Robust Permutation Test of Intraclass Correlation Coefficient for Assessing Agreement.

Inter-rater reliability is critical in oncology to ensure consistent and reliable measurements across raters and methods, such as when evaluating biomarker levels in different laboratories or comparing tumor size assessments by radiation oncologists during therapy planning. This consistency is essential for informed decision-making in both clinical and research contexts, and the intraclass correlation coefficient (ICC) is a widely recommended statistic for assessing agreement. This work focuses on hypothesis testing of the ICC(2,1) with two raters.

Optimizing One-Sample Tests for Proportions in Single- and Two-Stage Oncology Trials.

Background/objectives: Phase II oncology trials often rely on single-arm designs to test H0:π=π0 versus Ha:π>π0, especially when randomized trials are infeasible due to cost or disease rarity. Traditional approaches, such as the exact binomial test and Simon's two-stage design, tend to be conservative, with actual Type I error rates falling below the nominal α due to the discreteness of the underlying binomial distribution. This study aims to develop a more efficient and flexible method that maintains accurate Type I error control in such settings.

Strategies to boost statistical efficiency in randomized oncology trials with primary time-to-event endpoints.

Oncology clinical trials are increasingly expensive, necessitating efforts to streamline phase II and III trials to reduce costs and expedite treatment delivery. Randomization is often impractical in oncology trials due to small sample sizes and limited statistical power, leading to biased inferences. The FDA has recently published guidance documents encouraging the use of prognostic baseline measures to improve the precision of inferences around treatment effects.

ESR1 and p53 interactome alteration defines mechanisms of tamoxifen response in luminal breast cancer.

The canonical mechanism behind tamoxifen's therapeutic effect on estrogen receptor α/ESR1+ breast cancers is inhibition of ESR1-dependent estrogen signaling. Although ESR1+ tumors expressing wild-type p53 were reported to be more responsive to tamoxifen (Tam) therapy, p53 has not been factored into choice of this therapy and the mechanism underlying the role of p53 in Tam response remains unclear. In a window-of-opportunity trial on patients with newly diagnosed stage I-III ESR1+/HER2/wild-type p53 breast cancer who were randomized to arms with or without Tam prior to surgery, we reveal that the ESR1-p53 interaction in tumors was inhibited by Tam.

A robust Spearman correlation coefficient permutation test.

In this work, we show that Spearman's correlation coefficient test about found in most statistical software is theoretically incorrect and performs poorly when bivariate normality assumptions are not met or the sample size is small. There is common misconception that the tests about are robust to deviations from bivariate normality. However, we found under certain scenarios violation of the bivariate normality assumption has severe effects on type I error control for the common tests.

The generalized sigmoidal quantile function.

In this note we introduce a new smooth nonparametric quantile function estimator based on a newly defined generalized expectile function and termed the sigmoidal quantile function estimator. We also introduce a hybrid quantile function estimator, which combines the optimal properties of the classic kernel quantile function estimator with our new generalized sigmoidal quantile function estimator. The generalized sigmoidal quantile function can estimate quantiles beyond the range of the data, which is important for certain applications given smaller sample sizes.

A Characterization of Most(More) Powerful Test Statistics with Simple Nonparametric Applications.

Data-driven most powerful tests are statistical hypothesis decision-making tools that deliver the greatest power against a fixed null hypothesis among all corresponding data-based tests of a given size. When the underlying data distributions are known, the likelihood ratio principle can be applied to conduct most powerful tests. Reversing this notion, we consider the following questions.

Leveraging homologous hypotheses for increased efficiency in tumor growth curve testing.

In this note, we present an innovative approach called "homologous hypothesis tests" that focuses on cross-sectional comparisons of average tumor volumes at different time-points. By leveraging the correlation structure between time-points, our method enables highly efficient per time-point comparisons, providing inferences that are highly efficient as compared to those obtained from a standard two-sample t test. The key advantage of this approach lies in its user-friendliness and accessibility, as it can be easily employed by the broader scientific community through standard statistical software packages.

Leveraging Homologous Hypotheses for Increased Efficiency in Tumor Growth Curve Testing.

In this note, we present an innovative approach called "homologous hypothesis tests" that focuses on cross-sectional comparisons of average tumor volumes at different time-points. By leveraging the correlation structure between time-points, our method enables highly efficient per time-point comparisons, providing inferences that are highly efficient as compared to those obtained from a standard two-sample -test. The key advantage of this approach lies in its user-friendliness and accessibility, as it can be easily employed by the broader scientific community through standard statistical software packages.

Exact inference around ordinal measures of association is often not exact.

In this paper, we build upon the work of DiCiccio and Romano (2017) by extending their permutation test approach, based on the Pearson correlation coefficient in the continuous case, to ordinal measures of association. We investigate commonly used ordinal measures such as the Spearman correlation, Kendall's tau-b, and gamma, which are widely implemented in commercial and open-source software packages for exact testing routines based on generalized hypergeometric probabilities. Similar to DiCiccio and Romano's method, we apply studentization to correct the test statistic, which yields asymptotically valid inference for testing no ordinal association.

The Sign Test, Paired Data, and Asymmetric Dependence: A Cautionary Tale.

In the paired data setting, the sign test is often described in statistical textbooks as a test for comparing differences between the medians of two marginal distributions. There is an implicit assumption that the median of the differences is equivalent to the difference of the medians when employing the sign test in this fashion. We demonstrate however that given asymmetry in the bivariate distribution of the paired data, there are often scenarios where the median of the differences is not equal to the difference of the medians.

Phase IIa Study of SurVaxM Plus Adjuvant Temozolomide for Newly Diagnosed Glioblastoma.

Purpose: Despite intensive treatment with surgery, radiation therapy, temozolomide (TMZ) chemotherapy, and tumor-treating fields, mortality of newly diagnosed glioblastoma (nGBM) remains very high. SurVaxM is a peptide vaccine conjugate that has been shown to activate the immune system against its target molecule survivin, which is highly expressed by glioblastoma cells. We conducted a phase IIa, open-label, multicenter trial evaluating the safety, immunologic effects, and survival of patients with nGBM receiving SurVaxM plus adjuvant TMZ following surgery and chemoradiation (ClinicalTrials.

A semi-parametric bootstrap-based best linear unbiased estimator of location under symmetry.

In this note we provide a novel semi-parametric best linear unbiased estimator (BLUE) of location and its corresponding variance estimator under the assumption the random variate is generated from a symmetric location-scale family of distributions. The approach follows in a two-stage fashion and is based on the exact bootstrap estimate of the covariance matrix of the order statistic. We generalize our approach to add a robustness component in order to derive a trimmed BLUE of location under a semi-parametric symmetry assumption.

Inferential procedures based on the weighted Pearson correlation coefficient test statistic.

In this note, we evaluated the type I error control of the commonly used -test found in most statistical software packages for testing the hypothesis on vs. based on the sample weighted Pearson correlation coefficient. We found the type I error rate is severely inflated in general cases, even under bivariate normality.

First-In-Human Computer-Optimized Endobronchial Ultrasound-Guided Interstitial Photodynamic Therapy for Patients With Extrabronchial or Endobronchial Obstructing Malignancies.

Objective: Patients with inoperable extrabronchial or endobronchial tumors who are not candidates for curative radiotherapy have dire prognoses with no effective long-term treatment options. To reveal that our computer-optimized interstitial photodynamic therapy (I-PDT) is safe and potentially effective in the treatment of patients with inoperable extra or endobronchial malignancies inducing central airway obstructions.

Methods: High-spatial resolution computer simulations were used to personalize the light dose rate and dose for each tumor.

A Presurgical-Window Intervention Trial of Isothiocyanate-Rich Broccoli Sprout Extract in Patients with Breast Cancer.

Scope: Dietary isothiocyanates (ITCs) from cruciferous vegetables have shown potent anti-breast cancer activities in preclinical models, but their anticancer effects in vivo in breast cancer patients remain elusive. A proof-of-principle, presurgical window of opportunity trial is conducted to assess the anticancer effects of dietary ITCs in breast cancer patients.

Methods And Results: Thirty postmenopausal breast cancer patients are randomly assigned to receive ITC-rich broccoli sprout extract (BSE) (200 µmol ITC per day) or a placebo for 2 weeks.

Understanding Drug Sensitivity and Tackling Resistance in Cancer.

Decades of research into the molecular mechanisms of cancer and the development of novel therapeutics have yielded a number of remarkable successes. However, our ability to broadly assign effective, rationally targeted therapies in a personalized manner remains elusive for many patients, and drug resistance persists as a major problem. This is in part due to the well-documented heterogeneity of cancer, including the diversity of tumor cell lineages and cell states, the spectrum of somatic mutations, the complexity of microenvironments, and immune-suppressive features and immune repertoires, which collectively require numerous different therapeutic approaches.

Phase Ib/II Study of Cetuximab plus Pembrolizumab in Patients with Advanced RAS Wild-Type Colorectal Cancer.

Purpose: We evaluated the antitumor efficacy of cetuximab in combination with pembrolizumab in patients with wild-type (RASwt), metastatic colorectal adenocarcinoma (mCRC).

Patients And Methods: In this phase Ib/II study, cetuximab was combined with pembrolizumab in patients with RASwt mCRC with ≥ one prior line of therapy for advanced disease. We analyzed baseline on-treatment tumor tissues for changes in the tumor microenvironment (TME), using flow cytometry and multispectral immunofluorescence.

Practical and robust test for comparing binomial proportions in the randomized phase II setting.

The one-arm, non-randomized, one/two-stage phase II designs have been a mainstay in oncology trials for evaluating response rates or similar variants (i.e., tests about single proportions).

Inference for L-estimators of location using a bootstrap warping approach.

In this note we propose a new semi-parametric bootstrap procedure for hypothesis tests about a statistical function and termed bootstrap warping. This procedure was motivated by empirical likelihood and bootstrap tilting techniques. The procedure is computationally efficient and has a fixed number of parameters.

A generalized BLUE approach for combining location and scale information in a meta-analysis.

In systematic reviews and meta-analyses, one is interested in combining information from a variety of sources in order to obtain unbiased and efficient pooled estimates of the mean treatment effect compared to a control group along with the corresponding standard errors and confidence intervals, particularly when the source data is unavailable. However, in many studies the mean and standard deviation are not reported in lieu of other descriptive measures such as the median and quartiles. In this note we provide a theoretically optimal best linear unbiased estimator (BLUE) strategy for combining different types of summary information in order to pool results and estimate the overall treatment effect and the corresponding confidence intervals.

An Accelerated Life Model Analog for Discrete Survival and Count Data.

Background And Objective: Our goal is to provide an overall strategy for utilizing continuous accelerated life models in the discrete setting that provides a unique and flexible modeling approach across a variety of hazard shapes.

Methods: We convert well-known continuous accelerated life distributions into their discrete counterpart and show theoretically that the existing software that currently exists to accommodate, left, right and interval censoring in the continuous case is re-usable in the discrete setting due to the structure of the likelihood equations.

Results: We demonstrate across a variety of simulated and real-world data that our modeling approach can accommodate discrete data that may either be approximately symmetric, left-skewed or right skewed, overcoming the limitations of more traditional modeling approaches.

Sex Hormones, Insulin, and Insulin-like Growth Factors in Recurrence of High-Stage Endometrial Cancer.

Background: The influence of sex hormone and insulin/insulin-like growth factor (IGF) axis signaling on endometrial cancer recurrence is unknown. We evaluated these pathways in a prospective cohort of Gynecologic Oncology Group (GOG)0210 trial endometrial adenocarcinoma patients.

Methods: Stage II-IV patients ( = 816) were included in this study.

A robust permutation test for the concordance correlation coefficient.

In this work, we developed a robust permutation test for the concordance correlation coefficient (ρ ) for testing the general hypothesis H  : ρ  = ρ . The proposed test is based on an appropriately studentized statistic. Theoretically, the test is proven to be asymptotically valid in the general setting when two paired variables are uncorrelated but dependent.

Log-epsilon-skew normal: A generalization of the log-normal distribution.

The log-normal distribution is widely used to model non-negative data in many areas of applied research. In this paper, we introduce and study a family of distributions with non-negative reals as support and termed the log-epsilon-skew normal (LESN) which includes the log-normal distributions as a special case. It is related to the epsilon-skew normal developed in Mudholkar and Hutson (2000) the way the log-normal is related to the normal distribution.