A basket trial design based on constrained hierarchical Bayesian model for latent subgroups.

It is well known a basket trial consisting of multiple cancer types has the potential of borrowing strength across the baskets defined by the cancer types, leading to an efficient design in terms of sample size and trial duration. The treatment effects in those baskets are often heterogeneous and categorized by the cancer types being sensitive or insensitive to the treatment. Hence, the assumption of exchangeability in many existing basket trials may be violated, and there is a need to design trials without this assumption. In this paper, we simplify the constrained hierarchical Bayesian model for latent subgroups (CHBM-LS) for two classifiers to deal with the potential heterogeneity of treatment effects due to the single classifier of the cancer type. Different baskets are aggregated into subgroups using a latent subgroup modeling approach. The treatment effects are similar and exchangeable to facilitate information borrowing within each latent subgroup. Applying the simplified CHBM-LS approach to the real basket trials where baskets defined by only cancer types shows better performance than other available approaches. Further simulation study also demonstrates this CHBM-LS approach outperforms other approaches with higher statistical power and better-controlled type I error rates under various scenarios.

Comparison Between Simultaneous and Sequential Utilization of Safety and Efficacy for Optimal Dose Determination in Bayesian Model-Assisted Designs.

It has become quite common in recent early oncology trials to include both the dose-finding and the dose-expansion parts within the same study. This shift can be viewed as a seamless way of conducting the trials to obtain information on safety and efficacy hence identifying an optimal dose (OD) rather than just the maximum tolerated dose (MTD). One approach is to conduct a dose-finding part based solely on toxicity outcomes, followed by a dose expansion part to evaluate efficacy outcomes. Another approach employs only the dose-finding part, where the dose-finding decisions are made utilizing both the efficacy and toxicity outcomes of those enrolled patients. In this paper, we compared the two approaches through simulation studies under various realistic settings. The percentage of correct ODs selection, the average number of patients allocated to the ODs, and the average trial duration are reported in choosing the appropriate designs for their early-stage dose-finding trials, including expansion cohorts.

An adaptive biomarker basket design in phase II oncology trials.

The phase II basket trial in oncology is a novel design that enables the simultaneous assessment of treatment effects of one anti-cancer targeted agent in multiple cancer types. Biomarkers could potentially associate with the clinical outcomes and re-define clinically meaningful treatment effects. It is therefore natural to develop a biomarker-based basket design to allow the prospective enrichment of the trials with the adaptive selection of the biomarker-positive (BM+) subjects who are most sensitive to the experimental treatment. We propose a two-stage phase II adaptive biomarker basket (ABB) design based on a potential predictive biomarker measured on a continuous scale. At Stage 1, the design incorporates a biomarker cutoff estimation procedure via a hierarchical Bayesian model with biomarker as a covariate (HBMbc). At Stage 2, the design enrolls only BM+ subjects, defined as those with the biomarker values exceeding the biomarker cutoff within each cancer type, and subsequently assesses the early efficacy and/or futility stopping through the pre-defined interim analyses. At the end of the trial, the response rate of all BM+ subjects for each cancer type can guide drug development, while the data from all subjects can be used to further model the relationship between the biomarker value and the clinical outcome for potential future research. The extensive simulation studies show that the ABB design could produce a good estimate of the biomarker cutoff to select BM+ subjects with high accuracy and could outperform the existing phase II basket biomarker cutoff design under various scenarios.

Constrained hierarchical Bayesian model for latent subgroups in basket trials with two classifiers.

The basket trial in oncology is a novel clinical trial design that enables the simultaneous assessment of one treatment in multiple cancer types. In addition to the usual basket classifier of the cancer types, many recent basket trials further contain other classifiers like biomarkers that potentially affect the clinical outcomes. In other words, the treatment effects in those baskets are often categorized by not only the cancer types but also the levels of other classifiers. Therefore, the assumption of exchangeability is often violated when some baskets are more sensitive to the targeted treatment, whereas others are less. In this article, we propose a constrained hierarchical Bayesian model for latent subgroups (CHBM-LS) to deal with potential heterogeneity of treatment effects due to both the cancer type (first classifier) and another classifier (second classifier) in basket trials. Different baskets defined by multiple cancer types and multiple levels of the second classifier are aggregated into subgroups using a latent subgroup modeling approach. Within each latent subgroup, the treatment effects are similar and approximately exchangeable to borrow information. The CHBM-LS approach evaluates the treatment effect for each basket while allowing adaptive information borrowing across the baskets by identifying latent subgroups. The simulation study shows that the CHBM-LS approach outperforms other approaches with higher statistical power and better-controlled type I error rates under various scenarios with heterogeneous treatment effects across baskets.

TITE-gBOIN: Time-to-event Bayesian optimal interval design to accelerate dose-finding accounting for toxicity grades.

The new therapeutic agents, such as molecular targeted agents and immuno-oncology therapies, appear more likely to induce multiple toxicities at different grades than dose-limiting toxicities defined in traditional dose-finding trials. In addition, it is often challenging to make adaptive decisions on dose escalation and de-escalation on time because of the fast accrual rate and/or the late-onset toxicity outcomes, causing the potential suspension of the enrollment and the delay of the trials. To address these issues, we propose a time-to-event Bayesian optimal interval design to accelerate the dose-finding process utilizing toxicity grades based on both cumulative and pending toxicity outcomes. The proposed design, named "TITE-gBOIN" design, is a nonparametric and model-assisted design and has the virtues of robustness, simplicity and straightforward to implement in actual oncology dose-finding trials. A simulation study shows that the TITE-gBOIN design has a higher probability of selecting the MTDs correctly and allocating more patients to the MTDs across various realistic settings while reducing the trial duration significantly, therefore can accelerate early-stage dose-finding trials.

Study design of single-arm phase II immunotherapy trials with long-term survivors and random delayed treatment effect.

In the traditional study design of a single-arm phase II cancer clinical trial, the one-sample log-rank test has been frequently used. A common practice in sample size calculation is to assume that the event time in the new treatment follows exponential distribution. Such a study design may not be suitable for immunotherapy cancer trials, when both long-term survivors (or even cured patients from the disease) and delayed treatment effect are present, because exponential distribution is not appropriate to describe such data and consequently could lead to severely underpowered trial. In this research, we proposed a piecewise proportional hazards cure rate model with random delayed treatment effect to design single-arm phase II immunotherapy cancer trials. To improve test power, we proposed a new weighted one-sample log-rank test and provided a sample size calculation formula for designing trials. Our simulation study showed that the proposed log-rank test performs well and is robust of misspecified weight and the sample size calculation formula also performs well.

Estimation of delay time in survival data with delayed treatment effect.

In randomized controlled trials with delayed treatment effect, there is a delay period before the experimental therapy starts to exhibit a beneficial effect. The phenomenon of delayed treatment effect is often observed in the emerging and important field of immuno-oncology. It is important to estimate the duration of delay as this information helps in characterizing the pattern of comparative treatment effect, understanding the mechanism of action of the experimental therapy, and forming optimal treatment strategies. For a fixed delay time, we propose a maximum likelihood estimator and evaluate its asymptotic properties via simulation. We further evaluate two functions that link the pre- and postdelay hazard ratios to the average hazard ratio given a fixed delay time. For the case of random delay time, where the delay time may vary from patient to patient, we propose a semiparametric joint survival model for delay time and event time to estimate the mean delay time and the postdelay hazard ratio, assuming a Beta distribution for the delay time. We describe an extension of the model to estimate subgroup-specific mean delay times. Simulation study and application to data from a clinical trial in colon cancer patients demonstrate the robustness of the proposed model.

Weighted log-rank test for time-to-event data in immunotherapy trials with random delayed treatment effect and cure rate.

A cancer clinical trial with an immunotherapy often has 2 special features, which are patients being potentially cured from the cancer and the immunotherapy starting to take clinical effect after a certain delay time. Existing testing methods may be inadequate for immunotherapy clinical trials, because they do not appropriately take the 2 features into consideration at the same time, hence have low power to detect the true treatment effect. In this paper, we proposed a piece-wise proportional hazards cure rate model with a random delay time to fit data, and a new weighted log-rank test to detect the treatment effect of an immunotherapy over a chemotherapy control. We showed that the proposed weight was nearly optimal under mild conditions. Our simulation study showed a substantial gain of power in the proposed test over the existing tests and robustness of the test with misspecified weight. We also introduced a sample size calculation formula to design the immunotherapy clinical trials using the proposed weighted log-rank test.

Biomarker threshold adaptive designs for survival endpoints.

Due to the importance of precision medicine, it is essential to identify the right patients for the right treatment. Biomarkers, which have been commonly used in clinical research as well as in clinical practice, can facilitate selection of patients with a good response to the treatment. In this paper, we describe a biomarker threshold adaptive design with survival endpoints. In the first stage, we determine subgroups for one or more biomarkers such that patients in these subgroups benefit the most from the new treatment. The analysis in this stage can be based on historical or pilot studies. In the second stage, we sample subjects from the subgroups determined in the first stage and randomly allocate them to the treatment or control group. Extensive simulation studies are conducted to examine the performance of the proposed design. Application to a real data example is provided for implementation of the first-stage algorithms.

Pattern mixture models for clinical validation of biomarkers in the presence of missing data.

Targeted therapies for cancers are sometimes only effective in a subset of patients with a particular biomarker status. In clinical development, the biomarker status is typically determined by an investigational-use-only/laboratory-developed test. A market ready test (MRT) is developed later to meet regulatory requirements and for future commercial use. In the USA, the clinical validation of MRT showing efficacy and safety profile of the targeted therapy in the biomarker subgroups determined by MRT is needed for pre-market approval. One of the major challenges in carrying out clinical validation is that the biomarker status per MRT is often missing for many subjects. In this paper, we treat biomarker status as a missing covariate and develop a novel pattern mixture model in the setting of a proportional hazards model for the time-to-event outcome variable. We specify a multinomial regression model for the missing biomarker statuses, and develop an expectation-maximization algorithm by the Method of Weights (Ibrahim, Journal of the American Statistical Association, 1990) to estimate the parameters in the regression model. We use Louis' formula (Louis, Journal of the Royal Statistical Society. Series B, 1982) to obtain standard errors estimates. We examine the performance of our method in extensive simulation studies and apply our method to a clinical trial in metastatic colorectal cancer. Copyright © 2017 John Wiley & Sons, Ltd.

Statistical design of noninferiority multiple region clinical trials to assess global and consistent treatment effects.

Noninferiority multiregional clinical trials (MRCTs) have recently received increasing attention in drug development. While a major goal in an MRCT is to estimate the global treatment effect, it is also important to assess the consistency of treatment effects across multiple regions. In this paper, we propose an intuitive definition of consistency of noninferior treatment effects across regions under the random-effects modeling framework. Specifically, we quantify the consistency of treatment effects by the percentage of regions that meet a predefined treatment margin. This new approach enables us to achieve both goals in one modeling framework. We propose to use a signed likelihood ratio test for testing the global treatment effect and the consistency of noninferior treatment effects. In addition, we provide guidelines for the allocation rule to achieve optimal power for testing consistency among multiple regions. Extensive simulation studies are conducted to examine the performance of the proposed methodology. An application to a real data example is provided.

Flexible combination of multiple diagnostic biomarkers to improve diagnostic accuracy.

BACKGROUND: In medical research, it is common to collect information of multiple continuous biomarkers to improve the accuracy of diagnostic tests. Combining the measurements of these biomarkers into one single score is a popular practice to integrate the collected information, where the accuracy of the resultant diagnostic test is usually improved. To measure the accuracy of a diagnostic test, the Youden index has been widely used in literature. Various parametric and nonparametric methods have been proposed to linearly combine biomarkers so that the corresponding Youden index can be optimized. Yet there seems to be little justification of enforcing such a linear combination. METHODS: This paper proposes a flexible approach that allows both linear and nonlinear combinations of biomarkers. The proposed approach formulates the problem in a large margin classification framework, where the combination function is embedded in a flexible reproducing kernel Hilbert space. RESULTS: Advantages of the proposed approach are demonstrated in a variety of simulated experiments as well as a real application to a liver disorder study. CONCLUSION: Linear combination of multiple diagnostic biomarkers are widely used without proper justification. Additional research on flexible framework allowing both linear and nonlinear combinations is in need.

Extended RAS analysis for anti-epidermal growth factor therapy in patients with metastatic colorectal cancer.

RAS family proteins (including KRAS and NRAS) play important roles in the epidermal growth factor receptor (EGFR) signaling pathway. Mutations in RAS genes (occurring at loci in exons 2, 3, and 4) often result in constitutive activation of RAS proteins and persistent downstream signaling. Mutations in KRAS exon 2 (codon 12/13) are an established predictor of lack of response to the anti-EGFR monoclonal antibodies cetuximab and panitumumab in patients with metastatic colorectal cancer (mCRC), and have been used routinely in clinical practice to identify patients unlikely to derive benefit from these therapies. However, a meaningful proportion of patients with mCRC have tumors bearing other mutations in RAS genes. Recent studies have demonstrated that evaluation of an extended panel of RAS mutations­including mutations in KRAS exon 2, 3, and 4 and NRAS exons 2, 3, and 4­can better define the patient population that is unlikely to benefit from anti-EGFR therapy, with concomitant improvements in outcomes in the more highly selected RAS wild-type group. This discovery has changed the practice of oncology and has the potential to spare patients from exposure to ineffective therapy. In the near future, it is important for the oncology community to validate extended RAS analysis assays and make certain that patients who are candidates for anti-EGFR therapy undergo appropriate testing and treatment.

Panitumumab-FOLFOX4 treatment and RAS mutations in colorectal cancer.

BACKGROUND: Patients with metastatic colorectal cancer that harbors KRAS mutations in exon 2 do not benefit from anti-epidermal growth factor receptor (EGFR) therapy. Other activating RAS mutations may also be negative predictive biomarkers for anti-EGFR therapy. METHODS: In this prospective-retrospective analysis, we assessed the efficacy and safety of panitumumab plus oxaliplatin, fluorouracil, and leucovorin (FOLFOX4) as compared with FOLFOX4 alone, according to RAS (KRAS or NRAS) or BRAF mutation status. A total of 639 patients who had metastatic colorectal cancer without KRAS mutations in exon 2 had results for at least one of the following: KRAS exon 3 or 4; NRAS exon 2, 3, or 4; or BRAF exon 15. The overall rate of ascertainment of RAS status was 90%. RESULTS: Among 512 patients without RAS mutations, progression-free survival was 10.1 months with panitumumab-FOLFOX4 versus 7.9 months with FOLFOX4 alone (hazard ratio for progression or death with combination therapy, 0.72; 95% confidence interval [CI], 0.58 to 0.90; P=0.004). Overall survival was 26.0 months in the panitumumab-FOLFOX4 group versus 20.2 months in the FOLFOX4-alone group (hazard ratio for death, 0.78; 95% CI, 0.62 to 0.99; P=0.04). A total of 108 patients (17%) with nonmutated KRAS exon 2 had other RAS mutations. These mutations were associated with inferior progression-free survival and overall survival with panitumumab-FOLFOX4 treatment, which was consistent with the findings in patients with KRAS mutations in exon 2. BRAF mutations were a negative prognostic factor. No new safety signals were identified. CONCLUSIONS: Additional RAS mutations predicted a lack of response in patients who received panitumumab-FOLFOX4. In patients who had metastatic colorectal cancer without RAS mutations, improvements in overall survival were observed with panitumumab-FOLFOX4 therapy. (Funded by Amgen and others; PRIME ClinicalTrials.gov number, NCT00364013.).

Evaluation of progression-free survival as a surrogate endpoint for survival in chemotherapy and targeted agent metastatic colorectal cancer trials.

Pooled analyses of chemotherapy trials in metastatic colorectal cancer (mCRC) have suggested that progression-free survival (PFS) is a surrogate endpoint for overall survival (OS). However, this has not been evaluated under current standard-of-care regimens of chemotherapy in combination with targeted therapies. We conducted an analysis of published mCRC trials of chemotherapy and targeted therapies from 2000 to evaluate the surrogacy of PFS and response rate (RR) for OS. Study-level data was pooled from 24 randomized mCRC trials that evaluated fluoropyrimidine-based regimens and included trials conducted with targeted agents (panitumumab, cetuximab, bevacizumab, and aflibercept). A total of 69 treatment arms with a sample size of 20,438 patients was included. Linear regression analysis was carried out to estimate the correlation of PFS and RR with OS. The correlation coefficient between PFS HRs and OS HRs was 0.86 for all trials, 0.89 for 12 phase III trials of targeted agents in combination with chemotherapy, 0.95 for 8 first-line phase III trials of targeted agents, and 0.83 for 9 trials of anti-EGFR-targeted agents. In all cases, correlation coefficients between RR and OS HRs were lower than those between PFS HRs and OS HRs (range, 0.42-0.81). In this study-level analysis of randomized mCRC trials of chemotherapy and targeted agents, improvements in PFS are strongly correlated with improvements in OS. This suggests that PFS remains a valid surrogate endpoint for OS with current treatment regimens in the mCRC setting.

Randomized phase III study of panitumumab with fluorouracil, leucovorin, and irinotecan (FOLFIRI) compared with FOLFIRI alone as second-line treatment in patients with metastatic colorectal cancer.

PURPOSE: Panitumumab is a fully human anti-epidermal growth factor receptor (EGFR) monoclonal antibody that improves progression-free survival (PFS) in chemotherapy-refractory metastatic colorectal cancer (mCRC). This trial evaluated the efficacy and safety of panitumumab plus fluorouracil, leucovorin, and irinotecan (FOLFIRI) compared with FOLFIRI alone after failure of initial treatment for mCRC by tumor KRAS status. PATIENTS AND METHODS: Patients with mCRC, one prior chemotherapy regimen for mCRC, Eastern Cooperative Oncology Group performance status 0 to 2, and available tumor tissue for biomarker testing were randomly assigned 1:1 to panitumumab 6.0 mg/kg plus FOLFIRI versus FOLFIRI every 2 weeks. The coprimary end points of PFS and overall survival (OS) were independently tested and prospectively analyzed by KRAS status. RESULTS: From June 2006 to March 2008, 1,186 patients were randomly assigned 1:1 and received treatment. KRAS status was available for 91% of patients: 597 (55%) with wild-type (WT) KRAS tumors, and 486 (45%) with mutant (MT) KRAS tumors. In the WT KRAS subpopulation, when panitumumab was added to chemotherapy, a significant improvement in PFS was observed (hazard ratio [HR] = 0.73; 95% CI, 0.59 to 0.90; P = .004); median PFS was 5.9 months for panitumumab-FOLFIRI versus 3.9 months for FOLFIRI. A nonsignificant trend toward increased OS was observed; median OS was 14.5 months versus 12.5 months, respectively (HR = 0.85, 95% CI, 0.70 to 1.04; P = .12); response rate was improved to 35% versus 10% with the addition of panitumumab. In patients with MT KRAS, there was no difference in efficacy. Adverse event rates were generally comparable across arms with the exception of known toxicities associated with anti-EGFR therapy. CONCLUSION: Panitumumab plus FOLFIRI significantly improved PFS and is well-tolerated as second-line treatment in patients with WT KRAS mCRC.