surrogate can be used to reliably predict the effect of treatment on the final endpoint. In some situations, condition (1) is fulfilled but condition (2) is not. We use concepts of causal inference to explain this apparently paradoxical situation, illustrating this review with 2 contrasting examples in operable breast cancer: the example of pathological complete response (pCR) and that of disease-free survival (DFS). In a previous meta-analysis, pCR has been shown to be a strong and independent prognostic factor for event-free survival (EFS) and overall survival (OS) after neoadjuvant treatment of operable breast cancer. Yet, in randomized trials, the effects of experimental treatments on pCR have not translated into predictable effects on EFS or OS, making pCR an "individual-level" surrogate, but not a "trial-level" surrogate. In contrast, DFS has been shown to be an acceptable surrogate for OS at both the individual and trial levels in early, HER2-positive breast cancer. The distinction between the prognostic and predictive roles of a tentative surrogate, not always made in the literature, avoids unnecessary confusion and allows better understanding of what it takes to validate a surrogate endpoint that is truly able to replace a final endpoint.

The case against censoring of progression-free survival in cancer clinical trials - A pandemic shutdown as an illustration.

BACKGROUND: Missing data may lead to loss of statistical power and introduce bias in clinical trials. The Covid-19 pandemic has had a profound impact on patient health care and on the conduct of cancer clinical trials. Although several endpoints may be affected, progression-free survival (PFS) is of major concern, given its frequent use as primary endpoint in advanced cancer and the fact that missed radiographic assessments are to be expected. The recent introduction of the estimand framework creates an opportunity to define more precisely the target of estimation and ensure alignment between the scientific question and the statistical analysis. METHODS: We used simulations to investigate the impact of two basic approaches for handling missing tumor scans due to the pandemic: a "treatment policy" strategy, which consisted in ascribing events to the time they are observed, and a "hypothetical" approach of censoring patients with events during the shutdown period at the last assessment prior to that period. We computed the power of the logrank test, estimated hazard ratios (HR) using Cox models, and estimated median PFS times without and with a hypothetical 6-month shutdown period with no patient enrollment or tumor scans being performed, varying the shutdown starting times. RESULTS: Compared with the results in the absence of shutdown, the "treatment policy" strategy slightly overestimated median PFS proportionally to the timing of the shutdown period, but power was not affected. Except for one specific scenario, there was no impact on the estimated HR. In general, the pandemic had a greater impact on the analyses using the "hypothetical" strategy, which led to decreased power and overestimated median PFS times to a greater extent than the "treatment policy" strategy. CONCLUSION: As a rule, we suggest that the treatment policy approach, which conforms with the intent-to-treat principle, should be the primary analysis to avoid unnecessary loss of power and minimize bias in median PFS estimates.

Using an interim analysis based exclusively on an early outcome in a randomized clinical trial with a long-term clinical endpoint.

In RCTs with an interest in a long-term efficacy endpoint, the follow-up time necessary to observe the endpoint may be substantial. In order to reduce the expected duration of such trials, early-outcome data may be collected to enrich an interim analysis aimed at stopping the trial early for efficacy. We propose to extend such a design with an additional interim analysis using solely early-outcome data in order to expedite the evaluation of treatment's efficacy. We evaluate the potential gain in operating characteristics (power, expected trial duration, and expected sample size) when introducing such an early interim analysis, in function of the properties of the early outcome as a surrogate for the long-term endpoint. In the context of a longitudinal age-related macular degeneration (ARMD) ophthalmology trial, results show potentially substantial gains in both the expected trial duration and the expected sample size. A prerequisite, though, is that the treatment effect on the early outcome has to be strongly correlated with the treatment effect on the long-term endpoint, that is, that the early outcome is a validated surrogate for the long-term endpoint.

The International Collaboration for Research methods Development in Oncology (CReDO) workshops: shaping the future of global oncology research.

Low-income and middle-income countries (LMICs) have a disproportionately high burden of cancer and cancer mortality. The unique barriers to optimum cancer care in these regions necessitate context-specific research. The conduct of research in LMICs has several challenges, not least of which is a paucity of formal training in research methods. Building capacity by training early career researchers is essential to improve research output and cancer outcomes in LMICs. The International Collaboration for Research methods Development in Oncology (CReDO) workshop is an initiative by the Tata Memorial Centre and the National Cancer Grid of India to address gaps in research training and increase capacity in oncology research. Since 2015, there have been five CReDO workshops, which have trained more than 250 oncologists from India and other countries in clinical research methods and protocol development. Participants from all oncology and allied fields were represented at these workshops. Protocols developed included clinical trials, comparative effectiveness studies, health services research, and observational studies, and many of these protocols were particularly relevant to cancer management in LMICs. A follow-up of these participants in 2020 elicited an 88% response rate and showed that 42% of participants had made progress with their CReDO protocols, and 73% had initiated other research protocols and published papers. In this Policy Review, we describe the challenges to research in LMICs, as well as the evolution, structure, and impact of CReDO and other similar workshops on global oncology research.

Results of a randomized phase 3 study of oral sapacitabine in elderly patients with newly diagnosed acute myeloid leukemia (SEAMLESS).

BACKGROUND: Acute myeloid leukemia (AML) is fatal in elderly patients who are unfit for standard induction chemotherapy. The objective of this study was to evaluate the survival benefit of administering sapacitabine, an oral nucleoside analogue, in alternating cycles with decitabine, a low-intensity therapy, to elderly patients with newly diagnosed AML. METHODS: This randomized, open-label, phase 3 study (SEAMLESS) was conducted at 87 sites in 11 countries. Patients aged ≥70 years who were not candidates for or chose not to receive standard induction chemotherapy were randomized 1:1 to arm A (decitabine in alternating cycles with sapacitabine) received 1-hour intravenous infusions of decitabine 20 mg/m2 once daily for 5 consecutive days every 8 weeks (first cycle and subsequent odd cycles) and sapacitabine 300 mg twice daily on 3 consecutive days per week for 2 weeks every 8 weeks (second cycle and subsequent even cycles) or to control arm C who received 1-hour infusions of decitabine 20 mg/m2 once daily for 5 consecutive days every 4 weeks. Prior hypomethylating agent therapy for preexisting myelodysplastic syndromes or myeloproliferative neoplasms was an exclusion criterion. Randomization was stratified by antecedent myelodysplastic syndromes or myeloproliferative neoplasms, white blood cell count (<10 × 109 /L and ≥10 × 109 /L), and bone marrow blast percentage (≥50% vs <50%). The primary end point was overall survival (OS). Secondary end points were the rates of complete remission (CR), CR with incomplete platelet count recovery, partial remission, hematologic improvement, and stable disease along with the corresponding durations, transfusion requirements, number of hospitalized days, and 1-year survival. The trial is registered at ClinicalTrials.gov (NCT01303796). RESULTS: Between October 2011 and December 2014, 482 patients were enrolled and randomized to receive decitabine administered in alternating cycles with sapacitabine (study arm, n = 241) or decitabine monotherapy (control arm, n = 241). The median OS was 5.9 months on the study arm versus 5.7 months on the control arm (P = .8902). The CR rate was 16.6% on the study arm and 10.8% on the control arm (P = .1468). In patients with white blood cell counts <10 × 109 /L (n = 321), the median OS was higher on the study arm versus the control arm (8.0 vs 5.8 months; P = .145), as was the CR rate (21.5% vs 8.6%; P = .0017). CONCLUSIONS: The regimen of decitabine administered in alternating cycles with sapacitabine was active but did not significantly improve OS compared with decitabine monotherapy. Subgroup analyses suggest that patients with baseline white blood cell counts <10 × 109 /L might benefit from decitabine alternating with sapacitabine, with an improved CR rate and the convenience of an oral drug. These findings should be prospectively confirmed.

Net benefit in the presence of correlated prioritized outcomes using generalized pairwise comparisons: A simulation study.

BACKGROUND: The prioritized net benefit (Δ) is a measure of the benefit-risk balance in clinical trials, based on generalized pairwise comparisons (GPC) using several prioritized outcomes. Its estimation requires the classification as Wins or Losses of all possible pairs of patients, one from the experimental treatment (E) group and one from the control treatment (C) group. In this simulation study, we assessed the impact of the correlation between prioritized outcomes on Δ, its estimate, bias, size, and power. METHODS: The theoretical Δ value was derived for the specific case of two correlated binary outcomes when a normal copula is used. Focusing on one efficacy and one toxicity outcome, two situations frequently met in practice were simulated: binary efficacy outcome with binary toxicity outcome, or time to event efficacy outcome with categorical toxicity outcome. Several scenarios of efficacy and toxicity were generated, with various levels of correlation. RESULTS: When E was more effective than C, positive correlations were mainly associated with a decrease in the proportion of Losses, while negative correlations were associated with a decrease in the proportion of Wins on the toxicity outcome. This resulted in an increase of Δ^ with the intensity of the positive correlation without adding any bias. Results were similar whatever the type of outcomes generated but led to power alteration. CONCLUSION: Correlations between outcomes analyzed with GPC led to substantial but predictable modifications of Δ and its estimate. Correlations should be taken into consideration when performing sample size estimations in clinical trials.

A new measure of treatment effect in clinical trials involving competing risks based on generalized pairwise comparisons.

In survival analysis with competing risks, the treatment effect is typically expressed using cause-specific or subdistribution hazard ratios, both relying on proportional hazards assumptions. This paper proposes a nonparametric approach to analyze competing risks data based on generalized pairwise comparisons (GPC). GPC estimate the net benefit, defined as the probability that a patient from the treatment group has a better outcome than a patient from the control group minus the probability of the opposite situation, by comparing all pairs of patients taking one patient from each group. GPC allow using clinically relevant thresholds and simultaneously analyzing multiple prioritized endpoints. We show that under proportional subdistribution hazards, the net benefit for competing risks settings can be expressed as a decreasing function of the subdistribution hazard ratio, taking a value 0 when the latter equals 1. We propose four net benefit estimators dealing differently with censoring. Among them, the Péron estimator uses the Aalen-Johansen estimator of the cumulative incidence functions to classify the pairs for which the patient with the best outcome could not be determined due to censoring. We use simulations to study the bias of these estimators and the size and power of the tests based on the net benefit. The Péron estimator was approximately unbiased when the sample size was large and the censoring distribution's support sufficiently wide. With one endpoint, our approach showed a comparable power to a proportional subdistribution hazards model even under proportional subdistribution hazards. An application of the methodology in oncology is provided.

Correcting the bias of the net benefit estimator due to right-censored observations.

Generalized pairwise comparisons (GPCs) are a statistical method used in randomized clinical trials to simultaneously analyze several prioritized outcomes. This procedure estimates the net benefit (Δ). Δ may be interpreted as the probability for a random patient in the treatment group to have a better overall outcome than a random patient in the control group, minus the probability of the opposite situation. However, the presence of right censoring introduces uninformative pairs that will typically bias the estimate of Δ toward 0. We propose a correction to GPCs that estimates the contribution of each uninformative pair based on the average contribution of the informative pairs. The correction can be applied to the analysis of several prioritized outcomes. We perform a simulation study to evaluate the bias associated with this correction. When only one time-to-event outcome was generated, the corrected estimates were unbiased except in the presence of very heavy censoring. The correction had no effect on the power or type-1 error of the tests based on the Δ. Finally, we illustrate the impact of the correction using data from two randomized trials. The illustrative datasets showed that the correction had limited impact when the proportion of censored observations was around 20% and was most useful when this proportion was close to 70%. Overall, we propose an estimator for the net benefit that is minimally affected by censoring under the assumption that uninformative pairs are exchangeable with informative pairs.

Outcome measures in multimodal rectal cancer trials.

There is a large variability regarding the definition and choice of primary endpoints in phase 2 and phase 3 multimodal rectal cancer trials, resulting in inconsistency and difficulty of data interpretation. Also, surrogate properties of early and intermediate endpoints have not been systematically assessed. We provide a comprehensive review of clinical and surrogate endpoints used in trials for non-metastatic rectal cancer. The applicability, advantages, and disadvantages of these endpoints are summarised, with recommendations on clinical endpoints for the different phase trials, including limited surgery or non-operative management for organ preservation. We discuss how early and intermediate endpoints, including patient-reported outcomes and involvement of patients in decision making, can be used to guide trial design and facilitate consistency in reporting trial results in rectal cancer.

Statistical aspects in adjuvant and neoadjuvant trials for gastrointestinal cancer in 2020: focus on time-to-event endpoints.

PURPOSE OF REVIEW: Clinical-trial design, analysis, and interpretation entails the use of efficient and reliable endpoints. Statistical issues related to endpoints warrant continued attention, as they may have a substantial impact on the conduct of clinical trials and on interpretation of their results. RECENT FINDINGS: We review concepts and discuss recent developments related to the use of time-to-event endpoints in studies on adjuvant and neoadjuvant therapy for colon, pancreatic, and gastric adenocarcinomas. The definition of endpoints has varied to a considerable extent in these settings. Although these variations are relevant in interpreting results from individual trials, they probably have a small impact when considered in aggregate. In terms of surrogacy, most published reports so far have used aggregated data. A few studies based on the preferred method of a metaanalysis of individual-patient data have shown that disease-free survival (DFS) is a surrogate for overall survival in the adjuvant therapy of stage III colon cancer and in gastric cancer, whereas DFS with a landmark of six months is a surrogate for overall survival in the neoadjuvant therapy of adenocarcinoma of the esophagus, gastroesophageal junction, or stomach. SUMMARY: Testing novel agents in gastrointestinal cancer requires continued attention to statistical issues related to endpoints.

Central statistical monitoring of investigator-led clinical trials in oncology.

Investigator-led clinical trials are pragmatic trials that aim to investigate the benefits and harms of treatments in routine clinical practice. These much-needed trials represent the majority of all trials currently conducted. They are however threatened by the rising costs of clinical research, which are in part due to extensive trial monitoring processes that focus on unimportant details. Risk-based quality management focuses, instead, on "things that really matter". We discuss the role of central statistical monitoring as part of risk-based quality management. We describe the principles of central statistical monitoring, provide examples of its use, and argue that it could help drive down the cost of randomized clinical trials, especially investigator-led trials, whilst improving their quality.