Santu Ghosh

Non-inferiority (NI) clinical trials have gained immense popularity within the last decades, especially in cancer and cardiovascular studies. These trials are designed to establish the non-inferiority of a new experimental treatment as compared to the existing active control. In other words, NI trials are required to demonstrate that the efficacy of an experimental treatment is not unacceptably worse than that of an active control by more than a pre-specified small margin. We consider three-arm NI trials that have been widely acknowledged as the Gold Standard. Three-arm NI trials aim to simultaneously establish both NI and the assay sensitivity (AS). Hence, the analysis of three-arm NI trials involves multiple hypothesis testing. The existing literature on the Bayesian modeling of three-arm NI trials suggests implementing a test procedure based on the joint posterior probability of the NI and AS hypotheses. This joint testing of NI with AS resembles the framework of intersection-union (IU) testing, which may result in a very conservative test. In this article we propose a novel Bayesian testing based on an isotonic transformation in conjunction with Bayes factors. Bayes factors for assessing NI with AS are computed based on Gibbs Sampling. The performance of the proposed testing is evaluated through simulated data sets under varying scenarios. Empirical results show that the proposed Bayesian method gives better control in terms of Type-I error rates, and more powers than existing Bayesian tests. The usefulness of our test is illustrated by synthetic data from the Mildly Asthmatic Study.

Context: Vaccination coverage against COVID-19 varied across populations. While previous studies primarily documented disparities at a point of time, how uptake rates evolved over time across categories of socioeconomic status (SES) is less visited. Objective: This study utilizing timing (month and year) of the first dose of COVID-19 vaccination, assessed the differences in vaccination progress by different categories of SES across demographic groups in the United States (US). Design/Setting/Participants: Using data from the 2022 Behavioral Risk Factor Surveillance System for 135 730 individuals aged 18+ years, living in 29 US states, we estimated Kaplan-Meier failure functions and stratified Cox proportional hazard models for the event of first vaccination. Main Outcome Measures: We assessed how monthly survival probabilities, from December 2020 to December 2022, varied across categories of SES represented by educational attainment and household income in the full sample and in sub-samples of demographic characteristics including age groups, sex, and race and ethnicity. Results: Estimates of the K-M failure functions suggested a SES gradient of COVID-19 vaccination uptake rates, which were highest among adults with a college degree and income of ≥400% of Federal Poverty Level (FPL), and lowest among adults with educational attainment of less than high school diploma and income of <100% of FPL. Compared to college graduates, adults without a high school diploma were 0.50 (95% confidence interval: 0.48-0.52) times, and compared to adults with income of ≥400% of FPL, adults with income of <100% of FPL were 0.60 times as likely to receive the first dose of vaccination. The hazard ratios of vaccination were 0.61 and 0.70 for adults with high school diploma and some college education, and 0.69 and 0.80 for adults with income of 100% to 199% and 200% to 399% of FPL, respectively. The differences between high and low SES categories were qualitatively similar, though differed by magnitudes across the demographic groups. Conclusions: Our findings highlight a significant difference in the trends of COVID-19 vaccination uptake between individuals from high and low SES backgrounds and may offer insights for designing policies aimed at equitable vaccination coverage.

DNA methylation and copy number may be associated with each other to some extent, in positive or negative ways. Whether differential methylation and copy number variation have combined effects on gene expression is largely unknown. We use a multivariate linear model to formulate the relationship among the three genomic measurements, gene expression, copy number, and methylation levels. We propose a method that combines a distance covariance measure and the group LASSO to analyze multiple types of genomic data collectively for the purpose of insightfully revealing how gene expression is potentially affected by both copy number variation and differential methylation levels in cellular process. Our approach is of two stages, the first is a variable screening process in which a variable selection method is utilized by employing the concept of joint distance covariance (JdCov) of random vectors, and the second is to implement a penalized regression approach, a group LASSO, on the screened data of much lower dimension. The two-stage approach is tested in extensive simulation studies and shown to be effective. The approach is then applied to the TCGA Melanoma data, which consists of gene expression, methylation and copy number measurements of more than 300 patients and relationship of some genes with methylation and copy number measurements were revealed for the involved subjects.

Background: Reduction in oxygen delivery to developing kidneys of premature infants may be an important source for acute kidney injury in premature infants. Purpose: To describe changes in continuous kidney oxygenation (RrSO₂) measures before, during, and after routine diaper changes. Methods: Non–a priori analysis of a prospective cohort that received continuous measurement of RrSO₂ with near-infrared spectroscopy (NIRS) over the first 14 days of life demonstrating acute RrSO₂ drops surrounding diaper changes. Results: In total, 26 of 38 (68%) infants (≤1800 g) from our cohort exhibited acute drops in RrSO₂ that temporally correlated with diaper changes. Mean (SD) RrSO₂ baseline prior to each diaper change event was 71.1 (13.2), dropped to 59.3 (11.6) during diaper change, and recovered to 73.3 (13.2). There was a significant difference between means when comparing baseline to diaper change (P < .001; 95% CI, 9.9 to 13.8) and diaper change to recovery (P < .001; 95% CI, −16.9 to −11.2). The mean decrease in RrSO₂ during diaper change averaged 12 points (17%) below 15-minute RrSO₂ mean prior to diaper change, with quick recovery to prediaper change levels. No decreases in SpO₂, blood pressure, or heart rate were documented during the intermittent kidney hypoxic events. Implications for Practice and Research: Routine diaper changes in preterm infants may increase the risk for acute reductions in RrSO₂ as measured by NIRS; however, the impact on kidney health remains unknown. Larger prospective cohort studies assessing kidney function and outcomes related to this phenomenon are needed.

This chapter provides a brief review of the existing resampling methods for RSS and its implementation to construct a bootstrap confidence interval for the mean parameter. The authors present a brief comparison of these existing methods in terms of their flexibility and consistency. To construct the bootstrap confidence interval, three methods are adopted, namely, bootstrap percentile method, bias-corrected and accelerated method, and method based on monotone transformation along with normal approximation. Usually, for the second method, the accelerated constant is computed by employing the jackknife method. The authors discuss an analytical expression for the accelerated constant, which results in reducing the computational burden of this bias-corrected and accelerated bootstrap method. The usefulness of the proposed methods is further illustrated by analyzing real-life data on shrubs.

This study assesses differences between users and non-users of unscheduled healthcare for persistent childhood asthma, with regard to select demographic and risk factors. The objectives are to provide important healthcare utilization information and a foundation for future research on self-management effectiveness (SME), informed by a recently developed “holistic framework” for measuring SME in childhood asthma. An 18-month retrospective chart review was conducted on 59 pediatric outpatients with persistent asthma—mild, moderate, or severe, to obtain data on various demographic and risk factors, and healthcare use for each child. The study examined five types of “unscheduled” healthcare use. Users had non-zero encounters (at least one) in any of the five types; non-users had zero encounters (not even one) in all five types. Differences between users and non-users were assessed using contingency table and logistic regression analysis. There were 25 users and 34 non-users of unscheduled healthcare. Each severity category contained users and non-users. The only statistically significant finding was that the mild persistent category had fewer users than severe persistent (p < 0.05). There were no significant differences between users and non-users for any other demographic or risk factor examined. After adjusting for asthma severity, there were no other significant differences between users and non-users of unscheduled healthcare. This is a crucial finding which suggests that something else is driving unscheduled healthcare use in these children, given there were users and non-users in each asthma severity category. These results provide impetus for future research on the role of other aspects of the "holistic framework" in explaining differences in uses of unscheduled healthcare in persistent childhood asthma.

Testing equality of mean vectors is a very commonly used criterion when comparing two multivariate random variables. Traditional tests such as Hotelling's T-squared become either unusable or output small power when the number of variables is greater than the combined sample size. In this paper, w}e propose a test using both prepivoting and Edgeworth expansion for testing the equality of two population mean vectors in the "large p, small n" setting. The asymptotic null distribution of the test statistic is derived and it is shown that the power of suggested test converges to one under certain alternatives when both n and p increase to infinity against sparse alternatives. Finite sample performance of the proposed test statistic is compared with other recently developed tests designed to also handle the "large p, small n" situation through simulations. The proposed test achieves competitive rates for both type I error rate and power. The usefulness of our test is illustrated by applications to two microarray gene expression data sets