Pre-specified Level Research Articles

TWAS-GKF: a novel method for causal gene identification in transcriptome-wide association studies with knockoff inference.

Transcriptome-wide association study (TWAS) aims to identify trait-associated genes regulated by significant variants to explore the underlying biological mechanisms at a tissue-specific level. Despite the advancement of current TWAS methods to cover diverse traits, traditional approaches still face two main challenges: (i) the lack of methods that can guarantee finite-sample false discovery rate (FDR) control in identifying trait-associated genes; and (ii) the requirement for individual-level data, which is often inaccessible. To address this challenge, we propose a powerful knockoff inference method termed TWAS-GKF to identify candidate trait-associated genes with a guaranteed finite-sample FDR control. TWAS-GKF introduces the main idea of Ghostknockoff inference to generate knockoff variables using only summary statistics instead of individual-level data. In extensive studies, we demonstrate that TWAS-GKF successfully controls the finite-sample FDR under a pre-specified FDR level across all settings. We further apply TWAS-GKF to identify genes in brain cerebellum tissue from the Genotype-Tissue Expression (GTEx) v8 project associated with schizophrenia (SCZ) from the Psychiatric Genomics Consortium (PGC), and genes in liver tissue related to low-density lipoprotein cholesterol (LDL-C) from the UK Biobank, respectively. The results reveal that the majority of the identified genes are validated by Open Targets Validation Platform. The R package TWAS.GKF is publicly available at https://github.com/AnqiWang2021/TWAS.GKF.

The Effects of Aspirin Intervention on Inflammation-Associated Lingual Bacteria: A Pilot Study from a Randomized Clinical Trial.

Several bacterial taxa enriched in inflammatory bowel diseases and colorectal cancer (CRC) are found in the oral cavity. We conducted a pilot study nested within a six-week aspirin intervention in a randomized placebo-controlled trial to test their response to aspirin intervention. Fifty healthy subjects, 50-75 years old, were randomized to receive 325 mg aspirin (n = 30) or placebo (n = 20) orally once daily for six weeks. Oral tongue swabs were collected at baseline and week six. We estimated the association between aspirin use and the temporal changes in the relative abundance of pre-specified genus level taxa from pre- to post-treatment. The temporal change in relative abundance differed for eight genus level taxa between the aspirin and placebo groups. In the aspirin group, there were significant increases in the relative abundances of Neisseria, Streptococcus, Actinomyces, and Rothia and significant decreases in Prevotella, Veillonella, Fusobacterium, and Porphyromonas relative to placebo. The log ratio of Neisseria to Fusobacterium declined more in the aspirin group than placebo, signaling a potential marker associated with aspirin intervention. These preliminary findings should be validated using metagenomic sequencing and may guide future studies on the role of aspirin on taxa in various oral ecological niches.

Tumor-Infiltrating Lymphocytes in Patients With Stage I Triple-Negative Breast Cancer Untreated With Chemotherapy

The absolute benefit of chemotherapy for all patients with stage I triple-negative breast cancer (TNBC) is unclear, and biomarkers are not currently available for selecting patients with an excellent outcome for whom neoadjuvant or adjuvant chemotherapy may have negligible benefit. High levels of stromal tumor-infiltrating lymphocytes (sTILs) are associated with favorable survival in TNBC, but data solely in stage I TNBC are lacking. To examine the outcomes of patients of all ages with stage I TNBC solely and who received neither neoadjuvant nor adjuvant chemotherapy, according to centrally reviewed sTIL levels at prespecified cutoffs. This cohort study used the Netherlands Cancer Registry to identify patients diagnosed with stage I TNBC between January 1, 2005, and December 31, 2015, who were not treated with chemotherapy. Only patients who did not receive neoadjuvant and/or adjuvant chemotherapy were selected. The clinical data were matched with their corresponding pathology data provided by the Dutch Pathology Registry. Data analysis was performed between February and October 2023. The primary end point was breast cancer-specific survival (BCSS) at 5, 10, and 15 years for the prespecified sTIL level cutoffs of 30%, 50%, and 75%. Hematoxylin and eosin-stained slides were used for central review of histologic subtype, grade, and lymphovascular invasion. The International Immuno-Oncology Biomarker Working Group guidelines were used to score the sTIL levels; these levels were determined for 1041 patients. Of a total of 4511 females with stage I TNBC, patients who were not treated with chemotherapy were selected and tissue blocks requested; sTILs were scored in 1041 patients (mean [SD] age at diagnosis, 64.4 [11.1] years, median follow-up 11.4 [95% CI, 10.9-11.9] years) who were included in the analyses.. Most tumors (952 [91.5%]) were invasive carcinomas of nonspecial histologic subtype. Most patients (548 [52.6%]) had pT1cN0 tumors. Median (range) sTIL level was 5% (1%-99%). A total of 775 patients (74.4%) had sTIL levels below 30%, 266 (25.6%) had 30% or greater, 203 (19.5%) had 50% or greater, and 141 (13.5%) had 75% or greater. Patients with pT1abN0 tumors had a more favorable outcome vs patients with pT1cN0 tumors, with a 10-year BCSS of 92% (95% CI, 89%-94%) vs 86% (95% CI, 82%-89%). In the overall cohort, sTIL levels of at least 30% were associated with better BCSS compared with sTIL levels less than 30% (96% and 87%, respectively; hazard ratio [HR], 0.45; 95% CI, 0.26-0.77). High sTIL levels of 50% or greater were associated with a better outcome than low sTIL levels of less than 50% (HR, 0.27; 95% CI, 0.10-0.74) in patients with pT1C tumors, with a 10-year BCSS of 95% increasing to 98% with sTIL levels of 75% or greater. Results of this study showed that patients with stage I TNBC and high level of sTILs who did not receive neoadjuvant or adjuvant chemotherapy had excellent 10-year BCSS. The findings further support the role of sTILs as integral biomarkers in prospective clinical trials of therapy optimization for this patient population.

Goodness-of-fit tests for multinomial models with inverse sampling

This article proposes goodness-of-fit tests for multinomial models using an inverse sampling scheme. From the multiple decision-theoretic perspective, we devise a test statistic and stopping rule that satisfy a prespecified probability level P* and obtain corresponding optimal sample sizes. Incomplete Dirichlet type II distribution functions are used to develop the procedure and to express the probability of correct decisions for various cell configurations for multinomial models. For empirical studies, Monte Carlo experiments are conducted, and for illustrations, various cell configurations of a wheel of fortune are demonstrated.

Association of RNA-sequencing signatures with clinical outcomes of pembrolizumab + chemotherapy in patients with metastatic nonsquamous non–small cell lung cancer (NSCLC) enrolled in the phase 2 KEYNOTE-782 trial.

8578 Background: KEYNOTE-782 (NCT03664024) was a single-arm phase 2 study designed to assess possible biomarkers of response (objective response rate [ORR]) to first-line pembrolizumab + chemotherapy (pemetrexed + carboplatin or cisplatin) in patients with previously untreated metastatic nonsquamous NSCLC. Herein, we examined relationships between the T-cell–inflamed gene expression profile (TcellinfGEP) and other tumor microenvironment consensus signatures and efficacy of pembrolizumab + chemotherapy in an exploratory analysis of KEYNOTE-782. Methods: All patients were to receive pembrolizumab 200 mg Q3W, pemetrexed 500 mg/m2 Q3W, and 4 Q3W cycles of carboplatin AUC 5 mg/mL/min or cisplatin 75 mg/m2. Using tumor samples, RNA sequencing was used to measure expression of the TcellinfGEP and 10 non-TcellinfGEP signatures (angiogenesis, gMDSC, glycolysis, hypoxia, mMDSC, MYC, proliferation, RAS, stromal/EMT/TGFβ, and WNT). The association between each signature and clinical outcomes was analyzed using logistic regression (ORR) and Cox proportional hazards regression (progression-free survival [PFS] and overall survival [OS]); the prespecified significance level was α = 0.05 for TcellinfGEP (hypothesized positive association) and α = 0.10 for non-TcellinfGEP signatures (hypothesized negative associations). The clinical utility of TcellinfGEP was descriptively assessed using a prespecified cutoff of the first tertile. The clinical database cutoff was November 5, 2021. Results: Of 117 patients enrolled, 69 (59.0%) had evaluable RNA-sequencing data. TcellinfGEP was not associated with ORR ( P = 0.183), PFS ( P = 0.071), or OS ( P = 0.142); the area under the receiver operating characteristic curve for discriminating response was 0.56 (95% CI, 0.42-0.71). None of the 10 non-TcellinfGEP consensus signatures showed a statistically significant association with clinical outcomes after adjusting for TcellinfGEP (multiplicity adjusted P > 0.10). ORR and median PFS were comparable in the TcellinfGEP low and nonlow subgroups; there was a trend towards longer OS in the nonlow subgroup (Table). Conclusions: In this exploratory analysis of patients with metastatic nonsquamous NSCLC, none of the RNA-sequencing signatures evaluated were associated with clinical outcomes of pembrolizumab + chemotherapy. There was little evidence of clinical utility of TcellinfGEP when evaluated as a dichotomous variable. Data support the use of pembrolizumab + chemotherapy as first-line therapy for patients with nonsquamous NSCLC regardless of consensus signature status. Clinical trial information: NCT03664024 . [Table: see text]

Multiple phenotype association tests based on sliced inverse regression

BackgroundJoint analysis of multiple phenotypes in studies of biological systems such as Genome-Wide Association Studies is critical to revealing the functional interactions between various traits and genetic variants, but growth of data in dimensionality has become a very challenging problem in the widespread use of joint analysis. To handle the excessiveness of variables, we consider the sliced inverse regression (SIR) method. Specifically, we propose a novel SIR-based association test that is robust and powerful in testing the association between multiple predictors and multiple outcomes.ResultsWe conduct simulation studies in both low- and high-dimensional settings with various numbers of Single-Nucleotide Polymorphisms and consider the correlation structure of traits. Simulation results show that the proposed method outperforms the existing methods. We also successfully apply our method to the genetic association study of ADNI dataset. Both the simulation studies and real data analysis show that the SIR-based association test is valid and achieves a higher efficiency compared with its competitors.ConclusionSeveral scenarios with low- and high-dimensional responses and genotypes are considered in this paper. Our SIR-based method controls the estimated type I error at the pre-specified level α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha $$\\end{document}.

LFK index does not reliably detect small-study effects in meta-analysis: A simulation study.

The LFK index has been promoted as an improved method to detect bias in meta-analysis. Putatively, its performance does not depend on the number of studies in the meta-analysis. We conducted a simulation study, comparing the LFK index test to three standard tests for funnel plot asymmetry in settings with smaller or larger group sample sizes. In general, false positive rates of the LFK index test markedly depended on the number and size of studies as well as the between-study heterogeneity with values between 0% and almost 30%. Egger's test adhered well to the pre-specified significance level of 5% under homogeneity, but was too liberal (smaller groups) or conservative (larger groups) under heterogeneity. The rank test was too conservative for most simulation scenarios. The Thompson-Sharp test was too conservative under homogeneity, but adhered well to the significance level in case of heterogeneity. The true positive rate of the LFK index test was only larger compared with classic tests if the false positive rate was inflated. The power of classic tests was similar or larger than the LFK index test if the false positive rate of the LFK index test was used as significance level for the classic tests. Under ideal conditions, the false positive rate of the LFK index test markedly and unpredictably depends on the number and sample size of studies as well as the extent of between-study heterogeneity. The LFK index test in its current implementation should not be used to assess funnel plot asymmetry in meta-analysis.

Novel protection method for AC microgrids with multiple distributed generations using Unscented Kalman filter

Traditional protection methods were inadequate in multiple distributed generations (MDG) microgrids due to the bidirectional power flow/high fault current in the grid-connected (GC) mode, and reduced fault current in the islanded (ID) mode. Therefore, a reliable and robust protection method has become of paramount importance for such MDG-based microgrids. This article suggests an Unscented Kalman filter (UKF) to detect, classify, and locate faults in such MDG-based microgrids. Initially, the harmonic content is generated from the UKF-estimated current signal to compute the total harmonic RMS factor (THRF). Then the changes in the THRF of each phase are cross-checked to a pre-specified threshold level to distinguish the fault conditions. Furthermore, the cumulative covariance-dependent reactive energy (CCDRE) is computed from both the estimated state and covariance matrix of voltage and current signals provided by UKF. Finally, the directional trends in the CCDRE are used to identify the fault zone in the microgrids. The effectiveness of the proposed method is validated through ample simulations on CIGRE and IEC 61,850–7–420 microgrid test system via MATLAB® Simulink. The results illustrate that the presented strategy is robust in both the GC mode and the ID mode of operation in meshed and radial topologies with 99.9% accuracy.

Imperceptible Attacks on Fault Detection and Diagnosis Systems in Smart Buildings

Automated Fault Detection and Diagnosis (AFDD) systems are critical to safe and efficient operation of smart buildings. A significant amount of building data can be collected and analyzed to detect building compo-nent failures. Attacks against such data that are contami-nated with small additive disturbances (i.e., adversarial per-turbation attacks) could dreadfully impact the performance of such systems while maintaining a high level of imper-ceptibility. The vulnerability studies of such data attacks is lacking. Specifically, most existing detection and clas-sification models have flat structures, regarded as Single-Stage Classifiers (SSCs), are prone to adversarial data perturbation attacks. In this paper, we present a coarse-to-fine Hierarchical Fault Detection and multi-level Diagnosis (HFDD) model, and formulate a mathematical program to derive targeted attacks on the model with respect to a pre-specified target diagnosis level. Two algorithms are developed based on convex relaxations of the formulated program for non-targeted attacks. An Alternating Direction Method of Multipliers (ADMM)-based solver is developed for the convex programs. Extensive experiments are con-ducted using two real-world datasets of measurements from air handling units and chillers, demonstrating the fea-sibility of the proposed attacks with regard to misclassifi-cation rate and imperceptibility of the attack. We also show that the HFDD is more robust to disturbances than SSC-based fault detection and multi-level diagnosis systems.

Minimizing Noise in Location Privacy Protection Through Equipment Error Consideration

In recent years, systems that collect location information and publish statistics, such as those that publish congestion information, have been extensively employed. Because it is possible to infer an individual’s identity even if the information is not directly disclosed, it is essential to disclose data with privacy protection. Therefore, privacy protection methods based on differential privacy are attracting attention. Geo-indistinguishability is the most famous extension theorem of differential privacy for location information. Geo-indistinguishability can be achieved by adding noise to a target value that must be protected. However, noise addition reduces the usefulness of the data. Thus, it is desirable to add minimal noise to your privacy budget. Therefore, we focus on the fact that the values obtained using measurement devices contain errors. We introduced a novel concept of differential privacy tailored for location information, termed true-value-based geo-indistinguishability (T-Geo-I), which accounts for equipment noise. We also proposed a location information privacy protection method that considers T-Geo-I and reduces the amount of added noise. The object of privacy protection should be the “true value” not the “measured value” that includes measurement errors. According to the experimental results, in the case wherein the measurement error is the normal distribution, our method reduced the noise average and mean square error (MSE) by up to 41% and 63%, respectively, compared with conventional methods while maintaining a prespecified level of privacy in 108 samples of numerical data. In the case wherein the measurement error is the lognormal distribution, the proposed method based on T-Geo-I succeeded in reducing the noise average and MSE by up to 60% and 67%, respectively, compared with methods based on Geo-I, while maintaining a prespecified level of privacy. These findings indicate that the proposed method can improve the usefulness of data while maintaining a prespecified degree of privacy protection.

Soil-Matric-Potential-Based Irrigation Scheduling to Increase Yield and Water Productivity of Okra

A field experiment was conducted on okra (Abelmoschus esculentus L.) for assessing the sustainability of yield with optimum irrigation schedule based on soil moisture depletion. Four irrigation treatments: Irrigation at I1:20%, I2:30%, I3:40% and I4:50% of soil moisture depletion rate in main plots and three fertilizer treatments: Fertigation at F1:100%, F2:80% and F3:60% of recommended NPK (100:25:40 kg/ha) in subplots were tested. Soil matric potential was recorded continuously using electronic tensiometers. The soil moisture characteristics curve was derived for various soil matric potential value sand the soil water content. The irrigation controller triggered solenoid valves for irrigation when soil moisture depletion reached a prespecified level in each treatment. Soil moisture depletion values were significantly predicted based on a regression model calibrated for each treatment over the crop growing period. The model gave minimum prediction error (PE) for I1, followed by I2, I3 and I4, respectively. Plant growth and yield parameters were significantly influenced by the soil moisture availability under each treatment. It is recommended that irrigation be scheduled at 20% soil moisture depletion rate together with 100% NPK fertilizer application for attaining sustainable yield of okra (12.3 t/ha), apart from maximum WUE (3.5 kg/m3) and plant growth parameters under semiarid inceptisols.

Valuing equity-linked annuities under high-water mark fee structure

This paper studies the valuation of equity-linked investment products embedded with a high-water mark (HWM) fee structure. Under the HWM fee structure, the insurance company charges threshold fees at a constant rate from the policyholder's account whenever the account value is lower than a pre-specified level, and levies HMW fees at another constant rate whenever the account is hitting new record highs that are higher than another pre-specified level. The dynamics of the logarithmic value of the policyholder's account, before fees, is assumed to follow either a two-sided jump-diffusion process with double exponential jumps, or a down-ward jump-diffusion process with exponential jumps. For the two-sided jump-diffusion model with HWM fees, using the Wiener–Hopf factorisation theorem and the duality lemma, we derive an explicit expression for its potential measure. For the down-ward jump-diffusion model with both threshold fees and HWM fees, we are facilitated with the excursion theory to derive an explicit expression of the potential measure. Using the above newly derived potential measures, we are able to obtain formulas for valuing the equity-linked annuity under the HWM fee structure. Finally, we illustrate our results with some numerical examples.

Pooled Exposure-Response (ER) and Quantitative Benefit/Risk (B/R) Analyses Support the Approved Copanlisib Intermittent Dosing Regimen

Introduction: Copanlisib is a potent and highly selective pan-class phosphatidylinositol 3-kinase (PI3K) inhibitor with preferential activity against the p110α and p110δ isoforms. Unlike other PI3K inhibitors, copanlisib is uniquely administered intravenously using an intermittent schedule, supported by preclinical data and literature model-based analyses showing high tumor residence and time intervals enabling for recovery of normal tissue. The pivotal studies, CHRONOS-1 and CHRONOS-3, established the benefit/risk of copanlisib 60 mg on Day 1, 8, 15 of 28-day cycle as monotherapy in 3L+ relapsed follicular lymphoma and in combination with rituximab in 2L+ non-Hodgkin's lymphoma, respectively. While prior ER analyses in CHRONOS-1 suggested a positive ER trend for copanlisib safety, similar to oral continuously dosed PI3K inhibitors (2022 FDA ODAC), ER analyses from the larger Phase 3 study, CHRONOS-3, revealed no significant ER for safety (Morcos et al. CPT Pharmacometrics Syst Pharmacol 2023). This analysis investigates pooled ER relationships for copanlisib safety across the pivotal studies and considers them in the context of the ER for efficacy in CHRONOS-3, as part of a quantitative B/R analysis for the approved copanlisib intermittent dosing regimen. Methods: Individual static and time-varying exposure metrics in CHRONOS-1 and CHRONOS-3 were derived from a previously established comprehensive population pharmacokinetics (popPK) analysis (Morcos et al. CPT Pharmacometrics Syst Pharmacol 2023). ER analyses investigated the relationship to the efficacy endpoint: progression-free survival (PFS) in CHRONOS-3 using 2-year follow-up; and to the safety endpoints: time-to-first Grade ≥ 3 adverse events (Gr≥3 AEs) and serious adverse events (SAEs), and frequency of individual AEs associated with copanlisib treatment (hyperglycemia, diarrhea, hypertension, nausea, fatigue, neutropenia, and lung infections of any grade), pooled across CHRONOS-1 using 6-year follow-up and CHRONOS-3 using 2-year follow-up. Multivariate parametric time-to-event (TTE) and logistic regression models investigated ER relationships at a prespecified significance level of p<0.01 after accounting for other potentially prognostic demographic, laboratory, and disease related baseline covariates. To quantitatively assess the B/R of the copanlisib 60 mg intermittent dosing regimen, a clinical utility index (CUI) function was defined with a 1:1 weighting for efficacy and safety to consider the tradeoffs between any identified ER relationships for efficacy and safety. Results: In CHRONOS-3, the parametric TTE ER analysis for PFS was characterized by a 2-knot spline baseline hazard function and demonstrated a statistically significant positive ER relationship (p<0.00001). Analysis results are consistent with a prior ER analysis using a semi-parametric Cox proportional hazards model of earlier 1-year follow-up (Morcos et al. CPT Pharmacometrics Syst Pharmacol 2023). Therefore, greater copanlisib exposure is associated with more prolonged PFS. Across pooled CHRONOS-1 and CHRONOS-3, parametric TTE for first Gr≥3 AEs and SAEs were characterized by 2-knot spline baseline hazard functions and demonstrated a borderline significant ER for Gr≥3 AEs (p=0.014) and no significant ER for SAEs (p=0.26). Multivariate logistic regression analyses showed no significant ER for individual AEs after adjusting for relevant baseline covariates. Across the range of exposures achieved with the copanlisib 60 mg intermittent dosing regimen, a steeper relationship for the hazard ratio relative to placebo in is seen for the significant ER of PFS compared with the borderline ER of Gr≥3 AEs (Figure 1). The CUI accounting for the quantitative ER relationships confirmed the favorable B/R across the range of copanlisib exposures. Thus, lower copanlisib doses may result in lower efficacy while not necessarily resulting in substantially improved safety. Conclusions: The completed quantitative analyses substantiate the favorable B/R for the copanlisib 60 mg on days 1, 8, and 15 of a 28-day cycle dosing regimen.

Controlling the Minimum Item Exposure Rate in Computerized Adaptive Testing: A Two-Stage Sympson-Hetter Procedure.

Computerized adaptive testing (CAT) can improve test efficiency, but it also causes the problem of unbalanced item usage within a pool. The effect of uneven item exposure rates can not only induce a test security problem due to overexposed items but also raise economic concerns about item pool development due to underexposed items. Therefore, this study proposes a two-stage Sympson-Hetter (TSH) method to enhance balanced item pool utilization by simultaneously controlling the minimum and maximum item exposure rates. The TSH method divides CAT into two stages. While the item exposure rates are controlled above a prespecified level (e.g., rmin) in the first stage to increase the exposure rates of the underexposed items, they are controlled below another prespecified level (e.g., rmax) in the second stage to prevent items from overexposure. To reduce the effect on trait estimation, TSH only administers a minimum sufficient number of underexposed items that are generally less discriminating in the first stage of CAT. The simulation study results indicate that the TSH method can effectively improve item pool usage without clearly compromising trait estimation precision in most conditions while maintaining the required level of test security.

Inference on optimal treatment assignments

We consider inference on optimal treatment assignments. Our methods allow inference on the treatment assignment rule that would be optimal given knowledge of the population treatment effect in a general setting. The procedure uses multiple hypothesis testing methods to determine a subset of the population for which assignment to treatment can be determined to be optimal after conditioning on all available information, with a prespecified level of confidence. A Monte Carlo study confirms that the inference procedure has good small sample behavior. We apply the method to study Project STAR and the optimal assignment of a small class intervention based on school and teacher characteristics.

Deep Learning and Registration-Based Mapping for Analyzing the Distribution of Nodal Metastases in Head and Neck Cancer Cohorts: Informing Optimal Radiotherapy Target Volume Design.

We introduce a deep-learning- and a registration-based method for automatically analyzing the spatial distribution of nodal metastases (LNs) in head and neck (H/N) cancer cohorts to inform radiotherapy (RT) target volume design. The two methods are evaluated in a cohort of 193 H/N patients/planning CTs with a total of 449 LNs. In the deep learning method, a previously developed nnU-Net 3D/2D ensemble model is used to autosegment 20 H/N levels, with each LN subsequently being algorithmically assigned to the closest-level autosegmentation. In the nonrigid-registration-based mapping method, LNs are mapped into a calculated template CT representing the cohort-average patient anatomy, and kernel density estimation is employed to estimate the underlying average 3D-LN probability distribution allowing for analysis and visualization without prespecified level definitions. Multireader assessment by three radio-oncologists with majority voting was used to evaluate the deep learning method and obtain the ground-truth distribution. For the mapping technique, the proportion of LNs predicted by the 3D probability distribution for each level was calculated and compared to the deep learning and ground-truth distributions. As determined by a multireader review with majority voting, the deep learning method correctly categorized all 449 LNs to their respective levels. Level 2 showed the highest LN involvement (59.0%). The level involvement predicted by the mapping technique was consistent with the ground-truth distribution (p for difference 0.915). Application of the proposed methods to multicenter cohorts with selected H/N tumor subtypes for informing optimal RT target volume design is promising.

A Case Study of 2-Stage Seamless Adaptive Sample Size Re-Estimation Design with Efficacy Interim Analysis When Slope Is the Primary Endpoint

Due to highly unmet medical needs in rare disease areas, there is great desire to speed up the drug development process. A two-stage adaptive design option for a placebo-controlled registration study is being evaluated. Stage 1 consists of participants in an ongoing phase 2 study with 1-year double blinded (DB) treatment and Stage 2 includes newly enrolled participants with 2-year DB treatment period. The primary endpoint is the annualized rate of change (slope) for a continuous longitudinal measurement, which will be evaluated through a random coefficient linear mixed model. An unblinded interim analysis will be performed using Stage 1 data to re-estimate the sample size for Stage 2, followed by another interim analysis for potential early efficacy stopping when all participants completed the 1-year DB treatment. To control the overall Type 1 error rate, rather than using a conservative approach, the actual correlation between the interim and final test statistics will be taken into account to determine the final significance level given the pre-specified significance level for the interim efficacy analysis. Multiplicity adjustments for secondary endpoints including considerations for order switching between interim and final analysis in this specific case study will also be discussed.

Risk of Further Progression or Death Among Durable Progression-Free Survivors With Melanoma or Non-Small-Cell Lung Cancer in PD-1 Blockade Trials: Implications for Imaging Surveillance.

Durable progression-free survivors (dPFSors) over 2 years have been reported among patients with melanoma or non-small-cell lung cancer (NSCLC) who received PD-(L)1 therapy. However, risk of progression still exists and the optimal imaging surveillance interval is unknown. Individual patient data for progression-free survival (PFS) were extracted from PD-1 blockade clinical trials with a follow-up of at least 5 years. Patients with a PFS of at least 2 years were considered as dPFSors. Conditional risks of progression/death (P/D) every 3, 4, 6, and 12 months in each subsequent year were calculated. We prespecified three different levels of risk between scans (10%, 15%, or 20%) to allow clinicians and patients to decide on the scanning interval on the basis of considerations of imaging frequency and risk tolerance. An interval is considered acceptable if the upper bound of the 95% CI of the risk at each scan is lower than a prespecified level. Of 1,495 and 3,752 patients with melanoma and NSCLC, 474 (31.7%) and 586 (15.6%) were dPFSors, respectively. Among them, the PFS probability for an additional 3 years was 76.4% and 48.1%, respectively. Not more than 8% of patients had P/D in any quarter in the 3 years. With a risk threshold of 10%, melanoma dPFSors can be scanned every 6 months during the third year and then every 12 months in years 4 and 5. The interval for NSCLC would be every 3 months in the third year and every 4 months in years 4 and 5. The higher risk tolerance of 15% and 20% would allow for less frequent scans. On the basis of their own risk tolerance level, our findings allow clinicians and dPFSors make data-driven decisions regarding the imaging surveillance schedule beyond every 3 months.

The Smooth Away From Expected (SAFE) non-inferiority frontier: theory and implementation with an application to the D3 trial

BackgroundIn a non-inferiority trial, the choice of margin depends on the expected control event risk. If the true risk differs from expected, power and interpretability of results can be affected. A non-inferiority frontier pre-specifies an appropriate non-inferiority margin for each value of control event risk. D3 is a non-inferiority trial comparing two treatment regimens in children living with HIV, designed assuming a control event risk of 12%, a non-inferiority margin of 10%, 80% power and a significance level (α) of 0.025. We consider approaches to choosing and implementing a frontier for this already funded trial, where changing the sample size substantially would be difficult.MethodsIn D3, we fix the non-inferiority margin at 10%, 8% and 5% for control event risks of ≥9%, 5% and 1%, respectively. We propose four frontiers which fit these fixed points, including a Smooth Away From Expected (SAFE) frontier. Analysis approaches considered are as follows: using the pre-specified significance level (α=0.025); always using a reduced significance level (to achieve α≤0.025 across control event risks); reducing significance levels only when the control event risk differs significantly from expected (control event risk <9%); and using a likelihood ratio test. We compare power and type 1 error for SAFE with other frontiers.ResultsChanging the significance level only when the control event risk is <9% achieves approximately nominal (<3%) type I error rate and maintains reasonable power for control event risks between 1 and 15%. The likelihood ratio test method performs similarly, but the results are more complex to present. Other analysis methods lead to either inflated type 1 error or badly reduced power. The SAFE frontier gives more interpretable results with low control event risks than other frontiers (i.e. it uses more reasonable non-inferiority margins). Other frontiers do not achieve power close (i.e. within 1%) to SAFE across the range of likely control event risks while controlling type I error. ConclusionsThe SAFE non-inferiority frontier will be used in D3, and the non-inferiority margin and significance level will be modified if the control event risk is lower than expected. This ensures results will remain interpretable if design assumptions are incorrect, while achieving similar power. A similar approach could be considered for other non-inferiority trials where the control event risk is uncertain.

Genome-wide association study of treatment-related toxicity two years following radiotherapy for breast cancer

Background and purposeUp to a quarter of breast cancer patients treated by surgery and radiotherapy experience clinically significant toxicity. If patients at high risk of adverse effects could be identified at diagnosis, their treatment could be tailored accordingly. This study was designed to identify common single nucleotide polymorphisms (SNPs) associated with toxicity two years following whole breast radiotherapy. Materials and MethodsA genome-wide association study (GWAS) was performed in 1,640 breast cancer patients with complete SNP, clinical, treatment and toxicity data, recruited across 18 European and US centres into the prospective REQUITE cohort study. Toxicity data (CTCAE v4.0) were collected at baseline, end of radiotherapy, and annual follow-up. A total of 7,097,340 SNPs were tested for association with the residuals of toxicity endpoints, adjusted for clinical, treatment co-variates and population substructure. ResultsQuantile-quantile plots showed more associations with toxicity above the p < 5 × 10-5 level than expected by chance. Eight SNPs reached genome-wide significance. Nipple retraction grade ≥ 2 was associated with the rs188287402 variant (p = 2.80 × 10-8), breast oedema grade ≥ 2 with rs12657177 (p = 1.12 × 10-10), rs75912034 (p = 1.12 × 10-10), rs145328458 (p = 1.06 × 10-9) and rs61966612 (p = 1.23 × 10-9), induration grade ≥ 2 with rs77311050 (p = 2.54 × 10-8) and rs34063419 (p = 1.21 × 10-8), and arm lymphoedema grade ≥ 1 with rs643644 (p = 3.54 × 10-8). Heritability estimates across significant endpoints ranged from 25% to 39%. Our study did not replicate previously reported SNPs associated with breast radiation toxicity at the pre-specified significance level. ConclusionsThis GWAS for long-term breast radiation toxicity provides further evidence for significant association of common SNPs with distinct toxicity endpoints.