Narinder Nangia

Bio: Narinder Nangia is an academic researcher. The author has contributed to research in topics: Median income & Small area estimation. The author has an hindex of 2, co-authored 2 publications receiving 115 citations.

Estimation of Median Income of Four-Person Families: A Bayesian Time Series Approach

Abstract: This article develops a general methodology for small domain estimation based on data from repeated surveys. The results are directly applied to the estimation of median income of four-person families for the 50 states and the District of Columbia. These estimates are needed by the U.S. Department of Health and Human Services (HHS) to formulate its energy assistance program for low income families. The U.S. Bureau of the Census, by an informal agreement, has provided such estimates to HHS through a linear regression methodology since the latter part of the 1970s. The current method is an empirical Bayes method (EB) that uses the Current Population Survey (CPS) estimates as well as the most recent decennial census estimates updated by the per capita income estimates of the Bureau of Economic Analysis. However, with the existing methodology, standard errors associated with these estimates are not easy to obtain. The EB estimates, when used naively, can lead to underestimation of standard errors. Mo...

Narsoplimab, a Mannan-Binding Lectin-Associated Serine Protease-2 Inhibitor, for the Treatment of Adult Hematopoietic Stem-Cell Transplantation–Associated Thrombotic Microangiopathy

Abstract: PURPOSE Hematopoietic stem-cell transplantation–associated thrombotic microangiopathy (HSCT-TMA) is a serious complication with significant mortality and no approved therapy. HSCT-TMA results from endothelial injury, which activates the lectin pathway of complement. Narsoplimab (OMS721), an inhibitor of mannan-binding lectin-associated serine protease-2 (MASP-2), was evaluated for safety and efficacy in adults with HSCT-TMA. METHODS In this single-arm open-label pivotal trial (NCT02222545), patients received intravenous narsoplimab once weekly for 4-8 weeks. The primary end point (response rate) required clinical improvement in two categories: (1) laboratory TMA markers (both platelet count and lactate dehydrogenase) and (2) organ function or freedom from transfusion. Patients receiving at least one dose (full analysis set [FAS]; N = 28) were analyzed. RESULTS The response rate was 61% in the FAS population. Similar responses were observed across all patient subgroups defined by baseline features, HSCT characteristics, and HSCT complications. Improvement in organ function occurred in 74% of patients in the FAS population. One-hundred-day survival after HSCT-TMA diagnosis was 68% and 94% in FAS population and responders, respectively, whereas median overall survival was 274 days in the FAS population. Narsoplimab was well tolerated, and adverse events were typical of this population, with no apparent safety signal of concern. CONCLUSION In this study, narsoplimab treatment was safe, significantly improved laboratory TMA markers, and resulted in clinical response and favorable overall survival.

Small Area Estimation‐New Developments and Directions

Abstract: Summary The purpose of this paper is to provide a critical review of the main advances in small area estimation (SAE) methods in recent years. We also discuss some of the earlier developments, which serve as a necessary background for the new studies. The review focuses on model dependent methods with special emphasis on point prediction of the target area quantities, and mean square error assessments. The new models considered are models used for discrete measurements, time series models and models that arise under informative sampling. The possible gains from modeling the correlations among small area random effects used to represent the unexplained variation of the small area target quantities are examined. For review and appraisal of the earlier methods used for SAE, see Ghosh and Rao (1994).

Some recent advances in model-based small area estimation

Abstract: Small area estimation has received a lot of attention in recent years due to growing demand for reliable small area estimators. Traditional area-specific direct estimators do not provide adequate precision because sample sizes in small areas are seldom large enough. This makes it necessary to employ indirect estimators that borrow strength from related areas; in particular, model-based indirect estimators. Ghosh and Rao (1994) provided a comprehensive review and appraisal of methods for small area estimation, covering the literature to 1992-1993. This paper supplements Ghosh and Rao (1994) by covering the literature over the past five years or so on model-based estimation. In particular, we cover several small area models and empirical best linear unbiased prediction (EBLUP), empirical Bayes (EB) and hierarchical Bayes (HB) methods applied to these models. We also present several recent applications of small area estimation. 1. J.N.K. Rao, School of Mathematics and Statistics, Carleton University, Ottawa, Ontario, K1S 5B6.

Combining Information From Two Surveys to Estimate County-Level Prevalence Rates of Cancer Risk Factors and Screening

Abstract: Cancer surveillance research requires estimates of the prevalence of cancer risk factors and screening for small areas such as counties. Two popular data sources are the Behavioral Risk Factor Surveillance System (BRFSS), a telephone survey conducted by state agencies, and the National Health Interview Survey (NHIS), an area probability sample survey conducted through face-to-face interviews. Both data sources have advantages and disadvantages. The BRFSS is a larger survey and almost every county is included in the survey, but it has lower response rates as is typical with telephone surveys and it does not include subjects who live in households with no telephones. On the other hand, the NHIS is a smaller survey, with the majority of counties not included; but it includes both telephone and nontelephone households, and has higher response rates. A preliminary analysis shows that the distributions of cancer screening and risk factors are different for telephone and nontelephone households. Thus, informatio...