Two-to-One Randomization: Rarely Advisable.

In a randomized clinical trial, instead of allocating patients equally between the treatment arms, some trials in oncology assign a higher proportion of patients to receive the experimental treatment arm (eg, a two-to-one randomization). In this commentary, we first briefly review the common reasons given for the use of a two-to-one randomization and provide some examples of trials using these designs. We then explain why the risk-benefit ratio of this approach may not be favorable as is commonly assumed.

Design of randomized clinical trials with a binary endpoint: Conditional versus unconditional analyses of a two-by-two table.

When designing a randomized clinical trial to compare two treatments, the sample size required to have desired power with a specified type 1 error depends on the hypothesis testing procedure. With a binary endpoint (e.g., response), the trial results can be displayed in a 2 × 2 table. If one does the analysis conditional on the number of positive responses, then using Fisher's exact test has an actual type 1 error less than or equal to the specified nominal type 1 error. Alternatively, one can use one of many unconditional "exact" tests that also preserve the type 1 error and are less conservative than Fisher's exact test. In particular, the unconditional test of Boschloo is always at least as powerful as Fisher's exact test, leading to smaller required sample sizes for clinical trials. However, many statisticians have argued over the years that the conditional analysis with Fisher's exact test is the only appropriate procedure. Since having smaller clinical trials is an extremely important consideration, we review the general arguments given for the conditional analysis of a 2 × 2 table in the context of a randomized clinical trial. We find the arguments not relevant in this context, or, if relevant, not completely convincing, suggesting the sample-size advantage of the unconditional tests should lead to their recommended use. We also briefly suggest that since designers of clinical trials practically always have target null and alternative response rates, there is the possibility of using this information to improve the power of the unconditional tests.

Avoiding Delays in Reporting Time-to-Event Randomized Trials: Calendar Backstops and Other Approaches.

New oncology therapies that extend patients' lives beyond initial expectations and improving later-line treatments can lead to complications in clinical trial design and conduct. In particular, for trials with event-based analyses, the time to observe all the protocol-specified events can exceed the finite follow-up of a clinical trial or can lead to much delayed release of outcome data. With the advent of multiple classes of oncology therapies leading to much longer survival than in the past, this issue in clinical trial design and conduct has become increasingly important in recent years. We propose a straightforward prespecified backstop rule for trials with a time-to-event analysis and evaluate the impact of the rule with both simulated and real-world trial data. We then provide recommendations for implementing the rule across a range of oncology clinical trial settings.

Backfilling Patients in Phase I Dose-Escalation Trials Using Bayesian Optimal Interval Design (BOIN).

In recent years, there has been increased interest in incorporation of backfilling into dose-escalation clinical trials, which involves concurrently assigning patients to doses that have been previously cleared for safety by the dose-escalation design. Backfilling generates additional information on safety, tolerability, and preliminary activity on a range of doses below the maximum tolerated dose (MTD), which is relevant for selection of the recommended phase II dose and dose optimization. However, in practice, backfilling may not be rigorously defined in trial protocols and implemented consistently. Furthermore, backfilling designs require careful planning to minimize the probability of treating additional patients with potentially inactive agents (and/or subtherapeutic doses). In this paper, we propose a simple and principled approach to incorporate backfilling into the Bayesian optimal interval design (BOIN). The design integrates data from the dose-escalation and backfilling components of the design and ensures that the additional patients are treated at doses where some activity has been seen. Simulation studies demonstrated that the proposed backfilling BOIN design (BF-BOIN) generates additional data for future dose optimization, maintains the accuracy of the MTD identification, and improves patient safety without prolonging the trial duration.

Discovery of the first unconventional myosin: Acanthamoeba myosin-I.

Having characterized actin from Acanthamoeba castellanii (Weihing and Korn, Biochemistry, 1971, 10, 590-600) and knowing that myosin had been isolated from the slime mold Physarum (Hatano and Tazawa, Biochim. Biophys. Acta, 1968, 154, 507-519; Adelman and Taylor, Biochemistry, 1969, 8, 4976-4988), we set out in 1969 to find myosin in Acanthamoeba. We used K-EDTA-ATPase activity to assay myosin, because it is a unique feature of muscle myosins. After slightly less than 3 years, we purified a K-EDTA ATPase that interacted with actin. Actin filaments stimulated the Mg-ATPase activity of the crude enzyme, but this was lost with further purification. Recombining fractions from the column where this activity was lost revealed a "cofactor" that allowed actin filaments to stimulate the Mg-ATPase of the purified enzyme. The small size of the heavy chain and physical properties of the purified myosin were unprecedented, so many were skeptical, assuming that our myosin was a proteolytic fragment of a larger myosin similar to muscle or Physarum myosin. Subsequently our laboratories confirmed that Acanthamoeba myosin-I is a novel unconventional myosin that interacts with membrane lipids (Adams and Pollard, Nature, 1989, 340 (6234), 565-568) and that the cofactor is a myosin heavy chain kinase (Maruta and Korn, J. Biol. Chem., 1977, 252, 8329-8332). Phylogenetic analysis (Odronitz and Kollmar, Genome Biology, 2007, 8, R196) later established that class I myosin was the first myosin to appear during the evolution of eukaryotes.

Timing and Reporting of Secondary Overall Survival End Points for Phase III Trials in Advanced/Metastatic Disease.

Recent therapeutic advances have led to improved patient survival in many cancer settings. Although prolongation of survival remains the ultimate goal of cancer treatment, the availability of effective salvage therapies could make definitive phase III trials with primary overall survival (OS) end points difficult to complete in a timely manner. Therefore, to accelerate development of new therapies, many phase III trials of new cancer therapies are now designed with intermediate primary end points (eg, progression-free survival in the metastatic setting) with OS designated as a secondary end point. We review recently published phase III trials and assess contemporary practices for designing and reporting OS as a secondary end point. We then provide design and reporting recommendations for trials with OS as a secondary end point to safeguard OS data integrity and optimize access to the OS data for patient, clinician, and public-health stakeholders.

Augmenting randomized clinical trial data with historical control data: Precision medicine applications.

As precision medicine becomes more precise, the sizes of the molecularly targeted subpopulations become increasingly smaller. This can make it challenging to conduct randomized clinical trials of the targeted therapies in a timely manner. To help with this problem of a small patient subpopulation, a study design that is frequently proposed is to conduct a small randomized clinical trial (RCT) with the intent of augmenting the RCT control arm data with historical data from a set of patients who have received the control treatment outside the RCT (historical control data). In particular, strategies have been developed that compare the treatment outcomes across the cohorts of patients treated with the standard (control) treatment to guide the use of the historical data in the analysis; this can lessen the potential well-known biases of using historical controls without any randomization. Using some simple examples and completed studies, we demonstrate in this commentary that these strategies are unlikely to be useful in precision medicine applications.

Dose optimization during drug development: whether and when to optimize.

The goal of dose optimization during drug development is to identify a dose that preserves clinical benefit with optimal tolerability. Traditionally, the maximum tolerated dose in a small phase I dose escalation study is used in the phase II trial assessing clinical activity of the agent. Although it is possible that this dose level could be altered in the phase II trial if an unexpected level of toxicity is seen, no formal dose optimization has routinely been incorporated into later stages of drug development. Recently it has been suggested that formal dose optimization (involving randomly assigning patients between 2 or more dose levels) be routinely performed early in drug development, even before it is known that the experimental therapy has any clinical activity at any dose level. We consider the relative merits of performing dose optimization earlier vs later in the drug development process and demonstrate that a considerable number of patients may be exposed to ineffective therapies unless dose optimization is delayed until after clinical activity or benefit of the new agent has been established. We conclude that patient and public health interests may be better served by conducting dose optimization after (or during) phase III evaluation, with some exceptions when dose optimization should be performed after activity shown in phase II evaluation.

Clinical Benefit Scales and Trial Design: Some Statistical Issues.

Recently developed clinical-benefit outcome scales by the European Society for Medical Oncology and the American Society of Clinical Oncology allow standardized objective evaluation of outcomes of randomized clinical trials. However, incorporation of clinical-benefit outcome scales into trial designs highlights a number of statistical issues: the relationship between minimal clinical benefit and the target treatment-effect alternative used in the trial design, designing trials to assess long-term benefit, potential problems with using a trial endpoint that is not overall survival, and how to incorporate subgroup analyses into the trial design. Using the European Society for Medical Oncology Magnitude of Clinical Benefit Scale as a basis for discussion, we review what these issues are and how they can guide the choice of trial-design target effects, appropriate endpoints, and prespecified subgroup analyses to increase the chances that the resulting trial outcomes can be appropriately evaluated for clinical benefit.

Time trends with response-adaptive randomization: The inevitability of inefficiency.

Response-adaptive randomization, which changes the randomization ratio as a randomized clinical trial progresses, is inefficient as compared to a fixed 1:1 randomization ratio in terms of increased required sample size. It is also known that response-adaptive randomization leads to biased treatment effects if there are time trends in the accruing outcome data, for example, due to changes in the patient population being accrued, evaluation methods, or concomitant treatments. Response-adaptive-randomization analysis methods that account for potential time trends, such as time-block stratification or re-randomization, can eliminate this bias. However, as shown in this Commentary, these analysis methods cause a large additional inefficiency of response-adaptive randomization, regardless of whether a time trend actually exists.

A Problematic Biomarker Trial Design.

Efficient biomarker-driven randomized clinical trials are a key tool for implementing precision oncology. A commonly used biomarker phase III design is focused on testing the treatment effect in biomarker-positive and overall study populations. This approach may result in recommending new treatments to biomarker-negative patients when these treatments have no benefit for these patients.

Are restricted mean survival time methods especially useful for noninferiority trials?

BACKGROUND: Restricted mean survival time methods compare the areas under the Kaplan-Meier curves up to a time τ for the control and experimental treatments. Extraordinary claims have been made about the benefits (in terms of dramatically smaller required sample sizes) when using restricted mean survival time methods as compared to proportional hazards methods for analyzing noninferiority trials, even when the true survival distributions satisfy proportional hazardss. METHODS: Through some limited simulations and asymptotic power calculations, the authors compare the operating characteristics of restricted mean survival time and proportional hazards methods for analyzing both noninferiority and superiority trials under proportional hazardss to understand what relative power benefits there are when using restricted mean survival time methods for noninferiority testing. RESULTS: In the setting of low-event rates, very large targeted noninferiority margins, and limited follow-up past τ, restricted mean survival time methods have more power than proportional hazards methods. For superiority testing, proportional hazards methods have more power. This is not a small-sample phenomenon but requires a low-event rate and a large noninferiority margin. CONCLUSION: Although there are special settings where restricted mean survival time methods have a power advantage over proportional hazards methods for testing noninferiority, the larger issue in these settings is defining appropriate noninferiority margins. We find the restricted mean survival time methods lacking in these regards.

Dictyostelium myosin 1F and myosin 1E inhibit actin waves in a lipid-binding-dependent and motor-independent manner.

Actin waves are F-actin-rich entities traveling on the ventral plasma membrane by the treadmilling mechanism. Actin waves were first discovered and are best characterized in Dictyostelium. Class I myosins are unconventional monomeric myosins that bind lipids through their tails. Dictyostelium has seven class I myosins, six of these have tails (Myo1A-F) while one has a very short tail (Myo1K), and three of them (Myo1D, Myo1E and Myo1F) bind PIP3 with high affinity. Localization of five Dictyostelium Class I myosins synchronizes with localization and propagation of actin waves. Myo1B and Myo1C colocalize with actin in actin waves, whereas Myo1D, E and F localize to the PIP3-rich region surrounded by actin waves. Here, we studied the effect of overexpression of the three PIP3 specific Class I myosins on actin waves. We found that ectopic expression of the short-tail Myo1F inhibits wave formation, short-tail Myo1E has similar but weaker inhibitory effect, but long-tail Myo1D does not affect waves. A study of Myo1F mutants shows that its membrane-binding site is absolutely required for wave inhibition, but the head portion is not. The results suggest that PIP3 specificity and the presence of two membrane-binding sites are required for inhibition of actin waves, and that inhibition may be caused by crosslinking of PIP3 heads groups.

Muscle myosins form folded monomers, dimers, and tetramers during filament polymerization in vitro.

Muscle contraction depends on the cyclical interaction of myosin and actin filaments. Therefore, it is important to understand the mechanisms of polymerization and depolymerization of muscle myosins. Muscle myosin 2 monomers exist in two states: one with a folded tail that interacts with the heads (10S) and one with an unfolded tail (6S). It has been thought that only unfolded monomers assemble into bipolar and side-polar (smooth muscle myosin) filaments. We now show by electron microscopy that, after 4 s of polymerization in vitro in both the presence (smooth muscle myosin) and absence of ATP, skeletal, cardiac, and smooth muscle myosins form tail-folded monomers without tail-head interaction, tail-folded antiparallel dimers, tail-folded antiparallel tetramers, unfolded bipolar tetramers, and small filaments. After 4 h, the myosins form thick bipolar and, for smooth muscle myosin, side-polar filaments. Nonphosphorylated smooth muscle myosin polymerizes in the presence of ATP but with a higher critical concentration than in the absence of ATP and forms only bipolar filaments with bare zones. Partial depolymerization in vitro of nonphosphorylated smooth muscle myosin filaments by the addition of MgATP is the reverse of polymerization.