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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.

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.

Supplementary Figure 1 from Chromosome Transfer Induced Aneuploidy Results in Complex Dysregulation of the Cellular Transcriptome in Immortalized and Cancer Cells

Supplementary Methods from Chromosome Transfer Induced Aneuploidy Results in Complex Dysregulation of the Cellular Transcriptome in Immortalized and Cancer Cells

Supplementary Tables 1-6 from Chromosome Transfer Induced Aneuploidy Results in Complex Dysregulation of the Cellular Transcriptome in Immortalized and Cancer Cells

Chromosomal aneuploidies are observed in essentially all sporadic carcinomas. These aneuploidies result in tumor-specific patterns of genomic imbalances that are acquired early during tumorigenesis, continuously selected for and faithfully maintained in cancer cells. Although the paradigm of translocation induced oncogene activation in hematologic malignancies is firmly established, it is not known how genomic imbalances affect chromosome-specific gene expression patterns in particular and how chromosomal aneuploidy dysregulates the genetic equilibrium of cells in general. To model specific chromosomal aneuploidies in cancer cells and dissect the immediate consequences of genomic imbalances on the transcriptome, we generated artificial trisomies in a karyotypically stable diploid yet mismatch repair-deficient, colorectal cancer cell line and in telomerase immortalized, cytogenetically normal human breast epithelial cells using microcell-mediated chromosome transfer. The global consequences on gene expression levels were analyzed using cDNA arrays. Our results show that regardless of chromosome or cell type, chromosomal trisomies result in a significant increase in the average transcriptional activity of the trisomic chromosome. This increase affects the expression of numerous genes on other chromosomes as well. We therefore postulate that the genomic imbalances observed in cancer cells exert their effect through a complex pattern of transcriptional dysregulation.

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.

Background: Restricted mean survival time methods compare the areas under the Kaplan–Meier curves up to a time [Formula: see text] 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 [Formula: see text], 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.

Molecular profiling of a patient’s tumor to guide targeted treatment selection offers the potential to advance patient care by improving outcomes and minimizing toxicity (by avoiding ineffective treatments). However, current development of molecular profile (MP) panels is often based on applying institution-specific or subjective algorithms to nonrandomized patient cohorts. Consequently, obtaining reliable evidence that molecular profiling is offering clinical benefit and is ready for routine clinical practice is challenging. In particular, we discuss here the problems with interpreting for clinical utility nonrandomized studies that compare outcomes in patients treated based on their MP vs those treated with standard of care, studies that compare the progression-free survival (PFS) seen on a MP-directed treatment to the PFS seen for the same patient on a previous standard treatment (PFS ratio), and multibasket trials that evaluate the response rates of targeted therapies in specific molecularly defined subpopulations (regardless of histology). We also consider some limitations of randomized trial designs. A two-step strategy is proposed in which multiple mutation-agent pairs are tested for activity in one or more multibasket trials in the first step. The results of the first step are then used to identify promising mutation-agent pairs that are combined in a molecular panel that is then tested in the step-two strategy-design randomized clinical trial (the molecular panel–guided treatment for the selected mutations vs standard of care). This two-step strategy should allow rigorous evidence-driven identification of mutation-agent pairs that can be moved into routine clinical practice.

Designing and interpreting single-arm phase II trials of combinations of agents is challenging because it can be difficult, based on historical data, to identify levels of activity for which the combination would be worth pursuing. We identified Cancer Therapy Evaluation Program single-arm combination trials that were activated in 2008–2017 and tabulated their design characteristics and results. Positive trials were evaluated as to whether they provided credible evidence that the combination was better than its constituents. A total of 125 trials were identified, and 120 trials had results available. Twelve had designs where eligible patients were required to be resistant or refractory to all but one element of the combination. Only 17.8% of the 45 positive trials were deemed to provide credible evidence that the combination was better than its constituents. Of the 10 positive trials with observed rates 10 percentage points higher than their upper (alternative hypothesis) targets, only five were deemed to provide such credible evidence. Many trials were definitively negative, with observed clinical activity at or below their lower (null hypothesis) targets. Ideally, use of single-arm combination trials should be restricted to settings where each agent is known to have minimal monotherapy activity (and a randomized trial is infeasible). In these settings, an observed signal is attributable to synergy and thus could be used to decide whether the combination is worth pursuing. In other settings, credible evidence can still be obtained if the observed activity is much higher than expected, but experience suggests that this is a rare occurrence.

Oncology clinical trials are undergoing transformation to evaluate targeted therapies addressing a wider variety of biologically defined cancer subgroups. Multi-arm basket and umbrella trials conducted under master protocols have become more prominent mechanisms for the clinical evaluation of promising new biologically driven anti-cancer therapies that are integral to precision oncology medicine. These new trial designs permit efficient clinical evaluation of multiple therapies in a variety of histologically and biologically defined cancers. These complex trials require extensive planning and attention to many factors, including choice of biomarker assay platform, mechanism for processing clinico-pathologic and biomarker data to assign patients to substudies, and statistical design, monitoring, and analysis of substudies. Trial teams have expanded to include expertise in the interface between biology, clinical oncology, bioinformatics, and statistics. Strategies for the design, conduct, and analysis of these complex trials will continue to evolve to meet new challenges and opportunities in precision oncology medicine.

Properly designed clinical trials ensure that patients are treated safely and that the scientific objectives of the trial can be achieved in an efficient manner, so that current and future patients can benefit from the trial results. In order to address the study question, it is important for the trial protocol to describe prospectively how patients will be treated and how the resulting data will be analyzed. This chapter reviews clinical trial designs to efficiently develop and assess new treatments (phase I, phase II, phase III and phase II/III), newer precision medicine designs using biomarkers to direct treatments (so that patients will be given the treatments most likely to help them), appropriate end points for different designs (so that the results of the trial will appropriately address its clinical objectives), and interim monitoring for ongoing trials (to allow stopping trials as soon as possible for positive or negative results, while retaining the statistical validity of the trial conclusions).

Caution in Interpreting Platform Trials Multiplatform trials in which experimental groups are allowed to enter and exit the trial at different times often include a single control group in which re...

Methods for assessing whether a single biomarker is prognostic or predictive in the context of a control and experimental treatment are well known. With a panel of biomarkers, each component biomarker potentially measuring sensitivity to a different drug, it is not obvious how to extend these methods. We consider two situations, which lead to different ways of defining whether a biomarker panel is prognostic or predictive. In one, there are multiple experimental targeted treatments, each with an associated biomarker assay of the relevant target in the panel, along with a control treatment; the extension of the single-biomarker scenario to this situation is straightforward. In the other situation, there are many (nontargeted) treatments and a single assay that can be used to assess the sensitivity of the patient's tumor to the different treatments. In addition to evaluating previous approaches to this situation, we propose using regression models with varying assumptions to assess such panel biomarkers. Missing biomarker data can be problematic with the regression models, and, after demonstrating that a multiple imputation procedure does not work, we suggest a modified regression model that can accommodate some forms of missing data. We also address the notions of qualitative interactions in the biomarker panel setting.

Background: Sample size adjustment designs, which allow increasing the study sample size based on interim analysis of outcome data from a randomized clinical trial, have been increasingly promoted in the biostatistical literature. Although it is recognized that group sequential designs can be at least as efficient as sample size adjustment designs, many authors argue that a key advantage of these designs is their flexibility; interim sample size adjustment decisions can incorporate information and business interests external to the trial. Recently, Chen et al. (Clinical Trials 2015) considered sample size adjustment applications in the time-to-event setting using a design (CDL) that limits adjustments to situations where the interim results are promising. The authors demonstrated that while CDL provides little gain in unconditional power (versus fixed-sample-size designs), there is a considerable increase in conditional power for trials in which the sample size is adjusted. Methods: In time-to-event settings, sample size adjustment allows an increase in the number of events required for the final analysis. This can be achieved by either (a) following the original study population until the additional events are observed thus focusing on the tail of the survival curves or (b) enrolling a potentially large number of additional patients thus focusing on the early differences in survival curves. We use the CDL approach to investigate performance of sample size adjustment designs in time-to-event trials. Results: Through simulations, we demonstrate that when the magnitude of the true treatment effect changes over time, interim information on the shape of the survival curves can be used to enrich the final analysis with events from the time period with the strongest treatment effect. In particular, interested parties have the ability to make the end-of-trial treatment effect larger (on average) based on decisions using interim outcome data. Furthermore, in “clinical null” cases where there is no benefit due to crossing survival curves, the sample size adjustment design is shown to increase the probability of recommending an ineffective therapy. Conclusion: Access to interim information on the shape of the survival curves may jeopardize the perceived integrity of trials using sample size adjustment designs. Therefore, given the lack of efficiency advantage over group sequential designs, sample size adjustment designs in time-to-event settings remain unjustified.

Background Durability of response is a clinically relevant dimension of the treatment effect in randomized clinical trials; it is often measured by comparing among the responders the duration of response between the treatment arms. However, since the comparison groups are defined by response (a post-randomization event), it is subject to analysis-by-responder bias, especially if the proportion of responders differs between the arms. Methods Two simple methods are developed that use tumor shrinkage measurements in order to lessen analysis-by-responder bias by generating more comparable patient subsets in the control and experimental arms of the trial. These subsets are then used to estimate between-arm differences in response duration. In the subtraction method, responding patients with the least tumor shrinkage in the treatment arm with more responders are removed from the patient subset for that arm. In the addition method, non-responding patients with the most tumor shrinkage in the treatment arm with fewer responders are added to the patient subset for that arm. In both methods, the numbers of patients subtracted or added are such that the proportion of patients in the modified patient subset is the same as the proportion of responders in the other treatment arm. Results The methods are demonstrated on a hypothetical dataset where they are shown to eliminate analysis-by-responder bias, and on two published analyses of randomized trials that compared the duration of response between the treatment arms. Conclusions The proposed methods can lessen the analysis-by-responder bias. These methods to compare duration of response between treatment arms may provide a useful exploratory analysis to measure treatment efficacy among responders.

Evaluation of new anticancer therapies in randomized clinical trials (RCTs) is typically based on comparing a new treatment with a standard one, using a time-to-event end point such as overall survival or progression-free survival (PFS). Although the statistical framework underlying the design of these RCTs is centered on formal testing of a treatment effect, methods for estimation (quantification) of the treatment benefit are also specified. Currently, log-rank statistical tests and/or proportional hazards models are commonly used for the trial design and primary analysis. These methods are optimized for treatment effects that do not change substantially over time (the proportional hazard assumption). Introduction of immunotherapeutic agents with potentially delayed treatment effects has renewed interest in statistical methods that can better accommodate general departures from proportional hazards and, particularly, a delayed treatment effect. This has led to considerable attention in, and some controversy about, appropriate statistical methodology for comparing survival curves, as demonstrated by the comments and replies on trial reports1-24 and at a Duke–US Food and Drug Administration workshop25 that offered alternatives to the standard log-rank/hazard-ratio methodology. While these new methods could be useful, as outlined in comprehensive reviews,26-30 we offer a caution about some of these methods’ limitations in translating statistical evidence into clinical evidence, both for formal treatment-effect hypothesis testing and for estimation (when used for the primary analysis).

Background: Nonadherence to treatment assignment in a noninferiority randomized trial is especially problematic because it attenuates observed differences between the treatment arms, possibly leading one to conclude erroneously that a truly inferior experimental therapy is noninferior to a standard therapy (inflated type 1 error probability). The Lachin–Foulkes adjustment is an increase in the sample size to account for random nonadherence for the design of a superiority trial with a time-to-event outcome; it has not been explored in the noninferiority trial setting nor with nonrandom nonadherence. Noninferiority trials where patients have knowledge of a personal prognostic risk score may lead to nonrandom nonadherence, as patients with a relatively high risk may be more likely to not adhere to the random assignment to the (reduced) experimental therapy, and patients with a relatively low risk score may be more likely to not adhere to the random assignment to the (more aggressive) standard therapy. Methods: We investigated via simulations the properties of the Lachin–Foulkes adjustment in the noninferiority setting. We considered nonrandom in addition to random nonadherence to the treatment assignment. For nonrandom nonadherence, we used the scenario where a risk score, potentially associated with the between-arm treatment difference, influences patients’ nonadherence. A sensitivity analysis is proposed for addressing the nonrandom nonadherence for this scenario. The noninferiority TAILORx adjuvant breast cancer trial, where eligibility was based on a genomic risk score, is used as an example throughout. Results: The Lachin–Foulkes adjustment to the sample size improves the operating characteristics of noninferiority trials with random nonadherence. However, to maintain type 1 error probability, it is critical to adjust the noninferiorty margin as well as the sample size. With nonrandom nonadherence that is associated with a prognostic risk score, the type 1 error probability of the Lachin–Foulkes adjustment can be inflated (e.g. doubled) when the nonadherence is larger in the experimental arm than the standard arm. The proposed sensitivity analysis lessens the inflation in this situation. Conclusion: The Lachin–Foulkes adjustment to the sample size and noninferiority margin is a useful simple technique for attenuating the effects of random nonadherence in the noninferiority setting. With nonrandom nonadherence associated with a risk score known to the patients, the type 1 error probability can be inflated in certain situations. A proposed sensitivity analysis for these situations can attenuate the inflation.

Recent advances in biotechnology and cancer genomics have afforded enormous opportunities for development of more effective anticancer therapies. A key thrust of this modern drug development paradigm is successful identification of predictive biomarkers that can distinguish patients who might be sensitive to new targeted therapies. To respond to this challenge, a number of phase III cancer trial designs integrating biomarker-based objectives have been proposed and implemented in oncology drug development. In this article, we provide an updated review of commonly used biomarker-based randomized clinical trial designs, with a particular focus on design efficiency. When the efficacy of a new therapy may be limited to a biomarker-defined subgroup, the choice of an appropriate randomized clinical trial design should be guided by the strength of the biomarker’s credentials. If compelling evidence indicates that a targeted therapy is beneficial only in a particular biomarker-defined subgroup, an enrichment design should be used. If there is strong evidence that the treatment is likely to be more beneficial in the biomarker-positive patients but a meaningful benefit is also possible in the biomarker-negative patients, then a properly powered biomarker-stratified design (eg, a subgroup-specific or Marker Sequential Test strategy) would provide the most rigorous determination of the sensitive populations. If the evidence supporting the predictive value of the biomarker is weak and the treatment is expected to work in the overall population, then a fallback design could be used. Careful selection of an appropriate phase III design strategy that integrates evaluation of a new anticancer therapy and its companion diagnostic is critical to the success of precision medicine in oncology.

Background: Futility (inefficacy) interim monitoring is an important component in the conduct of phase III clinical trials, especially in life-threatening diseases. Desirable futility monitoring guidelines allow timely stopping if the new therapy is harmful or if it is unlikely to demonstrate to be sufficiently effective if the trial were to continue to its final analysis. There are a number of analytical approaches that are used to construct futility monitoring boundaries. The most common approaches are based on conditional power, sequential testing of the alternative hypothesis, or sequential confidence intervals. The resulting futility boundaries vary considerably with respect to the level of evidence required for recommending stopping the study. Purpose: We evaluate the performance of commonly used methods using event histories from completed phase III clinical trials of the Radiation Therapy Oncology Group, Cancer and Leukemia Group B, and North Central Cancer Treatment Group. Methods: We considered published superiority phase III trials with survival endpoints initiated after 1990. There are 52 studies available for this analysis from different disease sites. Total sample size and maximum number of events (statistical information) for each study were calculated using protocol-specified effect size, type I and type II error rates. In addition to the common futility approaches, we considered a recently proposed linear inefficacy boundary approach with an early harm look followed by several lack-of-efficacy analyses. For each futility approach, interim test statistics were generated for three schedules with different analysis frequency, and early stopping was recommended if the interim result crossed a futility stopping boundary. For trials not demonstrating superiority, the impact of each rule is summarized as savings on sample size, study duration, and information time scales. Results: For negative studies, our results show that the futility approaches based on testing the alternative hypothesis and repeated confidence interval rules yielded less savings (compared to the other two rules). These boundaries are too conservative, especially during the first half of the study (50% of information) and may stop a trial even when there is a clinically meaningful treatment effect. The linear inefficacy boundary with three or more interim analyses provided the best results. For positive studies, we demonstrated that none of the futility rules would have stopped the trials. Conclusion: The linear inefficacy boundary futility approach is attractive from statistical, clinical, and logistical standpoints in clinical trials evaluating new anti-cancer agents.

Background/aims Factorial analyses of 2 × 2 trial designs are known to be problematic unless one can be sure that there is no interaction between the treatments ( A and B). Instead, we consider non-factorial analyses of a factorial trial design that addresses clinically relevant questions of interest without any assumptions on the interaction. Primary questions of interest are as follows: (1) is A better than the control treatment C, (2) is B better than C, (3) is the combination of A and B ( AB) better than C, and (4) is AB better than A, B, and C. Methods A simple three-step procedure that tests the first three primary questions of interest using a Bonferroni adjustment at the first step is proposed. A Hochberg procedure on the four primary questions is also considered. The two procedures are evaluated and compared in limited simulations. Published results from three completed trials with factorial designs are re-evaluated using the two procedures. Results Both suggested procedures (that answer multiple questions) require a 50%–60% increase in per arm sample size over a two-arm design asking a single question. The simulations suggest a slight advantage to the three-step procedure in terms of power (for the primary and secondary questions). The proposed procedures would have formally addressed the questions arising in the highlighted published trials arguably more simply than the pre-specified factorial analyses used. Conclusion Factorial trial designs are an efficient way to evaluate two treatments, alone and in combination. In situations where a statistical interaction between the treatment effects cannot be assumed to be 0, simple non-factorial analyses are possible that directly assess the questions of interest without the zero interaction assumption.

Background With the development of targeted agents, the approach to combination cancer therapy has evolved to focus on identifying ways in which pathway inhibition by one agent may enhance the activity of other agents. In theory, this implies that under this new paradigm, agents are no longer required to show single-agent activity, as the pathway inhibited by the targeted agent may only have a therapeutic effect when given with other agents. This raises the question of the extent to which anticancer agents without single-agent activity can contribute to effective combination regimens. Patients and methods We reviewed outcomes of randomised phase 2 combination trials sponsored by the National Cancer Institute Cancer Therapy Evaluation Program that were activated in 2008 to 2017 and noted the single-agent activity of the experimental agents. Results Fifty-three trials were identified, and 50 had available results: 7 (14%), 15 (30%) and 28 (56%) had experimental agents with single-agent activity classified as active, inactive and indeterminate, respectively. Thirteen per cent (95% CI=1.7% to 40.5%) of trials evaluating inactive agents and 11.6% (95% CI=3.9% to 25.1%) of trials evaluating agents without known single-agent activity (pooled inactive and indeterminate) were positive, compared with 42.9% (95% CI=9.9% to 81.6%) for agents with single-agent activity. Conclusions Incorporating agents without documented single-agent activity into treatment regimens is unlikely to produce meaningful improvements in activity unless there is compelling biological rationale. This finding has important implications for the prioritisation of anticancer agents for combination testing, and for the allocation of clinical trial resources.

Purpose Introduction of new immune therapies that may have a delayed beneficial effect necessitates re-evaluation of traditional clinical trial designs in oncology. A key design feature of randomized trials is interim futility monitoring, which allows stopping early if the accruing data convincingly demonstrate that the experimental treatment is detrimental or is unlikely to be shown superior to the standard treatment. The appropriateness of futility monitoring is frequently questioned when the effect of the experimental treatment may be delayed (eg, in trials of many immune agents). We examine the advisability of using futility monitoring when there is potential for a delayed treatment effect and make recommendations concerning its use. Methods We evaluated the loss of statistical power when using some common futility interim monitoring rules and a new proposed conservative rule via simulation under varying amounts of treatment-effect delay and varying accrual periods. We also considered scenarios where the experimental treatment starts out being worse than the standard treatment but ends up being better, as may sometimes be the case when an immune therapy is compared with an active standard therapy. Results Some standard methods of futility monitoring can result in an unacceptable loss of power when there is a delayed treatment effect, especially if the accrual period is rapid or the experimental treatment is initially worse. The proposed conservative futility rule has a negligible loss of power in the situations considered. Conclusion Although care must be taken with the choice of futility monitoring when there is a delayed treatment effect, inclusion of appropriate rules can reduce the exposure of patients to ineffective therapies without reducing the probability of correctly identifying effective treatments.

On March 28- 29, 2017, the National Cancer Institute (NCI) Thoracic Malignacy Steering Committee, International Association for the Study of Lung Cancer, and Mesothelioma Applied Research Foundation convened the NCI-International Association for the Study of Lung Cancer- Mesothelioma Applied Research Foundation Mesothelioma Clinical Trials Planning Meeting in Bethesda, Maryland. The goal of the meeting was to bring together lead academicians, clinicians, scientists, and the U.S. Food and Drug Administration to focus on the development of clinical trials for patients in whom malignant pleural mesothelioma has been diagnosed. In light of the discovery of new cancer targets affecting the clinical development of novel agents and immunotherapies in malignant mesothelioma, the objective of this meeting was to assemble a consensus on at least two or three practice-changing multimodality clinical trials to be conducted through NCI's National Clinical Trials Network.

Establishing trial-level surrogacy of an intermediate endpoint for predicting survival benefit in future trials is extremely challenging because of the extrapolations required, but there are other useful drug development and patient management applications of intermediate endpoints. Clin Cancer Res; 24(10); 2239–40. ©2018 AACR. See related article by Mushti et al., p. 2268

The comparison of overall survival curves between treatment arms will always be of interest in a randomized clinical trial involving a life-shortening disease. In some settings, the experimental treatment is only expected to affect the deaths caused by the disease, and the proportion of deaths caused by the disease is relatively low. In these settings, the ability to assess treatment-effect differences between Kaplan-Meier survival curves can be hampered by the large proportion of deaths in both arms that are unrelated to the disease. To address this problem, frequently displayed are cause-specific survival curves or cumulative incidence curves, which respectively censor and immortalize events (deaths) not caused by the disease. However, the differences between the experimental and control treatment arms for these curves overestimate the difference between the overall survival curves for the treatment arms, and thus could result in overestimation of the benefit of the experimental treatment for the patients. To address this issue, we propose new estimators of overall survival for the treatment arms that are appropriate when the treatment does not affect the non-disease related deaths. These new estimators give a more precise estimate of the treatment benefit, potentially enabling future patients to make a more informed decision concerning treatment choice. We also consider the case where an exponential assumption allows the simple presentation of mortality rates as the outcome measures. Applications are given for estimating overall survival in a prostate-cancer treatment randomized clinical trial, and for estimating the overall mortality rates in a prostate-cancer screening trial.

For a fallback randomized clinical trial design with a marker, Choai and Matsui (2015, Biometrics 71, 25-32) estimate the bias of the estimator of the treatment effect in the marker-positive subgroup conditional on the treatment effect not being statistically significant in the overall population. This is used to construct and examine conditionally bias-corrected estimators of the treatment effect for the marker-positive subgroup. We argue that it may not be appropriate to correct for conditional bias in this setting. Instead, we consider the unconditional bias of estimators of the treatment effect for marker-positive patients.

Factorial 2 × 2 designs can be used to combine evaluation of two treatments in a single study. The standard analysis approach is based on a factorial analysis that evaluates each treatment by pooling data over the other treatment. This approach relies on the assumption that the effect of each treatment is not substantially affected by the other treatment. In many oncology settings, this no-interaction assumption cannot be adequately supported at the time the trial is designed. In this Commentary, we consider current practices for the design and analysis of factorial trials by performing a survey of factorial treatment trials published in the Journal of the National Cancer Institute, Journal of Clinical Oncology, and the New England Journal of Medicine (2007-2016). The protocol-specified sample size was derived based on the factorial (pooled) analysis in 96.7% of the 30 identified trials, and the factorial analysis was specified as the primary analysis in 90.0% of these identified trials. An interaction complicating study interpretation was reported in 16.7% of the trials. We provide recommendations for matching the trial analysis and design to the study goals to account for possible interaction and illustrate the recommendations on the data from several published trials.

There is a wide range of adaptive elements of clinical trial design (some old and some new), with differing advantages and disadvantages. Classical interim monitoring, which adapts the design based on early evidence of superiority or futility of a treatment arm, has long been known to be extremely useful. A more recent application of interim monitoring is in the use of phase II/III designs, which can be very effective (especially in the setting of multiple experimental treatments and a reliable intermediate end point) but do have the cost of having to commit earlier to the phase III question than if separate phase II and phase III trials were performed. Outcome-adaptive randomization is an older technique that has recently regained attention; it increases trial complexity and duration without offering substantial benefits to the patients in the trial. The use of adaptive trials with biomarkers is new and has great potential for efficiently identifying patients who will be helped most by specific treatments. Master protocols in which trial arms and treatment questions are added to an ongoing trial can be especially efficient in the biomarker setting, where patients are screened for entry into different subtrials based on evolving knowledge about targeted therapies. A discussion of three recent adaptive clinical trials (BATTLE-2, I-SPY 2, and FOCUS4) highlights the issues.

The promise of precision medicine will only be fully realized if the research community can adapt its clinical trials methodology to study molecularly characterized tumors instead of the traditional histologic classification. Such trials will depend on adequate tissue collection, availability of quality controlled, high throughput molecular assays, and the ability to screen large numbers of tumors to find those with the desired molecular alterations. The National Cancer Institute's (NCI) new National Clinical Trials Network (NCTN) is well positioned to conduct such trials. The NCTN has the ability to seamlessly perform ethics review, register patients, manage data, and deliver investigational drugs across its many sites including both in cities and rural communities, academic centers, and private practices. The initial set of trials will focus on different questions: (1) Exceptional Responders Initiative—why do a minority of patients with solid tumors or lymphoma respond very well to some drugs even if the majority do not?; (2) NCI MATCH trial—can molecular markers predict response to targeted therapies in patients with advanced cancer resistant to standard treatment?; (3) ALCHEMIST trial—will targeted epidermal growth factor receptor ( EGFR) and anaplastic lymphoma kinase ( ALK) inhibitors improve survival for adenocarcinoma of the lung in the adjuvant setting?; and (4) Lung Cancer Master Protocol trial for advanced squamous cell lung cancer—is there an advantage to developing drugs for small subsets of molecularly characterized tumors in a single, multiarm trial design? These studies will hopefully spawn a new era of treatment trials that will carefully select the tumors that may respond best to investigational therapy.

The use of biomarkers to identify patients who can benefit from treatment with a specific anticancer agent has the potential to both improve patient care and accelerate drug development. The development of targeted agents and their accompanying biomarkers frequently occurs contemporaneously, and confidence in a putative biomarker's performance might, therefore, be insufficient to restrict the definitive testing of a new agent to the subgroup of biomarker-positive patients. This Review considers which clinical trial designs and analysis strategies are appropriate for use in phase III, biomarker-driven, randomized clinical trials, on the basis of pre-existing evidence that the biomarker can successfully identify patients who will respond to the treatment in question. The types of interim monitoring that are appropriate for these trials are also discussed. In addition, enrichment strategies based on the use of prognostic biomarkers to separate a population into subgroups with better and worse outcomes, regardless of treatment, are described. Finally, the possibility of formally using a biomarker during phase II drug development, to select what type of biomarker-driven strategy should be used in the phase III trial, is discussed.

Background: Interim monitoring is a key component of randomized clinical trial design from both ethical and efficiency perspectives. In studies with time-to-event endpoints, timing of interim analyses is typically based on observing a pre-specified proportion of the total number of events required for the final analysis. While most randomized clinical trial designs pool events over the experimental and control arms in determining the analysis times, some designs use only the control-arm events for scheduling interim looks. Purpose: To evaluate the performance of the pooled and control-arm-based interim monitoring approaches and to propose a new procedure, the earliest information time procedure, that combines the benefits of the two approaches. Methods: The analytical and logistical considerations for the procedures are presented. The methodology is illustrated on data from three published randomized clinical trials. The procedures are compared in a simulation study. Results: The control-arm approach results in a slight inflation of the study type I error in one-sided randomized clinical trial designs. When the new treatment is no better than the control treatment, the pooled-arm approach results in, on average, earlier stopping times than the control-arm approach. When the new treatment works exceptionally well, the average stopping times under the control-arm approach are earlier than those under the pooled approach. The proposed earliest information time procedure is shown to result in stopping times corresponding to the best (earliest) of the two approaches over the entire range of alternatives. Limitations: The earliest information time procedure may result in a slight inflation of the type I error (especially in small trials); when exact control of the type I error is required, it is necessary to use a simulation-based method to correct the inflation. Conclusion: In time-to-event settings, the earliest information time procedure is an attractive alternative to the pooled and control-arm approaches. Improving the timing of interim analyses helps to minimize patient exposure to inferior treatments and to accelerate dissemination of the study results.

We review how overall survival (OS) comparisons should be interpreted with increasing availability of effective therapies that can be given subsequently to the treatment assigned in a randomized clinical trial (RCT). We examine in detail how effective subsequent therapies influence OS comparisons under varying conditions in RCTs. A subsequent therapy given after tumor progression (or relapse) in an RCT that works better in the standard arm than the experimental arm will lead to a smaller OS difference (possibly no difference) than one would see if the subsequent therapy was not available. Subsequent treatments that are equally effective in the treatment arms would not be expected to affect the absolute OS benefit of the experimental treatment but will make the relative improvement in OS smaller. In trials in which control arm patients cross over to the experimental treatment after their condition worsens, a smaller OS difference could be observed than one would see without cross-overs. In particular, use of cross-over designs in the first definitive evaluation of a new agent in a given disease compromises the ability to assess clinical benefit. In disease settings in which there is not an intermediate end point that directly measures clinical benefit, OS should be the primary end point of an RCT. The observed difference in OS should be considered the measure of clinical benefit to the patients, regardless of subsequent therapies, provided that the subsequent therapies used in both treatment arms follow the current standard of care.

When data consist of clusters of potentially correlated observations then conditional logistic regression can be used to estimate the association between a binary outcome and covariates conditionally on the cluster effects. Surveys can use multistage sampling with potentially differential probabilities of sampling individuals from the same conditioning cluster (e.g., family). We show that conditional logistic regression of survey data using standard inflation (weighted) estimation (i.e., observations are weighted by the inverse of their inclusion probabilities) can result in biased estimators when the sample sizes of the observations sampled from the conditioning clusters are small. We propose using methods based on weighted pseudo-pairwise likelihoods that combine the conditional logistic likelihoods for all pairs of observations consisting of a positive and a negative outcome within a conditioning cluster and weights the pairwise likelihoods by the inverse of their joint inclusion probabilities within t...