Due to the high cost and high failure rate of Phase III trials where a classical group sequential design (GSD) is usually used, seamless Phase II/III designs are more and more popular to improve ...trial efficiency. A potential attraction of Phase II/III design is to allow a randomized proof-of-concept stage prior to committing to the full cost of a Phase III trial. Population selection during the trial allows a trial to adapt and focus investment where it is most likely to provide patient benefit. Previous methods have been developed for this problem when there is a single primary endpoint and two possible populations.
To find the population that potentially benefits with one or two primary endpoints (e.g., progression free survival (PFS), overall survival (OS)), we propose a gated group sequential design for a seamless Phase II/III trial design with adaptive population selection.
The investigated design controls the familywise error rate and allows multiple interim analyses to enable early stopping for efficacy or futility. Simulations and an illustrative example suggest that the proposed gated group sequential design has more power and requires less time and resources compared to the group sequential design and adaptive design.
Combining the group sequential design and adaptive design, the gated group sequential design has more power and higher efficiency while controlling for the familywise error rate. It has the potential to save drug development cost and more quickly fulfill unmet medical needs.
The data from immuno-oncology (IO) therapy trials often show delayed effects, cure rate, crossing hazards, or some mixture of these phenomena. Thus, the proportional hazards (PH) assumption is often ...violated such that the commonly used log-rank test can be very underpowered. In these trials, the conventional hazard ratio for describing the treatment effect may not be a good estimand due to the lack of an easily understandable interpretation. To overcome this challenge, restricted mean survival time (RMST) has been strongly recommended for survival analysis in clinical literature due to its independence of the PH assumption as well as a more clinically meaningful interpretation. The RMST also aligns well with the estimand associated with the analysis from the recommendation in ICH E-9 (R1), and the test/estimation coherency. Currently, the Kaplan Meier (KM) curve is commonly applied to RMST related analyses. Due to some drawbacks of the KM approach such as the limitation in extrapolating to time points beyond the follow-up time, and the large variance at time points with small numbers of events, the RMST may be hindered.
The dynamic RMST curve using a mixture model is proposed in this paper to fully enhance the RMST method for survival analysis in clinical trials. It is constructed that the RMST difference or ratio is computed over a range of values to the restriction time τ which traces out an evolving treatment effect profile over time.
This new dynamic RMST curve overcomes the drawbacks from the KM approach. The good performance of this proposal is illustrated through three real examples.
The RMST provides a clinically meaningful and easily interpretable measure for survival clinical trials. The proposed dynamic RMST approach provides a useful tool for assessing treatment effect over different time frames for survival clinical trials. This dynamic RMST curve also allows ones for checking whether the follow-up time for a study is long enough to demonstrate a treatment difference. The prediction feature of the dynamic RMST analysis may be used for determining an appropriate time point for an interim analysis, and the data monitoring committee (DMC) can use this evaluation tool for study recommendation.
Identifying the maximum tolerated dose (MTD) and recommending a Phase II dose for an investigational treatment is crucial in cancer drug development. A suboptimal dose often leads to a failed ...late‐stage trial, while an overly toxic dose causes harm to patients. There is a very rich literature on trial designs for dose‐finding oncology clinical trials. We propose a novel hybrid design that maximizes the merits and minimizes the limitations of the existing designs. Building on two existing dose‐finding designs: a model‐assisted design (the modified toxicity probability interval) and a dose‐toxicity model‐based design, a hybrid design of the modified toxicity probability interval design and a dose‐toxicity model such as the logistic regression model is proposed, incorporating optimal properties from these existing approaches. The performance of the hybrid design was tested in a real trial example and through simulation scenarios. The hybrid design controlled the overdosing toxicity well and led to a recommended dose closer to the true MTD due to its ability to calibrate for an intermediate dose. The robust performance of the proposed hybrid design is illustrated through the real trial dataset and simulations. The simulation results demonstrated that the proposed hybrid design can achieve excellent and robust operating characteristics compared to other existing designs and can be an effective model for determining the MTD and recommended Phase II dose in oncology dose‐finding trials. For practical feasibility, an R‐shiny tool was developed and is freely available to guide clinicians in every step of the dose finding process.
What's new?
For novel cancer therapeutics, identifying the maximum tolerated dose (MTD) and the ideal dose for Phase II clinical trials are major goals of Phase I dose‐finding studies. Here, to better optimize dose selection, the authors present a hybrid approach to making dose estimates based on mode‐assisted design and a dose‐toxicity model. In a real trial example and simulation scenarios, the hybrid design limited the risk of overdosing toxicity and, owing to calibration for an intermediate dose, yielded a recommend dose more on par with true MTD. The results highlight the utility of the hybrid design for Phase I dose‐finding trials.
This paper describes both a near term and a long term optical interconnect solution, the first based on a packaging architecture and the second based on a monolithic photonic CMOS architecture. The ...packaging-based optical I/O architecture implemented with 90 nm CMOS transceiver circuits, 1 × 12 VCSEL/detector arrays and polymer waveguides achieves 10 Gb/s/channel at 11 pJ/b. A simple TX pre-emphasis technique enables a potential 18 Gb/s at 9.6 pJ/b link efficiency. Analysis predicts this architecture to reach less than 1 pJ/b at the 16 nm CMOS technology node. A photonic CMOS process enables higher bandwidth and lower energy-per-bit for chip-to-chip optical I/O through integration of electro-optical polymer based modulators, silicon nitride waveguides and polycrystalline germanium (Ge) detectors into a CMOS logic process. Experimental results for the photonic CMOS ring resonator modulators and Ge detectors demonstrate performance above 20 Gb/s and analysis predicts that photonic CMOS will eventually enable energy efficiency better than 0.3 pJ/b with 16 nm CMOS. Optical interconnect technologies such as these using multi-lane communication or wavelength division multiplexing have the potential to achieve TB/s interconnect and enable platforms suitable for the tera-scale computing era.
In oncology studies, it is important to understand and characterize disease heterogeneity among patients so that patients can be classified into different risk groups and one can identify high-risk ...patients at the right time. This information can then be used to identify a more homogeneous patient population for developing precision medicine. In this paper, we propose a mixture survival tree approach for direct risk classification. We assume that the patients can be classified into a pre-specified number of risk groups, where each group has distinct survival profile. Our proposed tree-based methods are devised to estimate latent group membership using an EM algorithm. The observed data log-likelihood function is used as the splitting criterion in recursive partitioning. The finite sample performance is evaluated by extensive simulation studies and the proposed method is illustrated by a case study in breast cancer.
Cox proportional hazards (PH) model evaluates the effects of interested covariates under PH assumption without specified the baseline hazard. In clinical trial applications, however, the explicitly ...estimated hazard or cumulative survival function for each treatment group helps to assess and interpret the meaning of treatment difference. In this paper, we propose to use a flexible mixture model under the PH constraint to fit the underline survival functions. Simulations are conducted to evaluate its performance and show that the proposed mixture PH model is very similar to the Cox PH model in terms of estimating the hazard ratio, bias, confidence interval coverage, type-I error and testing power. Application to several real clinical trial examples demonstrates that the results from this approach are almost identical to the results from Cox PH model. The explicitly estimated hazard function for each treatment group provides additional useful information and helps the interpretation of hazard comparisons.
In cancer studies, it is important to understand disease heterogeneity among patients so that precision medicine can particularly target high‐risk patients at the right time. Many feature variables ...such as demographic variables and biomarkers, combined with a patient's survival outcome, can be used to infer such latent heterogeneity. In this work, we propose a mixture model to model each patient's latent survival pattern, where the mixing probabilities for latent groups are modeled through a multinomial distribution. The Bayesian information criterion is used for selecting the number of latent groups. Furthermore, we incorporate variable selection with the adaptive lasso into inference so that only a few feature variables will be selected to characterize the latent heterogeneity. We show that our adaptive lasso estimator has oracle properties when the number of parameters diverges with the sample size. The finite sample performance is evaluated by the simulation study, and the proposed method is illustrated by two datasets.
Pancreatic ductal adenocarcinoma (PDAC) tumor growth is enhanced by tumor-associated macrophages (TAMs), yet the mechanisms by which tumor cells and TAMs communicate are not fully understood. Here we ...show that exosomes secreted by PDAC cell lines differed in their surface proteins, lipid composition, and efficiency of fusing with THP-1-derived macrophages in vitro. Exosomes from AsPC-1, an ascites-derived human PDAC cell line, were enriched in ICAM-1, which mediated their docking to macrophages through interactions with surface-exposed CD11c on macrophages. AsPC-1 exosomes also contained much higher levels of arachidonic acid (AA), and they fused at a higher rate with THP-1-derived macrophages than did exosomes from other PDAC cell lines or from an immortalized normal pancreatic ductal epithelial cell line (HPDE) H6c7. Phospholipase A2 enzymatic cleavage of arachidonic acid from AsPC-1 exosomes reduced fusion efficiency. PGE2 secretion was elevated in macrophages treated with AsPC-1 exosomes but not in macrophages treated with exosomes from other cell lines, suggesting a functional role for the AsPC-1 exosome-delivered arachidonic acid in macrophages. Non-polarized (M0) macrophages treated with AsPC-1 exosomes had increased levels of surface markers indicative of polarization to an immunosuppressive M2-like phenotype (CD14hi CD163hi CD206hi). Furthermore, macrophages treated with AsPC-1 exosomes had significantly increased secretion of pro-tumoral, bioactive molecules including VEGF, MCP-1, IL-6, IL-1β, MMP-9, and TNFα. Together, these results demonstrate that compared to exosomes from other primary tumor-derived PDAC cell lines, AsPC-1 exosomes alter THP-1-derived macrophage phenotype and function. AsPC-1 exosomes mediate communication between tumor cells and TAMs that contributes to tumor progression.
The reference scaled bioequivalence has been proposed with many successful applications for the highly variable products. The statistical properties for the reference scaled bioequivalence have been ...studied for the commonly used crossover design. However, a crossover design may not be feasible in a real application such as the biosimilar study, instead a parallel design is a more timely and cost-effective choice. In this paper, the approximate upper confidence interval limit for the linearized criteria in the reference scaled bioequivalence from a parallel design is derived. The performance of the approximation is evaluated through the simulation. The simulation results show that this approximation performs well and gives reasonable power and well-controlled type I error.
The frequency of visualization and size of internal mammary lymph nodes in women undergoing high-risk screening breast MRI is unknown. When these nodes are discovered on staging MRI of newly ...diagnosed breast cancer patients, management could present a treatment dilemma because normal size criteria do not exist. The aim of this study was to establish the average size and frequency of internal mammary lymph nodes observed in asymptomatic high-risk women undergoing screening breast MRI.
We conducted a retrospective review of 108 women at high risk for breast cancer who underwent screening breast MRI between January 2010 and January 2014. Patients with new or previous diagnosis of breast cancer, prior nonbreast malignancy affecting the thorax or mediastinum, or previous radiation to the thorax were excluded. The presence, diameter, laterality, intercostal space, relationship to the internal mammary vessels, age, morphology, and clinical history of internal mammary lymph nodes were recorded.
Internal mammary lymph nodes were visualized in 50 of 108 high-risk patients, with an average size of 4.5 mm (range ± SD, 2-9 ± 1.59 mm). In the 50 women who had internal mammary lymph nodes visible on MRI, an average of 1.4 nodes (range, 1-3 nodes) were present. Internal mammary lymph nodes were more frequently visualized on the left (p < 0.001), at the second and third intercostal spaces (p = 0.007), and medial to the internal mammary vessels (p < 0.001).
In this small cohort, 1-3 presumed normal internal mammary lymph nodes measuring 2-9 mm (mean diameter 4.5 mm) were detected in about half of asymptomatic high-risk women presenting for screening MRI of the breasts.
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