About

Arnab Maity is a Professor in the Department of Statistics at NC State University, located in SAS Hall. He holds a Ph.D. in Statistics from Texas A&M University, obtained in 2008. His areas of expertise include Semiparametric Methods, Measurement Error, and Longitudinal Data. Maity's research focuses on developing and applying statistical methods within these domains, contributing to the advancement of statistical inference and data analysis techniques. He is actively involved in the academic community through his teaching and research activities at NC State University, engaging with students and colleagues in the field of statistics.

Research topics

• Computer Science
• Statistics
• Econometrics
• Mathematics
• Artificial Intelligence
• Medicine

Selected publications

• Improving paleoclimate predictions from paleosol geochemistry

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• WCN25-1133 CLINICAL PROFILE AND OUTCOME OF PREGNANCY RELATED ACUTE KIDNEY INJURY (PRAKI) IN EASTERN PART OF INDIA

  Kidney International Reports · 2025-01-27

  articleOpen accessSenior author
  PublisherDOI

• Cumulative Logit Ordinal Regression with Proportional Odds under Nonignorable Missing Response -- Application to Phase III Trial

  ArXiv.org · 2025-05-07

  preprintOpen access1st authorCorresponding

  Missing data are inevitable in clinical trials, and trials that produce categorical ordinal responses are not exempted from this. Typically, missing values in the data occur due to different missing mechanisms, such as missing completely at random, missing at random, and missing not at random. Under a specific missing data regime, when the conditional distribution of the missing data is dependent on the ordinal response variable itself along with other predictor variables, then the missing data mechanism is called nonignorable. In this article we propose an expectation maximization based algorithm for fitting a proportional odds regression model when the missing responses are nonignorable. We report results from an extensive simulation study to illustrate the methodology and its finite sample properties. We also apply the proposed method to a recently completed Phase III psoriasis study using an investigational compound. The corresponding SAS program is provided.

  PublisherOA PDFDOI

• Approaches to Silica Production from Agriculture Waste Biomass

  Intelligent systems reference library · 2025-01-01 · 1 citations

  book-chapterSenior author
  PublisherDOI

• Cumulative Logit Ordinal Regression With Proportional Odds Under Nonignorable Missing Responses—Application to Phase III Trial

  Statistics in Medicine · 2025-08-01

  article1st authorCorresponding

  Missing data are inevitable in clinical trials, and trials that produce categorical ordinal responses are not exempted from this. Typically, missing values in the data occur due to different missing mechanisms, such as missing completely at random, missing at random, and missing not at random. Under a specific missing data regime, when the conditional distribution of the missing data is dependent on the ordinal response variable itself along with other predictor variables, then the missing data mechanism is called nonignorable. In this article, we propose an expectation maximization based algorithm for fitting a proportional odds regression model when the missing responses are nonignorable. We report the results from an extensive simulation study to illustrate the methodology and its finite sample properties. We also apply the proposed method to a recently completed Phase III psoriasis study using an investigational compound. The corresponding SAS program is provided.

  PublisherDOI

• On the substructure controls in rare variant analysis: Principal components or variance components?

  UNC Libraries · 2025-10-17

  articleOpen access

  Recent studies showed that population substructure (PS) can have more complex impact on rare variant tests and that similarity-based collapsing tests (e.g., SKAT) may suffer more severely by PS than burden-based tests. In this work, we evaluate the performance of SKAT coupling with principal components (PC) or variance components (VC) based PS correction methods. We consider confounding effects caused by PS including stratified populations, admixed populations, and spatially distributed nongenetic risk; we investigate which types of variants (e.g., common, less frequent, rare, or all variants) should be used to effectively control for confounding effects. We found that (i) PC-based methods can account for confounding effects in most scenarios except for admixture, although the number of sufficient PCs depends on the PS complexity and the type of variants used. (ii) PCs based on all variants (i.e., common + less frequent + rare) tend to require equal or fewer sufficient PCs and often achieve higher power than PCs based on other variant types. (iii) VC-based methods can effectively adjust for confounding in all scenarios (even for admixture), though the type of variants should be used to construct VC may vary. (iv) VC based on all variants works consistently in all scenarios, though its power may be sometimes lower than VC based on other variant types. Given that the best-performed method and which variants to use depend on the underlying unknown confounding mechanisms, a robust strategy is to perform SKAT analyses using VC-based methods based on all variants.

  PublisherDOI

• Maternal Periconceptional Adherence to a Mediterranean Diet Pattern and Child Body Mass Index Between Ages 3–10 Years by Sex, Age, and Race

  Current Developments in Nutrition · 2024-06-29

  articleOpen access

  Objectives: To assess the extent to which the associations between maternal periconceptional Mediterranean diet (MD) pattern adherence and child BMI between ages 3-10 years vary by age, sex, and race/ethnicity in a diverse cohort of mother-child dyads. Methods: We conducted secondary data analysis of n=537 mother-child dyads from the Newborn Epigenetics Study (NEST), a multi-ethnic longitudinal birth cohort from North Carolina. Maternal diet information was collected through a food frequency questionnaire reflecting the periconceptional period and assessed with the Mediterranean Diet Score. Height and weight were measured annually between ages 3-10 years and body mass index (BMI) was calculated. Multiple linear mixed effects regression models with exponential correlation structure and multiple logistic regression models were used to assess the associations between maternal MD adherence and 1) BMI between 3-10 years and 2) the likelihood of BMI < 85th percentile between 3-10 years. Models were adjusted for sex at birth, birth weight, breastfeeding, race/ethnicity, pre-pregnancy BMI, maternal smoking during pregnancy. Given statistically significant interaction terms, stratified models by race/ethnicity, age, and sex were explored. Results: Among mother-child dyads, 44% identified as Black/African American, 38% as White, and 18% as Hispanic/Latinx. A greater maternal periconceptional adherence to a MD was associated with a lower BMI between ages 3-10 years among non-Hispanic Black children (p=0.03), but not among those identifying as Hispanic or White. There was also a consistent association between maternal MD adherence and greater likelihood of BMI < 85th percentile at age 3 (p=0.005), 4 (p< 0.001), 5 (p=0.02), and 6 years (p< 0.001) among boys of all races/ethnicities. Associations were not statistically significant after age 6 years. Conclusions: Greater maternal periconceptional adherence to a Mediterranean dietary pattern is associated with a lower likelihood of child overweight/obesity between ages 3-6 years among boys, and a lower BMI across ages 3-10 years among non-Hispanic Black children. The Mediterranean diet shows promise as a potential periconceptional intervention for reducing the likelihood of future overweight in children. The role of sex, age, and race warrant further exploration. Funding Sources: NIH: NIEHS, NIMHD.

  PublisherDOI

• List of contributors

  Microplastics · 2024-10-18

  book-chapterOpen access
  PublisherDOI

• Robust kernel association testing (RobKAT)

  UNC Libraries · 2024-06-07

  articleOpen access

  Testing the association between single-nucleotide polymorphism (SNP) effects and a response is often carried out through kernel machine methods based on least squares, such as the sequence kernel association test (SKAT). However, these least-squares procedures are designed for a normally distributed conditional response, which may not apply. Other robust procedures such as the quantile regression kernel machine (QRKM) restrict the choice of the loss function and only allow inference on conditional quantiles. We propose a general and robust kernel association test with a flexible choice of the loss function, no distributional assumptions, and has SKAT and QRKM as special cases. We evaluate our proposed robust association test (RobKAT) across various data distributions through a simulation study. When errors are normally distributed, RobKAT controls type I error and shows comparable power with SKAT. In all other distributional settings investigated, our robust test has similar or greater power than SKAT. Finally, we apply our robust testing method to data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) clinical trial to detect associations between selected genes including the major histocompatibility complex (MHC) region on chromosome six and neurotropic herpesvirus antibody levels in schizophrenia patients. RobKAT detected significant association with four SNP sets (HST1H2BJ, MHC, POM12L2, and SLC17A1), three of which were undetected by SKAT.

  PublisherDOI

• Fragility Index for Time-to-Event Endpoints in Single-Arm Clinical Trials

  arXiv (Cornell University) · 2024-11-25

  preprintOpen access1st authorCorresponding

  The reliability of clinical trial outcomes is crucial, especially in guiding medical decisions. In this paper, we introduce the Fragility Index (FI) for time-to-event endpoints in single-arm clinical trials - a novel metric designed to quantify the robustness of study conclusions. The FI represents the smallest number of censored observations that, when reclassified as uncensored events, causes the posterior probability of the median survival time exceeding a specified threshold to fall below a predefined confidence level. While drug effectiveness is typically assessed by determining whether the posterior probability exceeds a specified confidence level, the FI offers a complementary measure, indicating how robust these conclusions are to potential shifts in the data. Using a Bayesian approach, we develop a practical framework for computing the FI based on the exponential survival model. To facilitate the application of our method, we developed an R package fi, which provides a tool to compute the Fragility Index. Through real world case studies involving time to event data from single arms clinical trials, we demonstrate the utility of this index. Our findings highlight how the FI can be a valuable tool for assessing the robustness of survival analyses in single-arm studies, aiding researchers and clinicians in making more informed decisions.

  PublisherOA PDFDOI

Recent grants

• NIH Grant R00ES017744

  NIH · $663k · 2014

• NIH Grant K99ES017744

  NIH · $76k · 2010

Frequent coauthors

• Raymond J. Carroll

  University of Technology Sydney

  30 shared

• Jung‐Ying Tzeng

  North Carolina State University

  21 shared

• Ana‐Maria Staicu

  North Carolina State University

  21 shared

• Rahul Ghosal

  11 shared

• Stacey A. Missmer

  Brigham and Women's Hospital

  11 shared

• Shruthi Mahalingaiah

  Harvard University

  10 shared

• Xihong Lin

  Harvard University

  10 shared

• Katharine Berry

  Morehouse School of Medicine

  9 shared

Education

• PhD, Statistics

  Texas A&M University

  2008

• MS, Statistics

  Texas A&M University

  2005

• BS, Statistics

  Indian Statistical Institute

  2003