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Multi-arm trials are increasingly of interest because for many diseases; there are multiple experimental treatments available for testing efficacy. Several novel multi-arm multi-stage (MAMS) clinical trial designs have been proposed. However, a major hurdle to adopting the group sequential MAMS routinely is the computational effort of obtaining stopping boundaries. For example, the method of Jaki and Magirr for time-to-event endpoint, implemented in R package MAMS, requires complicated computational efforts to obtain stopping boundaries. In this study, we develop a group sequential MAMS survival trial design based on the sequential conditional probability ratio test. The proposed method is an improvement of the Jaki and Magirr's method in the following three directions. First, the proposed method provides explicit solutions for both futility and efficacy boundaries to an arbitrary number of stages and arms. Thus, it avoids complicated computational efforts for the trial design. Second, the proposed method provides an accurate number of events for the fixed sample and group sequential designs. Third, the proposed method uses a new procedure for interim analysis which preserves the study power. [ABSTRACT FROM AUTHOR]

Cancer immunotherapy trials are frequently characterized by delayed treatment effects such that the proportional hazards assumption is violated and the log-rank test suffers a substantial loss of statistical power. To increase the efficacy of the trial design, a variety of weighted log-rank tests have been proposed for fixed sample and group sequential trial designs. However, in such a group sequential design, it is often not recommended for futility interim monitoring due to possible delayed treatment effect which could result a high false-negative rate. To resolve this problem, we propose a group sequential design using a piecewise weighted log-rank test which provides an event-driven approach based on number of events after the delayed time. That is, the interim looks will not be conducted until the planned number of events observed after the delay time. Thus, it avoids the possibility of false-negative rate due to the delayed treatment effect. Furthermore, with an event-driven approach, the proposed group sequential design is robust against the underlying survival, accrual and censoring distributions. The group sequential designs using Fleming-Harrington-($$\rho ,\gamma $$ ρ , γ) weighted log-rank test and a new weighted log-rank test are also discussed. [ABSTRACT FROM AUTHOR]

Group sequential designs using Lan-DeMets error spending functions are proposed for historical control trials with time-to-event endpoints. Both O'Brien–Fleming and Gamma family types of sequential decision boundaries are derived based on sequential log-rank tests, which follow a Brownian motion in a transformed information time. Simulation results show that the proposed group sequential designs using historical controls preserve the overall type I error and power. [ABSTRACT FROM AUTHOR]

Cancer immunotherapy trials are frequently characterized by a delayed treatment effect that violates the proportional hazards assumption. The log-rank test (LRT) suffers a substantial loss of statistical power under the nonproportional hazards model. Various group sequential designs using weighted LRTs (WLRTs) have been proposed under the fixed delayed treatment effect model. However, patients enrolled in immunotherapy trials are often heterogeneous, and the duration of the delayed treatment effect is a random variable. Therefore, we propose group sequential designs under the random delayed effect model using the random delayed distribution WLRT. The proposed group sequential designs are developed for monitoring the efficacy of the trial using the method of Lan-DeMets alpha-spending function with O’Brien-Fleming stopping boundaries or a gamma family alpha-spending function. The maximum sample size for the group sequential design is obtained by multiplying an inflation factor with the sample size for the fixed sample design. Simulations are conducted to study the operating characteristics of the proposed group sequential designs. The robustness of the proposed group sequential designs for misspecifying random delay time distribution and domain is studied via simulations. [ABSTRACT FROM AUTHOR]

We introduce a new survival distribution, of Pareto type, that arises from a cure-mixture frailty model. We describe its properties and demonstrate connections with familiar distributions including the Pareto and exponential. We derive its characteristic function and moments. [ABSTRACT FROM AUTHOR]

This work focused on the preparation and characterization of a novel amphiphilic block co-polymer and paclitaxel-loaded co-polymer nanoparticles (NPs) and in vitro evaluation of the release of paclitaxel and cytotoxicity of NPs. mPEG-b-P(OA-DLLA)-b-mPEG was prepared via melt polycondensation of methoxy poly(ethylene glycol) (mPEG), octadecanedioic acid (OA) and D,L-lactic acid (DLLA) and characterized by FT-IR, 1H-NMR, 13C-NMR, GPC, DSC and XRD. The paclitaxel-loaded mPEG-b-P(OA-DLLA)-b-mPEG NPs were prepared by nanoprecipitation and then characterized by LPSA, TEM and 1H-NMR. In vitro release behaviors of the paclitaxel-loaded NPs were investigated by HPLC. In vitro cytotoxicity of NPs was evaluated by MTT assay with normal mouse lung fibroblast cells (L929) as model cells. The composition of mPEG-b-P(OA-DLLA)-b-mPEG is consistent with that of the designed co-polymer. The paclitaxel-loaded NPs are of spherical shape with core/shell structure and size smaller than 300 nm. Paclitaxel can be continuously released from the paclitaxel-loaded NPs and the in vitro release rate of paclitaxel decreases with increasing the content of the P(OA-DLLA) segments in the co-polymer. The mPEG-b-P(OA-DLLA)-b-mPEG NPs are non-toxic to L929. The results suggest that mPEG-b-P(OA-DLLA)-b-mPEG NPs are a potential candidate carrier material for the controlled delivery of paclitaxel and other hydrophobic compounds. [ABSTRACT FROM AUTHOR]

This article discusses regression analysis of mixed interval-censored failure time data. Such data frequently occur across a variety of settings, including clinical trials, epidemiologic investigations, and many other biomedical studies with a follow-up component. For example, mixed failure times are commonly found in the two largest studies of long-term survivorship after childhood cancer, the datasets that motivated this work. However, most existing methods for failure time data consider only right-censored or only interval-censored failure times, not the more general case where times may be mixed. Additionally, among regression models developed for mixed interval-censored failure times, the proportional hazards formulation is generally assumed. It is well-known that the proportional hazards model may be inappropriate in certain situations, and alternatives are needed to analyze mixed failure time data in such cases. To fill this need, we develop a maximum likelihood estimation procedure for the proportional odds regression model with mixed interval-censored data. We show that the resulting estimators are consistent and asymptotically Gaussian. An extensive simulation study is performed to assess the finite-sample properties of the method, and this investigation indicates that the proposed method works well for many practical situations. We then apply our approach to examine the impact of age at cranial radiation therapy on risk of growth hormone deficiency in long-term survivors of childhood cancer. [ABSTRACT FROM AUTHOR]

Equal access to clinical trial enrollment is important to ensure that findings are generalizable to the broader population. This study aimed to evaluate disparities in enrollment on pediatric oncology clinical trials. We assessed the relationship between patient characteristics and enrollment on COG trial AAML1031 in a cohort of pediatric patients with AML in the Pediatric Health Information System. The associations of enrollment with outcomes were evaluated. Non-Hispanic Black patients, infants, and patients from zip codes with a lower proportion of poverty were less likely to enroll (30% vs. 61%, p =.004; 34% vs. 58%, p =.003; 46% vs. 58%, p =.02). On-therapy mortality was similar among enrolled and nonenrolled patients (7.3% vs. 8.9%, p =.47). Differences in early mortality were more pronounced among nonenrolled patients compared to enrolled patients (3.0% vs. 0.5%, p =.03). Understanding the etiology of these disparities will inform strategies to ensure balanced access to clinical trials across patient populations. [ABSTRACT FROM AUTHOR]

While most children receive acute myeloid leukemia (AML) chemotherapy as inpatients, there is variability in timing of discharge after chemotherapy completion. This study compared treatment-related morbidity, mortality and cumulative hospitalization in children with AML who were discharged after chemotherapy completion (early discharge) and those who remained hospitalized. Chart abstraction data for 153 early discharge-eligible patients enrolled on a Children’s Oncology Group trial were compared by discharge strategy. Targeted toxicities included viridans group streptococcal (VGS) bacteremia, hypoxia and hypotension. Early discharge occurred in 11% of courses post-Induction I. Re-admission occurred in 80–100%, but median hospital stay was 7 days shorter. Patients discharged early had higher rates of VGS (adjusted risk ratio (aRR) = 1.67, 95% CI = 1.11–2.51), hypoxia (aRR = 1.92, 95% CI = 1.06–3.48) and hypotension (aRR = 4.36, 95% CI = 2.01–9.46), but there was no difference in mortality. As pressure increases to shorten hospitalizations, these results have important implications for determining discharge practices in pediatric AML. [ABSTRACT FROM AUTHOR]

Antibiotic variation among pediatric oncology patients has not been well-described. Identification of significant variability in antibiotic use within this population would warrant evaluation of its clinical impact. We conducted a retrospective cohort study of newly diagnosed patients with pediatric acute lymophoblastic leukemia (ALL) hospitalized from 1999 to 2009 in 39 freestanding US children's hospitals within the Pediatric Health Information System. Medication use data were obtained for the first 30 days from each patient's index ALL admission date. Antibiotic exposure rates were reported as antibiotic days/1000 hospital days. Unadjusted composite broad-spectrum antibiotic exposure rates varied from 577 to 1628 antibiotic days/1000 hospital days. This wide range of antibiotic exposure was unaffected by adjustment for age, gender, race and days of severe illness (adjusted range: 532-1635 days of antibiotic therapy/1000 hospital days). Antibiotic use for children with newly diagnosed ALL varies widely across children's hospitals and is not explained by demographics or illness severity. [ABSTRACT FROM AUTHOR]

The aim of this paper is to develop an intrinsic regression model for the analysis of positive-definite matrices as responses in a Riemannian manifold and their association with a set of covariates, such as age and gender, in a Euclidean space. The primary motivation and application of the proposed methodology is in medical imaging. Because the set of positive-definite matrices do not form a vector space, directly applying classical multivariate regression may be inadequate in establishing the relationship between positive-definite matrices and covariates of interest, such as age and gender, in real applications. Our intrinsic regression model, which is a semiparametric model, uses a link function to map from the Euclidean space of covariates to the Riemannian manifold of positive-definite matrices. We develop an estimation procedure to calculate parameter estimates and establish their limiting distributions. We develop score statistics to test linear hypotheses on unknown parameters and develop a test procedure based on a resampling method to simultaneously assess the statistical significance of linear hypotheses across a large region of interest. Simulation studies are used to demonstrate the methodology and examine the finite sample performance of the test procedure for controlling the family-wise error rate. We apply our methods to the detection of statistical significance of diagnostic effects on the integrity of white matter in a diffusion tensor study of human immunodeficiency virus. Supplemental materials for this article are available online. [ABSTRACT FROM AUTHOR]